From ad278f9aa5d72c3636a2dbd9bb3f65427af9b93b Mon Sep 17 00:00:00 2001 From: Ashish Acharya Date: Fri, 9 Feb 2024 17:28:09 -0600 Subject: [PATCH] Add updated fixtures --- environmental_justice/fixtures/ej_row.json | 4227 +------------------- 1 file changed, 1 insertion(+), 4226 deletions(-) diff --git a/environmental_justice/fixtures/ej_row.json b/environmental_justice/fixtures/ej_row.json index a8b0790b..ee244cd7 100644 --- a/environmental_justice/fixtures/ej_row.json +++ b/environmental_justice/fixtures/ej_row.json @@ -1,4226 +1 @@ -[ - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 1, - "fields": { - "dataset": "NASA's Black Marble night lights data", - "description": "NASA's Black Marble night lights data product from the NASA/NOAA Suomi National Polar-orbiting Partnership satellite with USGS-NASA Landsat data and Google's OpenStreetMap were combined to develop a neighborhood-scale map of energy use in communities across Puerto Rico and New Orleans as the electricity grid was slowly restored after Hurrican Maria and Hurricane Ida. They then analyzed the relationship between restoration rates in terms of days without electricity and the remoteness of communities from major cities.", - "description_simplified": "NASA's Black Marble night lights data provide a neighborhood-scale map of energy use in communities in Puerto Rico and New Orleans as the electricity grid was slowly restored after Hurricane Maria and Hurricane Ida. These data can then be used to study the relationship between restoration rates and the remoteness or other aspects of communities across these areas. These data are a product of the NASA/NOAA (National Oceanic and Atmospheric Administration) Suomi National Polar-orbiting Partnership satellite, using USGS (United States Geologic Survey)-NASA Landsat data, as well as Google's OpenStreetMap data.", - "indicators": "Disasters", - "intended_use": "Path A", - "latency": "", - "limitations": "Not for data download. Data download is listed separately.", - "project": "Disaster Dashboard", - "source_link": "https://svs.gsfc.nasa.gov/4658#:~:text=NASA%27s%20Black%20Marble%20night%20lights%20used%20to%20examine%20disaster%20recovery%20in%20Puerto%20Rico,-Visualizations%20by%20Kel&text=At%20night%2C%20Earth%20is%20lit,Puerto%20Rico%27s%20lights%20went%20out.", - "strengths": "Specific examples and visualizations given for users", - "format": "tif, jpeg, mpeg", - "geographic_coverage": "Puerto Rico, New Orleans", - "data_visualization": "Story", - "spatial_resolution": "", - "temporal_extent": 2017, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 2, - "fields": { - "dataset": "NASA's Precipitation Processing System", - "description": "The Precipitation Processing System (PPS) evolved from the Tropical Rainfall Measuring Mission (TRMM) Science Data and Information System (TSDIS). The purpose of the PPS is to process, analyze and archive data from the Global Precipitation Measurement (GPM) mission, partner satellites and the TRMM mission. The PPS also supports TRMM by providing validation products from TRMM ground radar sites. All GPM, TRMM and Partner public data products are available to the science community and the general public from the TRMM/GPM FTPS and HTTPS Data Archives", - "description_simplified": "The Precipitation Processing System (PPS) processes, combines, analyzes, and archives data from other past and current precipitation datasets. Data can be used to track urban flooding, overall precipitation and drought trends, and significant storm events.", - "indicators": "Urban Flooding", - "intended_use": "Path B", - "latency": "varies-multiple datasets available", - "limitations": "Data format is unclear.", - "project": "Disaster Dashboard", - "source_link": "https://arthurhou.pps.eosdis.nasa.gov/", - "strengths": "Lots of data available within link for many different uses.", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Several Available", - "spatial_resolution": "varies", - "temporal_extent": "varies", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 3, - "fields": { - "dataset": "NASADEM (NASA Digital Elevation Model) Topography Data", - "description": "A method for delineating topography is NASA's Shuttle Radar Topography Mission (SRTM). SRTM provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. NASADEM, available at 1 arc-second resolution, extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products", - "description_simplified": "NASA Digital Elevation Model (NASADEM) Topography Data provide a digital elevation model of all land between 60 degrees north and 56 degrees south, nearly 80% of Earth's landmass. Topography data can be useful in urban flooding and hurricane research.", - "indicators": "Urban Flooding", - "intended_use": "Path C", - "latency": "", - "limitations": "Lacks recent data", - "project": "Disaster Dashboard", - "source_link": "https://lpdaac.usgs.gov/news/release-nasadem-data-products/", - "strengths": "High resolution and large geographic coverage.", - "format": "HGT or NetCDF4", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies-multiple datasets available", - "temporal_extent": "varies-multiple datasets available", - "temporal_resolution": "varies-multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 4, - "fields": { - "dataset": "Giovanni Surface Runoff", - "description": "Runoff after storm events can impact the amount of water entering a channel or water body. Satellites cannot measure runoff directly but information that can be used to assess predicted runoff can be measured using remote sensing. These data are input, along with ground-based data, into atmosphere-land models from LDAS to estimate runoff. Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni.", - "description_simplified": "Storm water runnoff can impact urban flooding. Satellites cannot measure runoff directly but information that can be used to assess predicted runoff can be measured using remote sensing. These data products can be visualized many different ways and aid in natural disaster research.", - "indicators": "Urban Flooding", - "intended_use": "Path C", - "latency": "", - "limitations": "Giovanni requires experience.", - "project": "Disaster Dashboard", - "source_link": "https://disc.gsfc.nasa.gov/datasets/NLDAS_NOAH0125_H_2.0/summary", - "strengths": "Includes very recent data, up to a few weeks before current date. Visualizations available in Giovanni.", - "format": "netCDF", - "geographic_coverage": "North America", - "data_visualization": "Giovanni", - "spatial_resolution": "0.125 \u00b0 x 0.125 \u00b0", - "temporal_extent": "Jan 1979-present", - "temporal_resolution": "1 hour" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 5, - "fields": { - "dataset": "NASA Flood Dashboard", - "description": "The Flood Dashboard brings together multiple NASA soil moisture and flood products with products from the National Weather Service and USGS to give a more complete picture of potential flooding in the United States. Includes data from Soil Moisture Map, USGS Stream Gauges, and MODIS Flood Maps.", - "description_simplified": "The Flood Dashboard brings together mutliple NASA soil moisture and flood products from the National Weather Service and USGS to give a more complete picture of potential flooding in the United States. Includes data from Soil Moisture Map, USGS Stream Gauges, and MODIS Flood Maps. These data can aid in urban flooding and hurricane research.", - "indicators": "Urban Flooding", - "intended_use": "Path A", - "latency": "", - "limitations": "Data format and download options are unclear.", - "project": "Disaster Dashboard", - "source_link": "https://maps.disasters.nasa.gov/arcgis/apps/opsdashboard/index.html#/a70a27ff74f94fa9a23123b58b3ee613", - "strengths": "Interactive dashboard with visualizations of data from various sources.", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Dashboard", - "spatial_resolution": "", - "temporal_extent": "varies-multiple datasets available", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 6, - "fields": { - "dataset": "SAR (Synthetic Aperture Radar) Hurricane Monitoring for the Gulf and East Coast", - "description": "Sentinel-1 Water Maps, RGB and RTC imagery over the Gulf and East Coast for the 2021 hurricane season. Updated approximately 4pm EDT, overpasses occur every ~8 days depending on AOI. (Copernicus Sentinel-1, Alaska Satellite Facility)", - "description_simplified": "Visualization of Sentinel-1 Water Maps and other imagery over the Gulf and East Coast for the 2021 hurricane season. Blue shows water extent. Updated at approximately 4pm EDT about every 8 days to show change in water levels pre and post-hurricane.", - "indicators": "Disasters", - "intended_use": "Path B", - "latency": "", - "limitations": "Data format and download options are unclear.", - "project": "Disaster Dashboard", - "source_link": "https://nasa.maps.arcgis.com/home/webmap/viewer.html?webmap=f33be724f04b4b4c942edd0c9bd18f48", - "strengths": "Visualization of data to show pre and post storm.", - "format": "", - "geographic_coverage": "Gulf and East Coast", - "data_visualization": "ArcGIS viewer", - "spatial_resolution": "30 meters", - "temporal_extent": 2021, - "temporal_resolution": "8 days" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 7, - "fields": { - "dataset": "Event Response to Floods at ARIA (Advanced Rapid Imaging and Analysis)", - "description": "The ARIA Project, a joint effort of the California Institute of Technology and NASA's Jet Propulsion Laboratory, is developing the infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response.", - "description_simplified": "The ARIA Project, a joint effort of the California Institute of Technology and NASA's Jet Propulsion Laboratory, is developing the infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response. These data can aid in natural disaster research.", - "indicators": "Disasters", - "intended_use": "Path C", - "latency": "NRT", - "limitations": "No visualization available.", - "project": "Disaster Dashboard", - "source_link": "https://aria-share.jpl.nasa.gov/", - "strengths": "Near real time data to aid natural disaster response and preparation.", - "format": "varies- kmz, png, geotiff", - "geographic_coverage": "Puerto Rico, New Orleans", - "data_visualization": "", - "spatial_resolution": "varies", - "temporal_extent": "varies", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 8, - "fields": { - "dataset": "Global Cyclone Proportional Economic Loss Risk Deciles", - "description": "The Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 minute grid of cyclone hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Cyclone Proportional Economic Loss Risk Deciles shows cyclone hazard economic loss as a proportion of Gross Domestic Product (GDP) of an area. The shown rates are calculated based on how often and to what extend the hazard of cyclones poses to area. These data can aid in tropical storm preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H44F1NNF", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-cyclone-proportional-economic-loss-risk-deciles/maps", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 9, - "fields": { - "dataset": "Global Cyclone Total Economic Loss Risk Deciles", - "description": "The Global Cyclone Total Economic Loss Risk Deciles is a 2.5 minute grid of global cyclone total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national GDP is spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using population distribution data. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell as determined from Gridded Population of the World, Version 3 (GPWv3) data. Once a GDP value is determined on a per grid cell basis, then the regionally variable loss rate, as derived from the historical records of EM-DAT, is used to determine the total economic loss risks posed to a grid cell by cyclone hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Cyclone Proportional Economic Loss Risk Deciles shows the total global cyclone economic loss using the Gross Domestic Product (GDP) of an area. The economic loss is assigned a decile or rank (1-10) based on how often and to what extend the hazard of cyclones poses to area. These data can aid in tropical storm preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H40P0WXQ", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, DBF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-cyclone-total-economic-loss-risk-deciles/maps", - "spatial_resolution": "0.041700 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 10, - "fields": { - "dataset": "Global Flood Proportional Economic Loss Risk Deciles", - "description": "The Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Flood Proportional Economic Loss Risk Deciles shows flood hazard economic loss as a proportion of Gross Domestic Product (GDP) of an area. The shown rates are calculated based on how often and to what extend the hazard of floods poses to area. These data can aid in storm preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4XS5S9Q", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-flood-proportional-economic-loss-risk-deciles/maps", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 11, - "fields": { - "dataset": "Global Flood Total Economic Loss Risk Deciles", - "description": "The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Flood Proportional Economic Loss Risk Deciles shows the total global flood economic loss using the Gross Domestic Product (GDP) of an area. The economic loss is assigned a decile or rank (1-10) based on how often and to what extend the hazard of floods poses to area. These data can aid in storm preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4T151KP", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, DBF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-flood-total-economic-loss-risk-deciles/maps", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 12, - "fields": { - "dataset": "Global Multihazard Proportional Economic Loss Risk Deciles", - "description": "The Global Multihazard Proportional Economic Loss Risks is a 2.5 minute grid of a multihazard-based economic loss risk as a proportion of the economic productivity of the analytical unit, the grid cell. Representation of multihazard risk is not based on a multihazard index but rather on combinations of hazard risk categories, drought, seismic, and hydro. The drought category includes drought only. The seismic category consists of earthquake and volcano hazards. Cyclones, floods, and landslides are included in the hydro category. For each of the six hazards considered, a binary risk surface is constructed utilizing the three most-at-risk deciles of each hazard's global proportional economic loss risks data set (deciles 8-10). Each of the category risk surfaces are constructed by adding all the relevant hazard high-risk surfaces. These categorical risk surfaces are reclassified into binary high-risk surfaces. The combination of the category risk values forms a three digit identifier for determining those locations that are at higher-risk from multihazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Multihazard Proportional Economic Loss Risks shows the multihazard-based economic loss risk as a proportion of economic productivity. In other words, several different hazards are combined and a proportion is created with the economic productivity of an area. These hazards include drought, earthquakes, volcanoes, cyclones, landslides, and flood risk.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "Disaster Dashboard", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C179001790-SEDAC.html", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "Archive: ASCII, DBF, PDF, PNG; Distribution: ASCII, DBF, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 13, - "fields": { - "dataset": "Global Multihazard Total Economic Loss Risk Deciles", - "description": "The Global Multihazard Total Economic Loss Risk Deciles is a 2.5 minute grid of global multihazard total economic loss risks. First, for each of the considered hazards (cyclones, droughts, earthquakes, floods, landslides, and volcanoes), subnational distributions of Gross Domestic Product (GDP) are computed using a methodology developed from Sachs et al. (2003). Where applicable, the contributions of subnational units to national GDP estimates, the contribution ratio, are determined using data of varied origin. World Bank Development Indicators are substituted for GDP estimates of varied origin and the subnational GDP is estimated using the fore mentioned contribution ratios. A subnational, per capita GDP is derived and a final GDP estimate per grid cell is made based on grid cell population density. A raw, total economic loss is computed per grid cell using a regional economic loss rate derived from EM-DAT records. To more accurately reflect the confidence surrounding the economic loss estimate, the range of losses are classified into deciles, 10 classes of an approximately equal number of grid cells. A multihazard index is generated by summing the top three deciles of the individual hazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Multihazard Proportional Economic Loss Risks shows the total economic loss risk by combining the risk of several types of hazards. These hazards include drought, earthquakes, volcanoes, cyclones, landslides, and flood risk. This dataset may aid disaster preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4S180F9", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, DBF, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-multihazard-total-economic-loss-risk-deciles/maps", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 14, - "fields": { - "dataset": "Geocoded Disasters (GDIS) Dataset", - "description": "The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).", - "description_simplified": "The Geocoded Disasters (GDIS) Dataset includes a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters (CRED) Emergency Events Database (EM_DAT). The data included come from 39,953 locations for 9,924 disasters that happened across the world from 1960-2018. Population data are also included. This dataset may aid disaster preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "PEND - Natural Disasters", - "source_link": "https://doi.org/10.7927/zz3b-8y61", - "strengths": "Significant temporal extent", - "format": "Geopackage, R-Workspace, Geodatabase, CSV, R-script Source Code", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/pend-gdis-1960-2018/maps", - "spatial_resolution": "", - "temporal_extent": "1960-01-01 to 2018-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 15, - "fields": { - "dataset": "Global Flood Hazard Frequency and Distribution", - "description": "The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Flood Hazard Frequency and Distribution compiles data from extreme flood events between 1985 and 2003. Each grid is assigned a value based on the frequency of floods in that area. The higher the number, the more frequent flood events are in that area.", - "indicators": "Urban Flooding", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4668B3D", - "strengths": "18 years temporal extent", - "format": "ASCII, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution/maps", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "1985-01-01 to 2003-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 16, - "fields": { - "dataset": "ARIA (Advanced Rapid Imaging and Analysis) DPM (Damage Proxy Map) Puerto Rico", - "description": "The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory in Pasadena, California, and Caltech, also in Pasadena, created this Damage Proxy Map (DPM) depicting areas of Eastern Puerto Rico that are likely damaged (shown by red and yellow pixels) as a result of Hurricane Maria (Category 4 at landfall in Puerto Rico on Sept. 20, 2017). The map is derived from synthetic aperture radar (SAR) images from the Copernicus Sentinel-1A and Sentinel-1B satellites, operated by the European Space Agency (ESA). The images were taken before (Mar. 25, 2017) and after (Sept. 21, 2017) the landfall of the storm.", - "description_simplified": "The ARIA (Advanced Rapid Imaging and Analysis) DPM (Damage Proxy Map) Puerto Rico dataset shows areas of Eastern Puerto Rico that are likely to be damaged (shown by red and yellow pixels) as a result of Hurricane Maria.", - "indicators": "Disasters", - "intended_use": "Path B", - "latency": "", - "limitations": "Data only available for case study of Puerto Rico (2017)", - "project": "Disaster Dashboard", - "source_link": "https://appliedsciences.nasa.gov/our-impact/news/aria-damage-proxy-map-puerto-rico-after-hurricane-maria https://ghis.maps.arcgis.com/home/item.html?id=1ce3ccaacc6c4cd7b3b6cef4ea4980aa", - "strengths": "Visualization available through ArcGIS viewer", - "format": "kml, raster data ", - "geographic_coverage": "Puerto Rico", - "data_visualization": "ArcGIS viewer", - "spatial_resolution": "30 m", - "temporal_extent": 2017, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 17, - "fields": { - "dataset": "Maps of Subsidence in New Orleans", - "description": "Through a combination of airborne radar and ground-based GPS, a research team has developed detailed models of how much land is sinking and rising in southern Louisiana.", - "description_simplified": "Through a combination of airborne radar and ground-based GPS, a research team has developed detailed models of how much land is sinking and rising in southern Louisiana.", - "indicators": "Urban Flooding", - "intended_use": "Path A", - "latency": "", - "limitations": "Data download unavailable", - "project": "Disaster Dashboard", - "source_link": "https://visibleearth.nasa.gov/images/88078/scientists-improve-maps-of-subsidence-in-new-orleans?size=large", - "strengths": "Images for easy visualization", - "format": "PNG", - "geographic_coverage": "New Orleans", - "data_visualization": "images", - "spatial_resolution": "", - "temporal_extent": "varies", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 18, - "fields": { - "dataset": "US Social Vulnerability Index", - "description": "The U.S. Social Vulnerability Index Grids data set contains gridded layers for the overall Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) using four sub-category themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) based on census tract level inputs from 15 variables for the years 2000, 2010, 2014, 2016, and 2018. SVI values range between 0 and 1 based on their percentile position among all census tracts in the U.S., with 0 representing lowest vulnerability census tracts and 1 representing highest vulnerability census tracts. SEDAC has gridded these vector inputs to create 1 km spatial resolution raster surfaces allowing users to obtain vulnerability metrics for any user-defined area within the U.S. Utilizing inputs from CIESIN's Gridded Population of the World, Version 4, Revision 11 (GPWv4.11), a mask is applied for water, and optionally, for no population. The data are provided in two different projection formats, NAD83 as a U.S. specific standard, and WGS84 as a global standard. The goal of the SVI is to help identify vulnerable communities by ranking them on these inputs across the U.S.", - "description_simplified": "The U.S. Social Vulnerability Index Grids identifies socially vulnerable populations at higher risk due to four main factors: socioeconomic status, household composition & disability, minority status & language, and housing type & transportation. These data are produced by the CDC (Centers for Disease Control and Prevention based on census data from the years 200, 2010, 2014, 2016, and 2018. ", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "USCG - U.S. Census Grids", - "source_link": "https://doi.org/10.7927/6s2a-9r49", - "strengths": "Temporal extent varies", - "format": "GeoTIFF, PDF, PNG", - "geographic_coverage": "United States", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/usgrid-us-social-vulnerability-index/maps", - "spatial_resolution": "0.0083 Decimal Degrees", - "temporal_extent": "2000-01-01, 2010-01-01, 2014-01-01, 2016-01-01, 2018-01-01", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 19, - "fields": { - "dataset": "VIIRS (Visual Infrared Imaging Radiometer Suite) Plus DMSP (Defense Meteorological Satellite System) Change in Lights", - "description": "To visualize changes in brightness and extent of global nighttime lights networks over two decades with improved radiometric accuracy and finer spatial resolution.", - "description_simplified": "The VIIRS (Visual Infrared Imaging Radiometer Suite) Plus DMSP (Defense Meteorological Satellite System) Change in Lights dataset allows users to visualize changes in brightness and the overall extent of global nighttime lights over a period of two decades. These data are useful in studying urbanization or disaster recovery.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Temporal extent only includes 3 years", - "project": "Other", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/sdei-viirs-dmsp-dlight", - "strengths": "Visualization available through SEDAC map widget", - "format": "GeoTIFF", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "", - "temporal_extent": "1992, 2002, 2013", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 20, - "fields": { - "dataset": "Urban-Rural Population and Land Area Estimates, v3 (1990, 2000, 2015)", - "description": "For tracking urban areas at risk of coastal hazards. To provide estimates of urban and rural populations and land areas for the years 1990, 2000, 2015 for 234 countries and statistical areas with contiguous coastal elevations of less than or equal to 5m above sea level, 5-10m above sea level, and national totals using multiple updated data sources for comparative analysis.", - "description_simplified": "The Urban-Rural Population and Land Area Estimates dataset provides estimates of urban and rural populations and land areas for the years 1990, 2000 and 2015 for 234 countries and other areas with coastal elevations of no more than 5m above sea level. These data may be useful in tracking urbanareas at risk of coastal hazards.", - "indicators": "Urban Flooding", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "UNBOUND", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v3/data-download", - "strengths": "Includes data for 234 countries", - "format": "GeoTIFF or XLSM", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "1990, 2000, 2015", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 21, - "fields": { - "dataset": "ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015", - "description": "This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. 1985-01-01 to 2015-12-31\n", - "description_simplified": "The ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015 dataset includes differenced Normalized Burned Ratio (dNBR) data for burn scars from fires within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project in Alaska and Canada 1985-2015. These data may be useful in fire disaster research.", - "indicators": "Disasters", - "intended_use": "Path C", - "latency": "", - "limitations": "Lacks recent data", - "project": "", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2111787144-ORNL_CLOUD.html", - "strengths": "30-year temporal extent", - "format": "GeoTIFF", - "geographic_coverage": "Alaska and Canada", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1985-01-01 to 2015-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 22, - "fields": { - "dataset": "ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015", - "description": "This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks.", - "description_simplified": "The ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015 dataset provides maps of landscape burn severity from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. These data may be useful in fire disaster and recovery research.", - "indicators": "Disasters", - "intended_use": "Pac C", - "latency": "", - "limitations": "Lacks recent data", - "project": "", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2143402644-ORNL_CLOUD.html", - "strengths": "Pre-classified burn severity for ease of use", - "format": "GeoTIFF", - "geographic_coverage": "Northwest Territories, Canada", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "LINK BROKEN 5/30/22", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 23, - "fields": { - "dataset": "SEDAC Hazards Mapper", - "description": "The SEDAC Hazards Mapper enables users to visualize data and map layers related to Socioeconomic, Infrastructure, Natural Disasters, and Environment and analyze potential impacts and exposure. The web app mashups layers from various sources including SEDAC, NASA LANCE, NASA GIBS, USGS, NOAA, ESRI, and others.", - "description_simplified": "The SEDAC Hazards Mapper enables users to visualize data and map layers related to Socioeconomic, Infrastructure, Natural Disasters, and Environment and analyze potential impacts and exposure. The web app mashups layers from various sources including SEDAC, NASA LANCE, NASA GIBS, USGS, NOAA, ESRI, and others.", - "indicators": "Disasters", - "intended_use": "Path A", - "latency": "", - "limitations": "no data download", - "project": "", - "source_link": "https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/sedac-hazards-mapper", - "strengths": "interactive mapper for all data users", - "format": "interactive mapper, not data download", - "geographic_coverage": "Global", - "data_visualization": "interactive mapper", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 24, - "fields": { - "dataset": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3", - "description": "The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper \"Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter\" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR).", - "description_simplified": "The Urban-Rural Population and Land Area Estimates dataset provides estimates of urban and rural populations and land areas for the years 1990, 2000 and 2015 for 234 countries and other areas with coastal elevations of no more than 5m above sea level. These data may be useful in tracking urbanareas at risk of coastal hazards. ", - "indicators": "Urban Flooding", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "LECZ - Low Elevation Coastal Zone", - "source_link": "https://doi.org/10.7927/d1x1-d702", - "strengths": "Includes data for 234 countries", - "format": "GeoTIFF, Excel, PDF, PNG", - "geographic_coverage": "Afghanistan; Aland Islands; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antigua and Barbuda; Argentina; Armenia; Aruba; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bonaire; Bosnia and Herzegovina; Botswana; Bouvet Island; Brazil; British Indian Ocean Territory; British Virgin Islands; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote d'Ivoire; Croatia; Cuba; Curacao; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Faeroe Islands; Falkland Islands; Fiji; Finland; France; French Guiana; French Polynesia; French Southern Territories; Gabon; Gambia; Georgia; Germany; Ghana; Gibraltar; Global; Greece; Greenland; Grenada; Guadeloupe; Guam; Guatemala; Guernsey; Guinea; Guinea-Bissau; Guyana; Haiti; Heard Island and McDonald Islands; Holy See; Honduras; Hong Kong; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Isle of Man; Israel; Italy; Jamaica; Japan; Jersey; Jordan; Kazakhstan; Kenya; Kiribati; Kosovo; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macau; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Martinique; Mauritania; Mauritius; Mayotte; Mexico; Micronesia; Moldova; Monaco; Mongolia; Montenegro; Montserrat; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; Norfolk Island; North Korea; Northern Mariana Islands; Norway; Oman; Pakistan; Palau; Palestine; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Pitcairn Islands; Poland; Portugal; Puerto Rico; Qatar; Reunion; Romania; Russia; Rwanda; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Georgia and the South Sandwich Islands; South Korea; South Sudan; Spain; Spratly Islands; Sri Lanka; St Barthelemy; St Helena; St Kitts and Nevis; St Lucia; St Maarten; St Martin; St Pierre and Miquelon; St Vincent and The Grenadines; Sudan; Suriname; Svalbard and Jan Mayen; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Taiwan; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Tokelau; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Turks and Caicos Islands; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States of America; Uruguay; US Virgin Islands; Uzbekistan; Vanuatu; Venezuela; Vietnam; Wallis and Futuna Islands; Western Sahara; Yemen; Zambia; Zimbabwe", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v3/maps", - "spatial_resolution": "0.0083 Decimal Degrees", - "temporal_extent": "1990-01-01; 2000-01-01; 2015-01-01", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 25, - "fields": { - "dataset": "Shared Socioeconomic Pathways (SSPs)", - "description": "To provide a literature database tracking the use of a global scenarios framework consisting of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), for climate, socioeconomic, environmental, and other related research. The Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019 consists of biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019 that used the SSPs.", - "description_simplified": "The Shared Socioeconomic Pathways (SSPs) dataset compiles biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019, generated from a Google Scholar search. This dataset may aid in climate, socioeconomic, environmental, and other related research.", - "indicators": "Human Dimensions", - "intended_use": "Path C", - "latency": "", - "limitations": "Compiled literature data, not spatial data", - "project": "Disaster Dashboard", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/ssp-ssp-literature-db-v1", - "strengths": "Significant temporal extent", - "format": "Excel workbook", - "geographic_coverage": "", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "2014-2019", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 26, - "fields": { - "dataset": "Food Insecurity Hotspots Data Set, v1 (2009\u200a\u2013\u200a2019)", - "description": "The Food Insecurity Hotspots Data Set consists of grids at 250 meter (~7.2 arc-seconds) resolution that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events. The gridded data are based on subnational food security analysis provided by FEWS NET (Famine Early Warning Systems Network) in five (5) regions, including Central America and the Caribbean, Central Asia, East Africa, Southern Africa, and West Africa. Based on the Integrated Food Security Phase Classification (IPC), food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", - "description_simplified": "The Food Insecurity Hotspots Dataset shows the level of intensity and frequency of food insecurity as well as hotspot areas that have experienced consecutive food insecurity events. Food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited geographic coverage.", - "project": "FOOD - Food Security", - "source_link": "https://doi.org/10.7927/cx02-2587", - "strengths": "10 years temporal extent", - "format": "GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/food-food-insecurity-hotspots/maps", - "spatial_resolution": "0.00833 Decimal Degrees", - "temporal_extent": "2009-01-01 to 20191231", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 27, - "fields": { - "dataset": "Poverty Mapping Project: Small Area Estimates of Poverty and Inequality", - "description": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality data set consists of consumption-based poverty, inequality and related measures for subnational administrative units in approximately twenty countries throughout Africa, Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and survey data using small area estimates techniques. The collection of data have been compiled, integrated and standardized from the original data providers into a unified spatially referenced and globally consistent data set. The data products include shapefiles (vector data), tabular data sets (csv format), and centroids (csv file with latitude and longitude of a geographic unit and associated poverty estimates). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with a number of external data providers.", - "description_simplified": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality dataset shows poverty, inequality, and other related data for approximately twenty ountries throughout Afirca, Asia, Europe, North America, and South America. These data may be useful in poverty and inequity research.", - "indicators": "Human Dimensions", - "intended_use": "Path A", - "latency": "", - "limitations": "Limited geographic coverage", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/H49P2ZKM", - "strengths": "Country-level data that has been integrated and compiled, integrated and standardized into a unified spatially referenced and globally consistent data set.", - "format": "CSV, PDF, PNG", - "geographic_coverage": "Albania; Bangladesh; Bolivia; Bulgaria; Cambodia; China; Dominican Republic; Ecuador; Gaza Strip; Guatemala; Honduras; Indonesia; Kenya; Madagascar; Malawi; Morocco; Mozambique; Nepal; Nicaragua; Panama; Papua New Guinea; Paraguay; Philippines; Uganda; Vietnam; West Bank", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/povmap-small-area-estimates-poverty-inequality/maps", - "spatial_resolution": "", - "temporal_extent": "1990-01-01 to 2002-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 28, - "fields": { - "dataset": "Gridded Population of the World (GPW), v4", - "description": "The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010.", - "description_simplified": "The Gridded Population of the World dataset includes basic demographic information such as age, sex, and population density for the year 2010.", - "indicators": "Human Dimensions", - "intended_use": "Path A", - "latency": "", - "limitations": "Single time slicce (year) available", - "project": "Disaster Dashboard", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/gpw-v4-basic-demographic-characteristics-rev11", - "strengths": "Several data formats available", - "format": "GeoTIFF, ASCII, and netCDF-4 formats", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "", - "temporal_extent": 2010, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 29, - "fields": { - "dataset": "US Social Vulnerability Index", - "description": "The U.S. Social Vulnerability Index Grids data set contains gridded layers for the overall Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) using four sub-category themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) based on census tract level inputs from 15 variables for the years 2000, 2010, 2014, 2016, and 2018. SVI values range between 0 and 1 based on their percentile position among all census tracts in the U.S., with 0 representing lowest vulnerability census tracts and 1 representing highest vulnerability census tracts. SEDAC has gridded these vector inputs to create 1 km spatial resolution raster surfaces allowing users to obtain vulnerability metrics for any user-defined area within the U.S. Utilizing inputs from CIESIN's Gridded Population of the World, Version 4, Revision 11 (GPWv4.11), a mask is applied for water, and optionally, for no population. The data are provided in two different projection formats, NAD83 as a U.S. specific standard, and WGS84 as a global standard. The goal of the SVI is to help identify vulnerable communities by ranking them on these inputs across the U.S.", - "description_simplified": "The U.S. Social Vulnerability Index Grids identifies socially vulnerable populations at higher risk due to four main factors: socioeconomic status, household composition & disability, minority status & language, and housing type & transportation. These data are produced by the CDC (Centers for Disease Control and Prevention based on census data from the years 200, 2010, 2014, 2016, and 2018.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "USCG - U.S. Census Grids", - "source_link": "https://doi.org/10.7927/6s2a-9r49", - "strengths": "Temporal extent varies", - "format": "GeoTIFF, PDF, PNG", - "geographic_coverage": "United States of America", - "data_visualization": "", - "spatial_resolution": "0.0083 Decimal Degrees", - "temporal_extent": "2000-01-01, 2010-01-01, 2014-01-01, 2016-01-01, 2018-01-01", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 30, - "fields": { - "dataset": "US Census Grids 2010", - "description": "The U.S. Census Grids (Summary File 1), 2010 data set contains grids of demographic and socioeconomic data from the year 2010 in ASCII and GeoTIFF formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2010 TIGER/Line Files and census variables (population, households, and housing variables).", - "description_simplified": "The US Census Grids 2010 provide gridded demographic data, including age, race, ethnicity, and housing for the US and Puerto Rico.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Data only available for the year 2010", - "project": "USCG - U.S. Census Grids", - "source_link": "https://doi.org/10.7927/H40Z716C", - "strengths": "Several socioeconomic factors available within dataset", - "format": "ASCII, GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; District of Columbia; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Puerto Rico; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.0083 Decimal Degrees", - "temporal_extent": "2010-01-01 to 2010-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 31, - "fields": { - "dataset": "Global Estimated Net Migration Grids By Decade: 1970-2000", - "description": "The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of globally consistent data on migration, indirect estimation methods were used. The authors relied on a combination of data on spatial population distribution for four time slices (1970, 1980, 1990, and 2000) and subnational rates of natural increase in order to derive estimates of net migration on a 30 arc-second (~1km) grid cell basis. Net migration was estimated by subtracting the population in time period 2 from the population in time period 1, and then subtracting the natural increase (births minus deaths). The residual was considered to be net migration (in-migrants minus out-migrants). The authors ran 13 geospatial net migration estimation models based on outputs from the same number of imputation runs for urban and rural rates of natural increase.This data set represents the average of those runs. These data are reliable at broad scales of analysis (e.g. ecosystems or regions), but are generally not reliable for local level analyses. The data were produced for the United Kingdom Foresight project on Migration and Global Environmental Change.", - "description_simplified": "The Global Estimated Net Migration Grids By Decade provides estimates of overall net migration (in-migration minus out-migration) per decade for the 1970s, 1980s, and 1990s.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "POPDYNAMICS - Population Dynamics", - "source_link": "https://doi.org/10.7927/H4319SVC", - "strengths": "30 year temporal extent", - "format": "GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.008333 Decimal Degrees", - "temporal_extent": "1970-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 32, - "fields": { - "dataset": "Global Relative Deprivation Index- Alpha Version", - "description": "Developed, but not disseminated yet- contact SEDAC for access", - "description_simplified": "Contact SEDAC for more info", - "indicators": "Human Dimensions", - "intended_use": "", - "latency": "", - "limitations": "", - "project": "Other", - "source_link": "Contact SEDAC for access", - "strengths": "", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 33, - "fields": { - "dataset": "Facebook High Resolution Population Density Maps", - "description": "Facebook collaborates with SEDAC on data; high resolution and updated frequently", - "description_simplified": "Facebook High Resolution Population Density Maps are available through the collaboration of SEDAC and Facebook. These data include demographic information that is high resolution and updated frequently. \n", - "indicators": "Human Dimensions", - "intended_use": "Path A", - "latency": "", - "limitations": "Data format and download unclear", - "project": "Dashboard, other", - "source_link": "Facebook Data For Good High Resolution Population Density Maps", - "strengths": "High resolution, updated frequently.", - "format": "", - "geographic_coverage": "", - "data_visualization": "Facebook visualization", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 34, - "fields": { - "dataset": "Global Rural-Urban Mapping Project (GRUMP) Urban Extent Polygons, v1", - "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02 is an update to Revision 01, which included new settlements and represented the first time that SEDAC released polygons (in Esri shapefile format) with the settlement name (or name of the largest city in the case of multi-city agglomerations). The shapefile consists of polygons defined by the extent of the nighttime lights and approximated urban extents (circles) based on buffered settlement points. Revision 01 also included new urban extents identified from multiple sources and corrected georeferencing for some settlements (see separate documentation for Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 for the data and methods). Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with CUNY Institute for Demographic Research (CIDR). Revision 02 was produced by CIESIN.", - "description_simplified": "The Global Rural-Urban Mapping Project (GRUMP) Urban Extent Polygons dataset maps urban settlements in a polygon or shapefile format defined by the extent of nighttime lights and approximated urban areas. These data are useful in studying urbanization and human migration.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Temporal extent only 1995; lacks recent data", - "project": "GRUMP - Global Rural-Urban Mapping Project", - "source_link": "https://doi.org/10.7927/np6p-qe61", - "strengths": "Population and urban extent in shapefile form", - "format": "Shapefile, CSV", - "geographic_coverage": "Africa; Asia; Australia; Europe; Global; North America; South America; Oceania; Afghanistan; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antigua and Barbuda; Argentina; Armenia; Aruba; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; British Virgin Islands; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Colombia; Comoros; Cook Islands; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Congo, Democratic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Faeroe Islands; Falkland Islands; Fiji; Finland; France; French Guiana; French Polynesia; Gabon; Gambia; Georgia; Germany; Ghana; Gibraltar; Greece; Greenland; Grenada; Guadeloupe; Guam; Guatemala; Guernsey; Guinea; Guinea-Bissau; Guyana; Haiti; Honduras; Hong Kong; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Isle of Man; Israel; Italy; Jamaica; Japan; Jersey; Jordan; Kazakhstan; Kenya; Kiribati; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macau; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Martinique; Mauritania; Mauritius; Mayotte; Mexico; Micronesia; Moldova; Monaco; Mongolia; Montserrat; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; Netherlands Antilles; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; North Korea; Northern Mariana Islands; Norway; Oman; Pakistan; Palau; Palestine; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Pitcairn Islands; Poland; Portugal; Puerto Rico; Qatar; Congo; Reunion; Romania; Russia; Rwanda; St Helena; St Kitts and Nevis; St Lucia; St Pierre and Miquelon; St Vincent and The Grenadines; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia and Montenegro; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Korea; Spain; Sri Lanka; Sudan; Suriname; Svalbard and Jan Mayen; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Taiwan; Tajikistan; Tanzania; Timor; Togo; Tokelau; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Turks and Caicos Islands; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States; Uruguay; US Virgin Islands; Uzbekistan; Vatican City; Vanuatu; Venezuela; Vietnam; Wallis and Futuna Islands; Yemen; Yugoslavia; Zambia; Zimbabwe", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "1995-07-01", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 35, - "fields": { - "dataset": "Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, v1 (2000\u200a\u2013\u200a2100)", - "description": "To provide global SSP-consistent spatial urban land projections and base year grids based on the Shared Socioeconomic Pathways (SSPs) data at a resolution of 1-km (about 30 arc-seconds) for climate, socioeconomic, environmental, and other related research.", - "description_simplified": "The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios dataset provides urban land projections based on the Shared Socioeconomic Pathways (SSPs) data. These data are useful in socioeconomic, environmental, and urban sprawl research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Recent years' data are projections from original dataset publishing date", - "project": "Other", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/ssp-1-km-downscaled-urban-land-extent-projection-base-year-ssp-2000-2100/data-download", - "strengths": "Includes projections for up to year 2100", - "format": "GeoTIFF and .nc", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "1 km", - "temporal_extent": "2000-2100", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 36, - "fields": { - "dataset": "Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, v1 (2000\u200a\u2013\u200a2100)", - "description": "The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes). Spatial urban land projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts and adaptation. This data set presents a set of global, spatially explicit urban land scenarios that are consistent with the Shared Socioeconomic Pathways (SSPs) to produce an empirically-grounded set of urban land spatial distributions over the 21st century. A data-science approach is used exploiting 15 diverse data sets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations from the Landsat satellite series. The SSPs are developed to support future climate and global change research, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with Special Reports.", - "description_simplified": "The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios dataset provides global urban land projections based on the SSP (Shared Socioeconomic Pathways) data. These data may be useful in climate, socioeconomic, environmental, or other related research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Recent years' data are projections from original dataset publishing date", - "project": "SSP - Shared Socioeconomic Pathways", - "source_link": "https://doi.org/10.7927/nj0x-8y67", - "strengths": "Includes past data and projections", - "format": "GeoTIFF, netCDF-4, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "7.5 Arc-Minutes", - "temporal_extent": "2000-01-01 to 21000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 37, - "fields": { - "dataset": "Global Subnational Infant Mortality Rates, v2.01 (2015)", - "description": "The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.", - "description_simplified": "The Global Subnational Infant Mortality Rates dataset provides a global subnational map of infant mortality rate estimates for the year 2015. These data may aid interdisciplinary studies of health, poverty, and the environment.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Only for 2015", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/0gdn-6y33", - "strengths": "Three data formats available: GeoTIFF, gdb, and excel", - "format": "GeoTIFF, Geodatabase, Excel, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "0.008333 Decimal Degrees", - "temporal_extent": "2015-01-01 to 2015-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 38, - "fields": { - "dataset": "Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020\u200a\u2013\u200a2100)", - "description": "The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.", - "description_simplified": "The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age dataset provides county population projections for the US essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Recent years' data are projections from original dataset publishing date", - "project": "Other", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/popdynamics-us-county-level-pop-projections-sex-race-age-ssp-2020-2100/data-download", - "strengths": "100-year temporal extent", - "format": "shape or excel", - "geographic_coverage": "US- county-level", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "2020-2100", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 39, - "fields": { - "dataset": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m V001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. 2013-01-01 to 2016-06-30\n", - "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m V001 dataset provides cropland extent data over Africa for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", - "indicators": "Food Availability", - "intended_use": "Path C", - "latency": "", - "limitations": "Lacks recent data", - "project": "Climate justice", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C1429296018-LPDAAC_ECS.html", - "strengths": "", - "format": "GeoTIFF", - "geographic_coverage": "Africa", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "2013-01-01 to 2016-06-30", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 40, - "fields": { - "dataset": "Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. 2013-01-01 to 2016-12-31\n", - "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001 dataset provides cropland extent data over Australia, New Zealand, China, and Mongolia for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", - "indicators": "Food Availability", - "intended_use": "Path C", - "latency": "", - "limitations": "Lacks recent data", - "project": "Climate justice", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C1431392676-LPDAAC_ECS.html", - "strengths": "", - "format": "GeoTIFF", - "geographic_coverage": "Australia, New Zealand, China, Mongolia", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "2013-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 41, - "fields": { - "dataset": "Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast and Northeast Asia product 30 m V001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. 2013-01-01 to 2016-12-31\n", - "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast and Northeast Asia product 30 m V001 dataset provides cropland extent data over Southeast and Northeast Asia for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", - "indicators": "Food Availability", - "intended_use": "Path C", - "latency": "", - "limitations": "Lacks recent data", - "project": "", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C1431439087-LPDAAC_ECS.html", - "strengths": "", - "format": "GeoTIFF", - "geographic_coverage": "Southeast and Northeast Asia", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "2013-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 42, - "fields": { - "dataset": "MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V011", - "description": "The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. 2016-01-01 to 2019-12-31", - "description_simplified": "The MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V011 dataset provides land Surface Phenology (seasonal changes in plant greening, flowering, etc) for North America. These datasets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping.", - "indicators": "Climate Change", - "intended_use": "Path C", - "latency": "", - "limitations": "Only available for North America", - "project": "", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2102664483-LPDAAC_ECS.html", - "strengths": "Semi-recent data available", - "format": "netCDF-4", - "geographic_coverage": "North America", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "2016-01-01 to 2019-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 43, - "fields": { - "dataset": "Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East product 30 m V001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security.", - "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East product 30 m V001 dataset provides cropland extent data for Europe, Central Asia, Russia, and the Middle East for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", - "indicators": "Food Availability", - "intended_use": "Path C", - "latency": "", - "limitations": "Lacks recent data", - "project": "", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C1431401246-LPDAAC_ECS.html", - "strengths": "Includes all of Northern Hemisphere", - "format": "GeoTIFF", - "geographic_coverage": "Northern Hemisphere: Europe, Central Asia, Russia, and Middle East", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "2013-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 44, - "fields": { - "dataset": "Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South America product 30 m V001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security.", - "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East product 30 m V001 dataset provides cropland extent data for South America for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", - "indicators": "Food Availability", - "intended_use": "Path C", - "latency": "", - "limitations": "Lacks recent data, limited to South America", - "project": "", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C1431432916-LPDAAC_ECS.html", - "strengths": "", - "format": "GeoTIFF", - "geographic_coverage": "South America", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "2013-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 45, - "fields": { - "dataset": "Famine Early Warning Systems Network", - "description": "FEWS NET, the Famine Early Warning Systems Network, is a leading provider of early warning and analysis on acute food insecurity around the world.Created in 1985 by the United States Agency for International Development (USAID) in response to devastating famines in East and West Africa, FEWS NET provides unbiased, evidence-based analysis to governments and relief agencies who plan for and respond to humanitarian crises. FEWS NET analyses support resilience and development programming as well. FEWS NET analysts and specialists work with scientists, government ministries, international agencies, and NGOs to track and publicly report on conditions in the world\u2019s most food-insecure countries.", - "description_simplified": "The Famine Early Warning Systems Network (FEWS NET) provides early warning and analysis on acute food insecurity around the world. These data may be useful in food insecuriy and response research.", - "indicators": "Food Availability", - "intended_use": "Path A", - "latency": "", - "limitations": "multiple datasets available", - "project": "", - "source_link": "https://fews.net/", - "strengths": "multiple datasets available", - "format": "Shapefiles", - "geographic_coverage": "Global", - "data_visualization": "visualization available", - "spatial_resolution": "", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 46, - "fields": { - "dataset": "FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System", - "description": "The FLDAS Global model (McNally et al. 2017) is a custom instance of the NASA Land Information System (LIS; http://lis.gsfc.nasa.gov/) that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. Adopting LIS allows FEWS NET to leverage existing land surface models and generate ensembles of soil moisture, ET, and other variables based on multiple meteorological inputs or land surface models. The goal of the FLDAS project is to achieve more effective use of limited available hydroclimatic observations and is designed to be adopted for routine use for FEWS NET decision support.", - "description_simplified": "The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) dataset is a custom model incorporating various data and monitoring systems. These data may aid in food insecurity research.", - "indicators": "Food Availability", - "intended_use": "Path B", - "latency": "", - "limitations": "multiple datasets available", - "project": "", - "source_link": "https://ldas.gsfc.nasa.gov/fldas", - "strengths": "multiple datasets available", - "format": "NetCDF, KMZ, GeoTIFF", - "geographic_coverage": "Global", - "data_visualization": "visualization available", - "spatial_resolution": "", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 47, - "fields": { - "dataset": "Global Gridded Relative Deprivation Index (GRDI), Version 1", - "description": "The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) data set characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final index raster. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage.", - "description_simplified": "Contact SEDAC for more info", - "indicators": "Human Dimensions", - "intended_use": "", - "latency": "", - "limitations": "", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/3xxe-ap97", - "strengths": "", - "format": "GeoTIFF, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/povmap-grdi-v1/maps", - "spatial_resolution": "0.008333 Decimal Degrees", - "temporal_extent": "2010-01-01 to 2020-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 48, - "fields": { - "dataset": "Global High Resolution Daily Extreme Urban Heat Exposure", - "description": "The Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily), 1983-2016 data set contains a high-resolution, longitudinal global record of geolocated urban extreme heat events and urban population exposure estimates for more than 10,000 urban settlements worldwide for 1983-2016. Urban extreme heat events and urban population exposure are identified for each urban settlement in the data record for five combined temperature-humidity thresholds: two-day or longer periods where the daily maximum Heat Index (HImax) > 40.6 \u00b0C; one-day or longer periods where HImax > 46.1 \u00b0C; and one day or longer periods where the daily maximum Wet Bulb Globe Temperature (WBGTmax) > 28 \u00b0C, 30 \u00b0C, and 32 \u00b0C. The WBGTmax thresholds follow the International Standards Organization (ISO) criteria for risk of occupational heat related heat illness, whereas the HImax thresholds follow the U.S. National Weather Services' definition for an excessive heat warning. For each criteria, across urban settlements worldwide, the data set also contains the duration, intensity, and severity of each urban extreme heat event.", - "description_simplified": "The Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily) dataset contains a record of urban extreme heat events and exposure estimates for over 10,000 urban settlements worldwide for the years 1983-2016.", - "indicators": "Extreme Heat", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "SDEI - Satellite-Derived Environmental Indicators", - "source_link": "https://doi.org/10.7927/fq7g-ny13", - "strengths": "33-year temporal extent", - "format": "Shapefile, CSV, JSON", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1983-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 49, - "fields": { - "dataset": "Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Collection 1 V1", - "description": "The Landsat Enhanced Thematic Mapper Plus (ETM+) is a sensor carried onboard the Landsat 7 satellite and has acquired images of the Earth nearly continuously since July 1999, with a 16-day repeat cycle. Landsat ETM+ image data consist of eight spectral bands (band designations), with a spatial resolution of 30 meters for bands 1 to 5 and band 7. Resolution for band 6 (thermal infrared) is 60 meters and resolution for band 8 (panchromatic) is 15 meters. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). The Level 0R data product is reformatted raw data.", - "description_simplified": "Landsat 7 satellite data provides continuous images from July 1999, repeating every 16 days. Surface reflectance data may be helpful in identifying vegetation coverage in the context of urban heat. Data is provided in its raw format.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search/granules?p=C1427459680-USGS_EROS&pg[0][v]=f&q=landsat%207&tl=1647148623.75!3!!", - "strengths": "Multiple datasets available", - "format": "varies- multiple datasets included", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets included", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies- multiple datasets included" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 50, - "fields": { - "dataset": "HLS (Harmonized Landsat and Sentinel) Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0", - "description": "The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe\u2019s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.", - "description_simplified": "The Harmonized Landsat and Sentinel-2 (HLS) project provides surface reflectance data from the joint NASA/USGS Landsat 8 Satellite. Surface reflectance data may be useful in identifying vegetation coverage in the context of urban heat. The dataset includes global observations every 2-3 days at 30-meter spatial resolution. Data avaliable from 11/28/2015 to now.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "Latency unclear", - "project": "Urban Heat", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2021957295-LPCLOUD.html", - "strengths": "recent data available", - "format": "Cloud Optimized GeoTIFF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "30m x 30m", - "temporal_extent": "2015-11-28 ongoing", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 51, - "fields": { - "dataset": "HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0", - "description": "The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe\u2019s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30-meter (m) spatial resolution.", - "description_simplified": "The HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 dataset provides surface reflectance (SR) and top of atmosphere (TOA) brightness data from Landsat 8 satellite data.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "Latency unclear", - "project": "Urban Heat", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2021957657-LPCLOUD.html", - "strengths": "Cloud-optimized", - "format": "Cloud Optimized GeoTIFF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "2013-04-11 ongoing", - "temporal_resolution": "2-3 days" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 52, - "fields": { - "dataset": "Landsat Collection 2 Surface Temperature", - "description": "Landsat surface temperature measures the Earth\u2019s surface temperature in Kelvin and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature data are also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.", - "description_simplified": "The Landsat Collection 2 Surface Temperature dataset measures Earth\u2019s surface temperature in Kelvin and is important global modeling. Surface temperature data are also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "multiple datasets available", - "project": "Urban Heat", - "source_link": "https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "image/example available", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 53, - "fields": { - "dataset": "AIRS Surface Air Temperature from Earthdata Search", - "description": "NASA's Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite gathers infrared energy emitted from Earth's surface and atmosphere globally every day. AIRS data are daily, 8-day, and monthly at 1 degree and the Level 3 data products are provided in either the descending (equatorial crossing North to South at 1:30 a.m. local time) or ascending (equatorial crossing South to North at 1:30 p.m. local time) orbit. When you open the file in HDF format (in a program like Panoply or QGIS), you will see an ascending option and a descending option each with SurfAirTemp.", - "description_simplified": "The AIRS Surface Air Temperature from Earthdata Search dataset provides global air surface temperature. AIRS data are daily, 8-day, and monthly at 1 degree. These data may aid in urban heat research.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "difficult to determine differences between datasets included", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=AIRS3ST&fi=AIRS&fs10=Surface%20Temperature&fsm0=Atmospheric%20Temperature&fs20=Air%20Temperature&fst0=Atmosphere", - "strengths": "daily temporal resolution", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "1 degree", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "daily, 8-day, and monthly" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 54, - "fields": { - "dataset": "MERRA-2 Temperature from Earthdata Search", - "description": "The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface skin temperature, the air temperature at 2 m, and the air temperature at 10 m.", - "description_simplified": "The MERRA-2 (Modern-Era Retrospective analysis for Research and Applications) dataset provide air surface temperature data on surface skin temperature, the air temperature at 2m, and the air temperature at 10m. These data may aid in urban heat research.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "many datasets included, can be confusing", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=merra-2&fs10=Surface%20Temperature&fsm0=Atmospheric%20Temperature&fs20=Air%20Temperature&fst0=Atmosphere", - "strengths": "hourly temporal resolution", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "0.625 x 0.5 degree", - "temporal_extent": "1980-01-01 ongoing", - "temporal_resolution": "1-hourly, 3-hourly, 6-hourly" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 55, - "fields": { - "dataset": "Terra MODIS Land Surface Temperature from Earthdata Search", - "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11A1) Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", - "description_simplified": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11A1) Version 6.1 datatset provides daily Land Surface Temperature and Emissivity (LST&E) on a global scale. Many Land surface temperature datasets", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "multiple datasets available", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=MOD11&fsm0=Surface%20Radiative%20Properties&fst0=Land%20Surface", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "1 km", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 56, - "fields": { - "dataset": "Terra MODIS Land Surface Temperature/3-Band Emissivity", - "description": "", - "description_simplified": "This link includes multiple MODIS Land surface temperature datasets.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "multiple datasets available", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=MOD21&fp=Terra", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets included", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies- multiple datasets included" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 57, - "fields": { - "dataset": "VIIRS Land Surface Temperature from Earthdata Search", - "description": "", - "description_simplified": "This link includes multiple MODIS Land surface temperature datasets.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "multiple datasets available", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=VNP21&fi=VIIRS", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets included", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies- multiple datasets included" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 58, - "fields": { - "dataset": "VIIRS Land Surface Temperature/3-Band Emissivity", - "description": "", - "description_simplified": "This link includes multiple MODIS Land surface temperature datasets.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "multiple datasets available", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=VNP21", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets included", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies- multiple datasets included" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 59, - "fields": { - "dataset": "ASTER Surface Kinetic Temperature from Earthdata Search", - "description": "ASTER Surface Temperature products are processed on-demand and so must be requested with additional parameters. Note that there is a limit to 2000 granules per order.", - "description_simplified": "The The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) dataset provides detailed land surface temperature data. These data may aid in urban heat research.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "only HDF-EOS2 format available", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=ASTER%20Surface%20Kinetic%20Temperature&fst0=Land%20Surface", - "strengths": "22-year temporal extent", - "format": "HDF-EOS2", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "90 m", - "temporal_extent": "2000-03-04 ongoing", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 60, - "fields": { - "dataset": "ECOSTRESS Land Surface Temperature from Earthdata Search", - "description": "ECOSTRESS land surface temperature and urban heat analysis are being used by the city of Los Angeles to determine mitigation strategies for urban heat and heat stress. See how our data is used in this presentation by the Assistant Director of Los Angeles Bureau of Street Services link", - "description_simplified": "The ECOSTRESS Land Surface Temperature dataset provides land surface temperature data and urban heat analysis for the city of Los Angeles. These data may aid mitigation strategies for urban heat and heat stress.", - "indicators": "Extreme Heat", - "intended_use": "Path B", - "latency": "", - "limitations": "Latency unclear", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=ECO2LSTE&fl=2%20-%20Geophys.%20Variables%2C%20Sensor%20Coordinates", - "strengths": "Recent data available", - "format": "HDF-5", - "geographic_coverage": "Los Angeles", - "data_visualization": "", - "spatial_resolution": "70 m", - "temporal_extent": "2018-07-09 ongoing", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 61, - "fields": { - "dataset": "NLDAS Level 4 Monthly Climatology", - "description": "This monthly climatology data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1.", - "description_simplified": "The NLDAS Level 4 Monthly Climatology dataset provides monthly heat and weather-related data. These data may aid in urban heat and resilience research.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "lacks recent data, uncommon data format", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=NLDAS_NOAH0125_MC", - "strengths": "29-year temporal resolution", - "format": "GRIB", - "geographic_coverage": "United States", - "data_visualization": "", - "spatial_resolution": "0.125 degree", - "temporal_extent": "1980-2009", - "temporal_resolution": "monthly" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 62, - "fields": { - "dataset": "AIRS Relative Humidity from Earthdata Search", - "description": "AIRS data are daily at 1 degree and the Level 3 data products are provided in either the descending (equatorial crossing North to South at 1:30 a.m. local time) or ascending (equatorial crossing South to North at 1:30 p.m. local time) orbit. Note that the data were acquired only until 2016.", - "description_simplified": "The AIRS Relative Humidity from Earthdata Search dataset provide global humidity data. These data may aid in urban heat and resilience research.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "multiple datasets available", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=AIRX3std", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 63, - "fields": { - "dataset": "MERRA-2 Humidity from Earthdata Search", - "description": "There are several options available: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface specific humidity, specific humidity at 2 m, and relative humidity.", - "description_simplified": "The MERRA-2 Humidity from Earthdata Search dataset provides data on three measureable types of humidity: surface specific humidity, humidity at 2m, and relative humidity. These data may aid in urban heat and resilience research.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "multiple datasets available", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=merra-2&fs10=Surface%20Temperature&fsm0=Atmospheric%20Temperature&fs20=Air%20Temperature&fst0=Atmosphere", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 64, - "fields": { - "dataset": "GEOS-5 Weather Maps", - "description": "Within the viewer, select the parameter or field of interest, the area of interest, and then indicate the forecast time and the forecast lead hour. Animate shows the forecast for the given parameter over the time period indicated. Note that it may take time to load the images to animate. For those variables near the surface, make sure to select 850 as your level.", - "description_simplified": "The GEOS-5 Weather Maps provides a viewer, allowing users to select a parameter or field of interest, area of interest, and a forcast time. Users can then produce an animation of the selected weather parameters.", - "indicators": "Extreme Heat", - "intended_use": "Path A", - "latency": "", - "limitations": "multiple datasets available", - "project": "Urban Heat", - "source_link": "https://fluid.nccs.nasa.gov/wxmaps/", - "strengths": "multiple datasets available", - "format": "", - "geographic_coverage": "United States", - "data_visualization": "dataset is a viewer", - "spatial_resolution": "", - "temporal_extent": "Varies", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 65, - "fields": { - "dataset": "Global Urban Heat Island (UHI) Data Set, v1 (2013)", - "description": "The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC?s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC?s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).", - "description_simplified": "SEDAC's Global Urban Heat Island (UHI) Data Set, v1 (2013) dataset provides a global dataset of average summer daytime maximum land surface temperatures and minimum nighttime land surface temperatures. These data may aid in urban heat research.", - "indicators": "Extreme Heat", - "intended_use": "Path A", - "latency": "", - "limitations": "lacks recent data", - "project": "SDEI - Satellite-Derived Environmental Indicators", - "source_link": "https://doi.org/10.7927/H4H70CRF", - "strengths": "Shapefile format for use in GIS", - "format": "Shapefile, Excel, PDF, PNG, WMS", - "geographic_coverage": "Africa; Asia; Australia; Europe; Global; North America; Oceanography; South America", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/sdei-global-uhi-2013/maps", - "spatial_resolution": "", - "temporal_extent": "2013-01-01 to 2013-12-31", - "temporal_resolution": "Unsure" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 66, - "fields": { - "dataset": "Global Man-made Impervious Surface (GMIS) Dataset From Landsat, v1 (2010)", - "description": "The Global Man-made Impervious Surface (GMIS) Dataset From Landsat consists of global estimates of fractional impervious cover derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. The GMIS dataset consists of two components: 1) global percent of impervious cover; and 2) per-pixel associated uncertainty for the global impervious cover. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. The spatial extent covers the entire globe except Antarctica and some small islands. This dataset is one of the first global, 30m datasets of man-made impervious cover to be derived from the GLS data for 2010 and is a companion dataset to the Global Human Built-up And Settlement Extent (HBASE) dataset. The dataset is expected to have a rather broad spectrum of users, from those wishing to examine/study the fine details of urban land cover over the globe at full 30m resolution to global modelers trying to understand the climate/environmental impacts of man-made surfaces at continental to global scales. For example, the data are applicable to local modeling studies of urban impacts on the energy, water, and carbon cycles, as well as analyses at the individual country level.", - "description_simplified": "The Global Man-made Impervious Surface (GMIS) Dataset From Landsat consists of global estimates of fractional impervious cover derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. These data show the extent of land cover that does not absorb water such as concrete and roads. These data may aid in urban flooding and urban heat research. ", - "indicators": "Extreme Heat", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data, only gmis format available", - "project": "ULANDSAT - Global High Resolution Urban Data from Landsat", - "source_link": "https://doi.org/10.7927/H4P55KKF", - "strengths": "makes landsat data more user-friendly", - "format": "GeoTIFF, PDF, PNG", - "geographic_coverage": "Afghanistan; Aland Islands; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antigua and Barbuda; Argentina; Armenia; Aruba; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bonaire; Bosnia and Herzegovina; Botswana; Brazil; British Indian Ocean Territory; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Curacao; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial; Eritrea; Estonia; Ethiopia; Faeroe Islands; Falkland Islands; Fiji; Finland; France; French Guiana; French Polynesia; Gabon; Gambia; Georgia; Germany; Ghana; Gibraltar; Global; Greece; Greenland; Grenada; Guadeloupe; Guam; Guatemala; Guernsey; Guinea; Haiti; Honduras; Hong Kong; Hungary; Iceland; Bermuda; India; Indonesia; Iran; Iraq; Ireland; Isle of Man; Israel; Italy; Jamaica; Japan; Jersey; Jordan; Kazakhstan; Kenya; Kiribati; Kosovo; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macau; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Martinique; Mauritania; Mauritius; Mayotte; Mexico; Micronesia; Moldova; Monaco; Mongolia; Montenegro; Montserrat; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; Niue; Norfolk Island; North Korea; Northern Mariana Islands; Norway; Oman; Pakistan; Palau; Palestine; Papua New Guinea; Paraguay; Peru; Philippines; Pitcairn Islands; Poland; Portugal; Puerto Rico; Qatar; Reunion; Romania; Russia; Rwanda; Saba; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Korea; South Sudan; Spain; Sri Lanka; St Barthelemy; St Eustatius; St Helena; St Kitts and Nevis; St Lucia; St Maarten; St Martin; St Pierre and Miquelon; St Vincent and The Grenadines; Sudan; Suriname; Svalbard and Jan Mayen; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Taiwan; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Tokelau; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Turks and Caicos Islands; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States of America; Uruguay; US Virgin Islands; Uzbekistan; Vanuatu; Vatican City; Venezuela; Vietnam; Wallis and Futuna Islands; Western Sahara; Yemen; Zambia; Zimbabwe", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ulandsat-gmis-v1/maps", - "spatial_resolution": "", - "temporal_extent": "2010-01-01 to 2010-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 67, - "fields": { - "dataset": "NASA's Precipitation Processing System", - "description": "The Precipitation Processing System (PPS) evolved from the Tropical Rainfall Measuring Mission (TRMM) Science Data and Information System (TSDIS). The purpose of the PPS is to process, analyze and archive data from the Global Precipitation Measurement (GPM) mission, partner satellites and the TRMM mission. The PPS also supports TRMM by providing validation products from TRMM ground radar sites. All GPM, TRMM and Partner public data products are available to the science community and the general public from the TRMM/GPM FTPS and HTTPS Data Archives", - "description_simplified": "The Precipitation Processing System (PPS) processes, combines, analyzes, and archives data from other past and current precipitation datasets. Data can be used to track urban flooding, overall precipitation and drought trends, and significant storm events. ", - "indicators": "Urban Flooding", - "intended_use": "Path B", - "latency": "varies-multiple datasets available", - "limitations": "Data format is unclear.", - "project": "Disaster Dashboard", - "source_link": "https://arthurhou.pps.eosdis.nasa.gov/", - "strengths": "Lots of data available within link for many different uses. ", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Several Available", - "spatial_resolution": "varies", - "temporal_extent": "varies", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 68, - "fields": { - "dataset": "SAR (Synthetic Aperture Radar) Hurricane Monitoring for the Gulf and East Coast", - "description": "Sentinel-1 Water Maps, RGB and RTC imagery over the Gulf and East Coast for the 2021 hurricane season. Updated approximately 4pm EDT, overpasses occur every ~8 days depending on AOI. (Copernicus Sentinel-1, Alaska Satellite Facility)", - "description_simplified": "Visualization of Sentinel-1 Water Maps and other imagery over the Gulf and East Coast for the 2021 hurricane season. Blue shows water extent. Updated at approximately 4pm EDT about every 8 days to show change in water levels pre and post-hurricane. ", - "indicators": "Disasters", - "intended_use": "Path B", - "latency": "", - "limitations": "Data format and download options are unclear.", - "project": "Disaster Dashboard", - "source_link": "https://nasa.maps.arcgis.com/home/webmap/viewer.html?webmap=f33be724f04b4b4c942edd0c9bd18f48", - "strengths": "Visualization of data to show pre and post storm.", - "format": "", - "geographic_coverage": "Gulf and East Coast", - "data_visualization": "ArcGIS viewer", - "spatial_resolution": "30 meters", - "temporal_extent": 2021, - "temporal_resolution": "8 days" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 69, - "fields": { - "dataset": "Global Cyclone Proportional Economic Loss Risk Deciles", - "description": "The Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 minute grid of cyclone hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Cyclone Proportional Economic Loss Risk Deciles shows cyclone hazard economic loss as a proportion of Gross Domestic Product (GDP) of an area. The shown rates are calculated based on how often and to what extend the hazard of cyclones poses to area. These data can aid in tropical storm preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H44F1NNF", - "strengths": "SEDAC Map widget available for visualization.", - "format": "ASCII, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-cyclone-proportional-economic-loss-risk-deciles/maps", - "spatial_resolution": "0.041700 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 70, - "fields": { - "dataset": "Global Multihazard Proportional Economic Loss Risk Deciles", - "description": "The Global Multihazard Proportional Economic Loss Risks is a 2.5 minute grid of a multihazard-based economic loss risk as a proportion of the economic productivity of the analytical Unit, the grid cell. Representation of multihazard risk is not based on a multihazard index but rather on combinations of hazard risk categories, drought, seismic, and hydro. The drought category includes drought only. The seismic category consists of earthquake and volcano hazards. Cyclones, floods, and landslides are included in the hydro category.", - "description_simplified": "The Global Multihazard Proportional Economic Loss Risks shows the multihazard-based economic loss risk as a proportion of economic productivity. In other words, several different hazards are combined and a proportion is created with the economic productivity of an area. These hazards include drought, earthquakes, volcanoes, cyclones, landslides, and flood risk. ", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "Disaster Dashboard", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C179001790-SEDAC.html", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "Archive: ASCII, DBF, PDF, PNG; Distribution: ASCII, DBF, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 71, - "fields": { - "dataset": "Global Multihazard Total Economic Loss Risk Deciles", - "description": "The Global Multihazard Total Economic Loss Risk Deciles is a 2.5 minute grid of global multihazard total economic loss risks. First, for each of the considered hazards (cyclones, droughts, earthquakes, floods, landslides, and volcanoes), subnational distributions of Gross Domestic Product (GDP) are computed using a methodology developed from Sachs et al. (2003). Where applicable, the contributions of subnational units to national GDP estimates, the contribution ratio, are determined using data of varied origin. World Bank Development Indicators are substituted for GDP estimates of varied origin and the subnational GDP is estimated using the fore mentioned contribution ratios. A subnational, per capita GDP is derived and a final GDP estimate per grid cell is made based on grid cell population density. A raw, total economic loss is computed per grid cell using a regional economic loss rate derived from EM-DAT records. To more accurately reflect the confidence surrounding the economic loss estimate, the range of losses are classified into deciles, 10 classes of an approximately equal number of grid cells. A multihazard index is generated by summing the top three deciles of the individual hazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Multihazard Proportional Economic Loss Risks shows the total economic loss risk by combining the risk of several types of hazards. These hazards include drought, earthquakes, volcanoes, cyclones, landslides, and flood risk. This dataset may aid disaster preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4S180F9", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, DBF, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 72, - "fields": { - "dataset": "IPCC (Intergovernmental Panel on Climate Change) Socio-Economic Baseline Dataset", - "description": "The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset consists of population, human development, economic, water resources, land cover, land use, agriculture, food, energy and biodiversity data . This dataset was collated by IPCC from a variety of sources such as The World Bank, United Nations Environment Programme (UNEP), and Food and Agriculture Organization of the United Nations (FAO), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset combines several factors including population, human development, economic, water resources, land cover, land use, agriculture, food, energy, and biodiversity data. Other organizations involved in the production of this dataset include The World Bank, United Nations Environment Programme (UNEP), and Food and Agriculture Organization of the United Nations (FAO), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). This dataset may aid environmental hazard research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "IPCC - Intergovernmental Panel on Climate Change", - "source_link": "https://doi.org/10.7927/H4WM1BB7", - "strengths": "Many socioeconomic factors included", - "format": "Excel", - "geographic_coverage": "Afghanistan; Africa; Albania; Algeria; Andorra; Angola; Antigua and Barbuda; Argentina; Armenia; Asia; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Central African Republic; Chad; Chile; China; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Europe; Fiji; Finland; France; French Guiana; Gabon; Gambia; Georgia; Germany; Ghana; Global; Global; Greece; Grenada; Guatemala; Guinea; Guinea-Bissau; Guyana; Haiti; Honduras; Hong Kong; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Israel; Italy; Jamaica; Japan; Jordan; Kazakhstan; Kenya; Kiribati; Kuwait; Kyrgyzstan; Laos; Latin America; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Mauritania; Mauritius; Mexico; Micronesia; Middle East; Moldova; Monaco; Mongolia; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; New Zealand; Nicaragua; Niger; Nigeria; North America; North Korea; Norway; Oman; Pakistan; Palau; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Poland; Portugal; Qatar; Romania; Russia; Rwanda; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Korea; Spain; Sri Lanka; St Kitts and Nevis; St Lucia; St Vincent and The Grenadines; Sudan; Suriname; Swaziland; Sweden; Switzerland; Syria; Tajikistan; Tanzania; Thailand; Togo; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States of America; Uruguay; Uzbekistan; Vanuatu; Venezuela; Vietnam; Yemen; Yugoslavia; Zambia; Zimbabwe", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1980-01-01; 1991-01-01; 1992-01-01; 1993-01-01; 1994-01-01; 1995-01-01; 2025-01-01", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 73, - "fields": { - "dataset": "Geocoded Disasters (GDIS) Dataset, v1 (1960\u200a\u2013\u200a2018)", - "description": "The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).", - "description_simplified": "The Geocoded Disasters (GDIS) Dataset includes a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters (CRED) Emergency Events Database (EM_DAT). The data included come from 39,953 locations for 9,924 disasters that happened across the world from 1960-2018. Population data are also included. This dataset may aid disaster preparation and recovery research. ", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "PEND - Natural Disasters", - "source_link": "https://doi.org/10.7927/zz3b-8y61", - "strengths": "Significant temporal extent", - "format": "Geopackage, R-Workspace, Geodatabase, CSV, R-script Source Code", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1960-01-01 to 2018-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 74, - "fields": { - "dataset": "Food Insecurity Hotspots Data Set, v1 (2009\u200a\u2013\u200a2019)", - "description": "The Food Insecurity Hotspots Data Set consists of grids at 250 meter (~7.2 arc-seconds) resolution that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events. The gridded data are based on subnational food security analysis provided by FEWS NET (Famine Early Warning Systems Network) in five (5) regions, including Central America and the Caribbean, Central Asia, East Africa, Southern Africa, and West Africa. Based on the Integrated Food Security Phase Classification (IPC), food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", - "description_simplified": "The Food Insecurity Hotspots Dataset shows the level of intensity and frequency of food insecurity as well as hotspot areas that have experienced consecutive food insecurity events. Food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine. ", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited geographic coverage.", - "project": "FOOD - Food Security", - "source_link": "https://doi.org/10.7927/cx02-2587", - "strengths": "10 years temporal extent", - "format": "GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.00833 Decimal Degrees", - "temporal_extent": "2009-01-01 to 20191231", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 75, - "fields": { - "dataset": "Global Flood Hazard Frequency and Distribution", - "description": "The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Flood Hazard Frequency and Distribution compiles data from extreme flood events between 1985 and 2003. Each grid is assigned a value based on the frequency of floods in that area. The higher the number, the more frequent flood events are in that area. ", - "indicators": "Urban Flooding", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4668B3D", - "strengths": "18 years temporal extent", - "format": "ASCII, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "1985-01-01 to 2003-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 76, - "fields": { - "dataset": "ARIA (Advanced Rapid Imaging and Analysis) DPM (Damage Proxy Map) Puerto Rico", - "description": "The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory in Pasadena, California, and Caltech, also in Pasadena, created this Damage Proxy Map (DPM) depicting areas of Eastern Puerto Rico that are likely damaged (shown by red and yellow pixels) as a result of Hurricane Maria (Category 4 at landfall in Puerto Rico on Sept. 20, 2017). The map is derived from synthetic aperture radar (SAR) images from the Copernicus Sentinel-1A and Sentinel-1B satellites, operated by the European Space Agency (ESA). The images were taken before (Mar. 25, 2017) and after (Sept. 21, 2017) the landfall of the storm.", - "description_simplified": "The ARIA (Advanced Rapid Imaging and Analysis) DPM (Damage Proxy Map) Puerto Rico dataset shows areas of Eastern Puerto Rico that are likely to be damaged (shown by red and yellow pixels) as a result of Hurricane Maria. ", - "indicators": "Disasters", - "intended_use": "Path B", - "latency": "", - "limitations": "Data only available for case study of Puerto Rico (2017)", - "project": "Disaster Dashboard", - "source_link": "https://appliedsciences.nasa.gov/our-impact/news/aria-damage-proxy-map-puerto-rico-after-hurricane-maria https://ghis.maps.arcgis.com/home/item.html?id=1ce3ccaacc6c4cd7b3b6cef4ea4980aa ", - "strengths": "Visualization available through ArcGIS viewer", - "format": "kml, raster data", - "geographic_coverage": "Puerto Rico", - "data_visualization": "ArcGIS viewer", - "spatial_resolution": "30 m", - "temporal_extent": 2017, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 77, - "fields": { - "dataset": "Poverty Mapping Project: Small Area Estimates of Poverty and Inequality", - "description": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality data set consists of consumption-based poverty, inequality and related measures for subnational administrative units in approximately twenty countries throughout Africa, Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and survey data using small area estimates techniques. The collection of data have been compiled, integrated and standardized from the original data providers into a unified spatially referenced and globally consistent data set. The data products include shapefiles (vector data), tabular data sets (csv format), and centroids (csv file with latitude and longitude of a geographic unit and associated poverty estimates). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with a number of external data providers.", - "description_simplified": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality dataset shows poverty, inequality, and other related data for approximately twenty ountries throughout Afirca, Asia, Europe, North America, and South America. These data may be useful in poverty and inequity research.", - "indicators": "Human Dimensions", - "intended_use": "Path A", - "latency": "", - "limitations": "Limited geographic coverage", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/H49P2ZKM", - "strengths": "Country-level data that has been integrated and compiled, integrated and standardized into a unified spatially referenced and globally consistent data set.", - "format": "CSV, PDF, PNG", - "geographic_coverage": "Albania; Bangladesh; Bolivia; Bulgaria; Cambodia; China; Dominican Republic; Ecuador; Gaza Strip; Guatemala; Honduras; Indonesia; Kenya; Madagascar; Malawi; Morocco; Mozambique; Nepal; Nicaragua; Panama; Papua New Guinea; Paraguay; Philippines; Uganda; Vietnam; West Bank", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1990-01-01 to 2002-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 78, - "fields": { - "dataset": "Gridded Population of the World (GPW), v4", - "description": "The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010.", - "description_simplified": "The Gridded Population of the World dataset includes basic demographic information such as age, sex, and population density for the year 2010. ", - "indicators": "Human Dimensions", - "intended_use": "Path A", - "latency": "", - "limitations": "Single time slicce (year) available", - "project": "Disaster Dashboard", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/gpw-v4-basic-demographic-characteristics-rev11", - "strengths": "Several data formats available", - "format": "GeoTIFF, ASCII, and netCDF-4 formats", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "", - "temporal_extent": 2010, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 79, - "fields": { - "dataset": "Global High Resolution Daily Extreme Urban Heat Exposure", - "description": "The Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily), 1983-2016 data set contains a high-resolution, longitudinal global record of geolocated urban extreme heat events and urban population exposure estimates for more than 10,000 urban settlements worldwide for 1983-2016. Urban extreme heat events and urban population exposure are identified for each urban settlement in the data record for five combined temperature-humidity thresholds: two-day or longer periods where the daily maximum Heat Index (HImax) > 40.6 \u00b0C; one-day or longer periods where HImax > 46.1 \u00b0C; and one day or longer periods where the daily maximum Wet Bulb Globe Temperature (WBGTmax) > 28 \u00b0C, 30 \u00b0C, and 32 \u00b0C. The WBGTmax thresholds follow the International Standards Organization (ISO) criteria for risk of occupational heat related heat illness, whereas the HImax thresholds follow the U.S. National Weather Services' definition for an excessive heat warning. For each criteria, across urban settlements worldwide, the data set also contains the duration, intensity, and severity of each urban extreme heat event.", - "description_simplified": "The Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily) dataset contains a record of urban extreme heat events and exposure estimates for over 10,000 urban settlements worldwide for the years 1983-2016.", - "indicators": "Extreme Heat", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "SDEI - Satellite-Derived Environmental Indicators", - "source_link": "https://doi.org/10.7927/fq7g-ny13", - "strengths": "33-year temporal extent", - "format": "Shapefile, CSV, JSON", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "", - "temporal_extent": "1983-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 80, - "fields": { - "dataset": "Global Estimated Net Migration Grids By Decade", - "description": "The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of globally consistent data on migration, indirect estimation methods were used. The authors relied on a combination of data on spatial population distribution for four time slices (1970, 1980, 1990, and 2000) and subnational rates of natural increase in order to derive estimates of net migration on a 30 arc-second (~1km) grid cell basis. Net migration was estimated by subtracting the population in time period 2 from the population in time period 1, and then subtracting the natural increase (births minus deaths). The residual was considered to be net migration (in-migrants minus out-migrants). The authors ran 13 geospatial net migration estimation models based on outputs from the same number of imputation runs for urban and rural rates of natural increase.This data set represents the average of those runs. These data are reliable at broad scales of analysis (e.g. ecosystems or regions), but are generally not reliable for local level analyses. The data were produced for the United Kingdom Foresight project on Migration and Global Environmental Change.", - "description_simplified": "The Global Estimated Net Migration Grids By Decade provides estimates of overall net migration (in-migration minus out-migration) per decade for the 1970s, 1980s, and 1990s. ", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "POPDYNAMICS - Population Dynamics", - "source_link": "https://doi.org/10.7927/H4319SVC", - "strengths": "30 year temporal extent", - "format": "GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.008333 Decimal Degrees", - "temporal_extent": "1970-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 81, - "fields": { - "dataset": "IPCC (Intergovernmental Panel on Climate Change) INFORM Global Risk Index 2019 Mid Year, v0.3.7, (2019)", - "description": "The INFORM Global Risk Index 2019 Mid Year, v0.3.7 data set identifies the countries at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM Global Risk Index (GRI) model is based on risk concepts published in the scientific literature and envisages three dimensions of risk: Hazard & Exposure, Vulnerability, and Lack of Coping Capacity. The INFORM GRI model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk. The INFORM GRI model supports a proactive crisis management framework, and will be helpful for an objective allocation of resources for disaster management, as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies. Only the two main sections, Vulnerability and Lack of Coping Capacity, not the Hazard & Exposure section, were used in the IPCC AR6.", - "description_simplified": "The IPCC (Intergovernmental Panel on Climate Change) INFORM Global Risk Index allows the user to assess country-level vulnerability and coping capacity related to climate change. This idex is based on Chapter 8 of the Sixth Assessment Report (AR6) by the IPCC. This dataset identifies the country high at risk of humanitarian crisis that are more likely to need assistance.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Data only available excel format for 2019", - "project": "IPCC - Intergovernmental Panel on Climate Change", - "source_link": "https://doi.org/10.7927/yzp7-sm30", - "strengths": "Visualization available through SEDAC map widget", - "format": "Excel", - "geographic_coverage": "Afghanistan; Albania; Algeria; Angola; Antigua and Barbuda; Argentina; Armenia; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Central African Republic; Chad; Chile; China; Colombia; Comoros; Congo; Congo, Democratic; Costa Rica; Cote d'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Eswatini; Ethiopia; Fiji; Finland; France; Gabon; Gambia; Georgia; Germany; Ghana; Global; Greece; Grenada; Guatemala; Guinea; Guinea-Bissau; Guyana; Haiti; Honduras; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Israel; Italy; Jamaica; Japan; Jordan; Kazakhstan; Kenya; Kiribati; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Mauritania; Mauritius; Mexico; Micronesia; Moldova; Mongolia; Montenegro; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; New Zealand; Nicaragua; Niger; Nigeria; North Korea; Norway; Oman; Pakistan; Palau; Palestine; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Poland; Portugal; Qatar; Reunion; Romania; Russia; Rwanda; Samoa; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Korea; South Sudan; Spain; Sri Lanka; St Kitts and Nevis; St Lucia; St Vincent and The Grenadines; Sudan; Suriname; Sweden; Switzerland; Syria; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States of America; Uruguay; Uzbekistan; Vanuatu; Venezuela; Vietnam; Yemen; Zambia; Zimbabwe", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "2019-01-01 to 2019-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 82, - "fields": { - "dataset": "Urban-Rural Population and Land Area Estimates, v3 (1990, 2000, 2015)", - "description": "The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper \"Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter\" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR).", - "description_simplified": "The Urban-Rural Population and Land Area Estimates dataset provides estimates of urban and rural populations and land areas for the years 1990, 2000 and 2015 for 234 countries and other areas with coastal elevations of no more than 5m above sea level. These data may be useful in tracking urbanareas at risk of coastal hazards.", - "indicators": "Urban Flooding", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "LECZ - Low Elevation Coastal Zone", - "source_link": "https://doi.org/10.7927/d1x1-d702", - "strengths": "Includes data for 234 countries", - "format": "GeoTIFF, Excel, PDF, PNG", - "geographic_coverage": "Afghanistan; Aland Islands; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antigua and Barbuda; Argentina; Armenia; Aruba; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bonaire; Bosnia and Herzegovina; Botswana; Bouvet Island; Brazil; British Indian Ocean Territory; British Virgin Islands; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote d'Ivoire; Croatia; Cuba; Curacao; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Faeroe Islands; Falkland Islands; Fiji; Finland; France; French Guiana; French Polynesia; French Southern Territories; Gabon; Gambia; Georgia; Germany; Ghana; Gibraltar; Global; Greece; Greenland; Grenada; Guadeloupe; Guam; Guatemala; Guernsey; Guinea; Guinea-Bissau; Guyana; Haiti; Heard Island and McDonald Islands; Holy See; Honduras; Hong Kong; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Isle of Man; Israel; Italy; Jamaica; Japan; Jersey; Jordan; Kazakhstan; Kenya; Kiribati; Kosovo; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macau; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Martinique; Mauritania; Mauritius; Mayotte; Mexico; Micronesia; Moldova; Monaco; Mongolia; Montenegro; Montserrat; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; Norfolk Island; North Korea; Northern Mariana Islands; Norway; Oman; Pakistan; Palau; Palestine; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Pitcairn Islands; Poland; Portugal; Puerto Rico; Qatar; Reunion; Romania; Russia; Rwanda; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Georgia and the South Sandwich Islands; South Korea; South Sudan; Spain; Spratly Islands; Sri Lanka; St Barthelemy; St Helena; St Kitts and Nevis; St Lucia; St Maarten; St Martin; St Pierre and Miquelon; St Vincent and The Grenadines; Sudan; Suriname; Svalbard and Jan Mayen; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Taiwan; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Tokelau; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Turks and Caicos Islands; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States of America; Uruguay; US Virgin Islands; Uzbekistan; Vanuatu; Venezuela; Vietnam; Wallis and Futuna Islands; Western Sahara; Yemen; Zambia; Zimbabwe", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "0.0083 Decimal Degrees", - "temporal_extent": "1990-01-01; 2000-01-01; 2015-01-01", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 83, - "fields": { - "dataset": "Natural Resource Protection and Child Health Indicators, 2021 Release (2010\u200a\u2013\u200a2021)", - "description": "To assist in the country selection process for the Millennium Challenge Corporation (MCC) by providing indicators of natural resource protection and child health that complement the governance, social, and economic indicators used by MCC as country selection criteria.", - "description_simplified": "The Natural Resource Protection and Child Health Indicators dataset provides indicators of natural resource protection and child health that complement the governance, social, and economic indicators used by MCC (Millenium Challenge Corporation) as country selection criteria.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Only available in excel format", - "project": "Other", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/nrmi-natural-resource-protection-child-health-indicators-2021/data-download", - "strengths": "Includes recent data", - "format": "xlsx", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "2010-2021", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 84, - "fields": { - "dataset": "ECCO (Estimating the Circulation and Climate of the Ocean) Global Mean Sea Level - Monthly Mean (Version 4 Release 4)", - "description": "This dataset provides monthly-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense.", - "description_simplified": "The ECCO (Estimating the Circulation and Climate of the Ocean) Global Mean Sea Level - Monthly Mean dataset provides monthly-averaged global mean sea level from the ECCO (Estimating the Circulation and Climate of the Ocean) Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. The dataset reconstructs average sea level for urban flooding and coastal vulnerability research.", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "", - "limitations": "lacks recent data", - "project": "", - "source_link": "https://podaac.jpl.nasa.gov/dataset/ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4", - "strengths": "20-year temporal extent", - "format": "netCDF-4", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "0.5x0.5 degrees", - "temporal_extent": "1992-Jan-01 to 2018-Jan-01", - "temporal_resolution": "Monthly - < Annual" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 85, - "fields": { - "dataset": "ECCO Global Mean Sea Level - Daily Mean (Version 4 Release 4)", - "description": "This dataset provides daily-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense.", - "description_simplified": "The ECCO (Estimating the Circulation and Climate of the Ocean) Global Mean Sea Level - Daily Mean dataset provides daily-averaged global mean sea level from the ECCO (Estimating the Circulation and Climate of the Ocean) Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. The dataset reconstructs average sea level for urban flooding and coastal vulnerability research.", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "", - "limitations": "", - "project": "Other", - "source_link": "https://podaac.jpl.nasa.gov/dataset/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4", - "strengths": "20-year temporal extent", - "format": "netCDF-4", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "0.5x0.5 degrees", - "temporal_extent": "1992-Jan-01 to 2018-Jan-01", - "temporal_resolution": "Daily- < Weekly" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 86, - "fields": { - "dataset": "Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1", - "description": "This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 4.2 and version 5.x releases are described in detail in the user handbook.", - "description_simplified": "The Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 dataset provides Global Mean Sea Level (GMSL) estimates.", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "4 months", - "limitations": "Low latency", - "project": "Other", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2157848116-PODAAC.html", - "strengths": "recent data available", - "format": "ASCII", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "360 Decimal Degrees x 132 Decimal Degrees", - "temporal_extent": "1992-12-01 ongoing", - "temporal_resolution": "10 days" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 87, - "fields": { - "dataset": "Reconstructed Global Mean Sea Level from GRACE and In Situ 1900 to 2018", - "description": "This dataset contains reconstructed global-mean sea level evolution and the estimated contributing processes over 1900-2018. Reconstructed sea level is based on annual-mean tide-gauge observations and uses the virtual-station method to aggregate the individual observations into a global estimate. The contributing processes consist of thermosteric changes, glacier mass changes, mass changes of the Greenland and Antarctic Ice Sheet, and terrestrial water storage changes. The glacier, ice sheet, and terrestrial water storage are estimated by combining GRACE observations (2003-2018) with long-term estimates from in-situ observations and models. Steric estimates are based on in-situ temperature profiles.", - "description_simplified": "The Reconstructed Global Mean Sea Level from GRACE and In Situ 1900 to 2018 dataset shows the average global sea level change and the processes that caused it over the years 1900-2018. Some of the contributing processes include changes in global temperature, glacier mass changes, ice sheet changes, and changes in water stored on land.", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "", - "limitations": "lacks recent data", - "project": "Other", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C1936665403-PODAAC.html", - "strengths": "18-year temporal extent", - "format": "netCDF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "360 Decimal Degrees x 179 Decimal Degrees", - "temporal_extent": "1900-01-01 to 2018-12-31", - "temporal_resolution": "1 yr" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 88, - "fields": { - "dataset": "Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1- 3 Granules", - "description": "This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied.", - "description_simplified": "The Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters dataset includes globally averaged Sea Surface Height Anomalies (SSHA) from September 1992 to present. Adjustments for bias and glacial differences. 3-granule dataset", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "up to 4 months", - "limitations": "Low latency", - "project": "Other", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2157848116-PODAAC.html", - "strengths": "10-day temporal resolution", - "format": "ASCII", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "360 Decimal Degrees x 132 Decimal Degrees", - "temporal_extent": "1992-12-31 ongoing", - "temporal_resolution": "10 day" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 89, - "fields": { - "dataset": "Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1- 1 Granule", - "description": "This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 4.2 and version 5.x releases are described in detail in the user handbook.", - "description_simplified": "The Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters dataset includes globally averaged Sea Surface Height Anomalies (SSHA) from September 1992 to present. Adjustments for bias and glacial differences. 1-granule dataset", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "up to 4 months", - "limitations": "Low latency", - "project": "Other", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2205556193-POCLOUD.html", - "strengths": "30-year temporal extent", - "format": "ASCII", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "360 Decimal Degrees x 132 Decimal Degrees", - "temporal_extent": "1992-09-01 ongoing", - "temporal_resolution": "Weekly - < Monthly" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 90, - "fields": { - "dataset": "Integrated Multi-Mission Ocean Altimeter Data for Climate Research complete time series Version 5.0- 1 Granule", - "description": "This dataset contains along track Sea Surface Height Anomalies (SSHA) from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 missions geo-referenced to a mean reference orbit. Altimeter data from the multi-mission Geophysical Data Records (GDRs) have been interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. Version 5.0 updates include improved Precise Orbit Determination (POD) with GSFC std2006 standards, and the application of internal tides.", - "description_simplified": "The Integrated Multi-Mission Ocean Altimeter Data for Climate Research dataset includes SSHA adjusted for differences among various satellites. 1-granule dataset", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "quarterly", - "limitations": "low latency", - "project": "Other", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2007584216-PODAAC.html", - "strengths": "30-year temporal extent", - "format": "netCDF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1992-09-25 ongoing", - "temporal_resolution": "10 Day" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 91, - "fields": { - "dataset": "Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.0", - "description": "This dataset contains along track Sea Surface Height Anomalies (SSHA) for individual 10-day cycles from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 missions geo-referenced to a mean reference orbit. Altimeter data from the multi-mission Geophysical Data Records (GDRs) have been interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. Version 5.0 updates include improved Precise Orbit Determination (POD) with GSFC std2006 standards, and the application of internal tides.", - "description_simplified": "The Integrated Multi-Mission Ocean Altimeter Data for Climate Research dataset contains SSHA adjusted for satellite differences and tides.", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "quarterly", - "limitations": "low latency", - "project": "Other", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2007732592-PODAAC.html", - "strengths": "30-year temporal extent", - "format": "netCDF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1992-09-25 ongoing", - "temporal_resolution": "10 Day" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 92, - "fields": { - "dataset": "ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015", - "description": "This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. 1985-01-01 to 2015-12-31\n", - "description_simplified": "The ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015 dataset includes differenced Normalized Burned Ratio (dNBR) data for burn scars from fires within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project in Alaska and Canada 1985-2015. These data may be useful in fire disaster research. ", - "indicators": "Disasters", - "intended_use": "Path C", - "latency": "", - "limitations": "Lacks recent data", - "project": "", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2111787144-ORNL_CLOUD.html", - "strengths": "30-year temporal extent", - "format": "GeoTIFF", - "geographic_coverage": "Alaska and Canada", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1985-01-01 to 2015-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 93, - "fields": { - "dataset": "MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V011", - "description": "The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. 2016-01-01 to 2019-12-31", - "description_simplified": "The MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V011 dataset provides land Surface Phenology (seasonal changes in plant greening, flowering, etc) for North America. These datasets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping.", - "indicators": "Climate", - "intended_use": "Path C", - "latency": "", - "limitations": "Only available for North America", - "project": "", - "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2102664483-LPDAAC_ECS.html", - "strengths": "Semi-recent data available", - "format": "netCDF-4", - "geographic_coverage": "North America", - "data_visualization": "", - "spatial_resolution": "30m", - "temporal_extent": "2016-01-01 to 2019-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 94, - "fields": { - "dataset": "Global Precipitation Measurement Data Directory", - "description": "Precipitation data from the GPM and TRMM missions are made available free to the public in a variety of formats from several sources at NASA Goddard Space Flight Center. This section outlines the different types of data available, the levels of processing, the sources to download the data, and some helpful tips for utilizing precipitation data in your research.", - "description_simplified": "The Global Precipitation Measurement Data Directory includes various datasets related to precipitation and atmospheric monitoring.", - "indicators": "Urban Flooding", - "intended_use": "Path B", - "latency": "", - "limitations": "multiple datasets available", - "project": "", - "source_link": "https://gpm.nasa.gov/data/directory", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "visualization avaible, varies per dataset", - "spatial_resolution": "varies", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 95, - "fields": { - "dataset": "JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height Coastal Resolution Improvement (CRI) Filtered Release 06 Version 02", - "description": "This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (Version2/RL06). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. This version of the data employs a Coastal Resolution Improvement (CRI) filter that reduces signal leakage errors across coastlines. The water storage/height anomalies are given in equivalent water thickness units (cm). .", - "description_simplified": "This dataset includes monthly global water storage/height to be used in studying groundwater availability. These data may aid in freshwater availability and access research.", - "indicators": "Water Availability", - "intended_use": "Path C", - "latency": "", - "limitations": "only netCDF available", - "project": "", - "source_link": "https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2", - "strengths": "cloud-optimized", - "format": "netCDF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "0.5 degrees (Latitude) x 0.5 degrees (Longitude)", - "temporal_extent": "2002-Apr-04 to Present", - "temporal_resolution": "Monthly - < Annual" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 96, - "fields": { - "dataset": "ASTER Surface Kinetic Temperature from Earthdata Search", - "description": "ASTER Surface Temperature products are processed on-demand and so must be requested with additional parameters. Note that there is a limit to 2000 granules per order.", - "description_simplified": "The The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) dataset provides detailed land surface temperature data. These data may aid in urban heat research.", - "indicators": "Extreme Heat", - "intended_use": "Path C", - "latency": "", - "limitations": "", - "project": "Urban Heat", - "source_link": "https://search.earthdata.nasa.gov/search?q=ASTER%20Surface%20Kinetic%20Temperature&fst0=Land%20Surface", - "strengths": "22-year temporal extent", - "format": "HDF-EOS2", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "90 m", - "temporal_extent": "2000-03-04 ongoing", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 97, - "fields": { - "dataset": "Global Urban Heat Island (UHI) Data Set, v1 (2013)", - "description": "The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC?s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC?s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).", - "description_simplified": "SEDAC's Global Urban Heat Island (UHI) Data Set, v1 (2013) dataset provides a global dataset of average summer daytime maximum land surface temperatures and minimum nighttime land surface temperatures. These data may aid in urban heat research.", - "indicators": "Extreme Heat", - "intended_use": "Path A", - "latency": "", - "limitations": "lacks recent data", - "project": "SDEI - Satellite-Derived Environmental Indicators", - "source_link": "https://doi.org/10.7927/H4H70CRF", - "strengths": "Shapefile format for use in GIS", - "format": "Shapefile, Excel, PDF, PNG, WMS", - "geographic_coverage": "Africa; Asia; Australia; Europe; Global; North America; Oceanography; South America", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "2013-01-01 to 2013-12-31", - "temporal_resolution": "Unsure" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 98, - "fields": { - "dataset": "Global Development Potential Indices, v1 (2016)", - "description": "The Global Development Potential Indices (DPIs) data set contains thirteen sector-level DPIs for sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). The DPI for each sector represents land suitability that accounts for both resource potential and development feasibility. Each DPI is a 1-km spatially-explicit, global land suitability map that has been validated using locations of current and planned development, and examined for uncertainty and sensitivity. The DPIs can be used to identify lands with current favorable economic and physical conditions for individual sector expansion and assist in planning for sector and cumulative development across the globe.", - "description_simplified": "The Global Development Potential Indices, v1 (2016) dataset provides Development Potential Indices for sectors related to renewable energy, fossil fuels, mining, and agriculture. Each DPI shows how suitable land is for the potential land use sector. These data may aid in urban sprawl and human impacts research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/k9t6-gh59", - "strengths": "GeoTIFF format for GIS", - "format": "GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "30 Decimal Degrees", - "temporal_extent": "2016-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 99, - "fields": { - "dataset": "Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence, v1 (2005)", - "description": "The West Africa Coastal Vulnerability Mapping: Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence represents the HarvestChoice Subnational Poverty and Extreme Poverty Prevalence data set as a one kilometer raster, and includes values within 200 kilometers of the coast. Poverty levels affect the \"defenselessness\" of populations in the low elevation coastal zone. These data were developed by the Harvest Choice project funded by the Bill and Melinda Gates Foundation. Harvest Choice measured 2005 poverty levels using 2005 purchasing power parity data for two thresholds: $1.25/day and $2/day international poverty lines. The $2/day threshold was selected for this mapping exercise.", - "description_simplified": "The Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence, v1 (2005) dataset shows poverty levels based on Harvest Choice Project using purchasing power data with two thresholds: $1.25/day and $2/day international poverty lines.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "", - "project": "WACVM - West Africa Coastal Vulnerability Mapping", - "source_link": "https://doi.org/10.7927/H44T6G9K", - "strengths": "Lacks recent data, limited to West Africa", - "format": "GeoTIFF", - "geographic_coverage": "West Africa", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "2005-01-01 to 2005-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 100, - "fields": { - "dataset": "Global Subnational Prevalence of Child Malnutrition, v1 (1990\u200a\u2013\u200a2002)", - "description": "The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition data set consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. Data are reported for the most recent year with subnational information available at the time of development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of underweight children under five (the rate numerator), and a tabular data set of the same and associated data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition dataset provides data related to the malnutrition of children globally. Data are reported for the most recent year. These data may aid poverty and food access research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/H4K64G12", - "strengths": "12-year temporal extent", - "format": "ASCII Grid, Excel, Shapefile, PDF, PNG, WMS", - "geographic_coverage": "Afghanistan; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antarctica; Antigua and Barbuda; Argentina; Armenia; Aruba; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; British Indian Ocean Territory; British Virgin Islands; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Christmas Island; Cocos Islands; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Faeroe Islands; Falkland Islands; Fiji; Finland; France; French Guiana; French Polynesia; French Southern Territories; Gabon; Gambia; Georgia; Germany; Ghana; Gibraltar; Global; Greece; Greenland; Grenada; Guadeloupe; Guam; Guatemala; Guernsey; Guinea; Guinea-Bissau; Guyana; Haiti; Heard and McDonald Islands; Honduras; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Isle of Man; Israel; Italy; Jamaica; Japan; Jersey; Jordan; Kazakhstan; Kenya; Kiribati; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Martinique; Mauritania; Mauritius; Mayotte; Mexico; Micronesia; Moldova; Monaco; Mongolia; Montserrat; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; Netherlands Antilles; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; Norfolk Island; North Korea; Northern Mariana Islands; Oman; Pakistan; Palau; Palestine; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Pitcairn Islands; Poland; Portugal; Puerto Rico; Qatar; Reunion; Romania; Russia; Rwanda; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia and Montenegro; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Georgia Island; South Korea; South Sandwich Islands; Spain; Spratly Islands; Sri Lanka; St Helena; St Kitts and Nevis; St Lucia; St Pierre and Miquelon; St Vincent and The Grenadines; Sudan; Suriname; Svalbard and Jan Mayen; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Taiwan; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Tokelau; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Turks and Caicos Islands; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States Minor Outlying Islands; United States of America; Uruguay; US Virgin Islands; Uzbekistan; Vanuatu; Vatican City; Venezuela; Vietnam; Wallis and Futuna Islands; Yemen; Zambia; Zimbabwe", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "1990-01-01 to 2002-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 101, - "fields": { - "dataset": "Global 15 x 15 Minute Grids of the Downscaled GDP Based on the SRES B2 Scenario, v1 (1990, 2025)", - "description": "The Global 15x15 Minute Grids of the Downscaled Population Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of the downscaled population per unit area (population densities). These global grids were generated using the Country-level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. The 1990 GPW was used as the base distribution and the country-level downscaled projections were used to replace population estimates of 1990 in GPW and 2025. The fractional distribution of the population at each grid cell is the same as the 1990 GPW, sub-nationally. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global 15 x 15 Minute Grids of the Downscaled GDP Based on the SRES B2 Scenario, v1 (1990, 2025) dataset provides Gross Domestic Product (GDP) estimates globally as well as projections for smaller scale areas within countries. These data may help estimate poverty and economic growth and success.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "projections based on original publishing date", - "project": "SDP - Socioeconomic Downscaled Projections", - "source_link": "https://doi.org/10.7927/H4HQ3WTH", - "strengths": "global coverage", - "format": "ASCII, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1990, 2025", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 102, - "fields": { - "dataset": "OMI NO2 data from Earthdata Search", - "description": "The OMI, aboard the Aura spacecraft, provides daily gridded and non-gridded products at 13x24 km resolution; data are in HDF5 format and can be opened using Panoply. A tutorial on using OMI NO2 data is available as a PDF and a webinar on Analyzing NO2 data within Java and Excel is available from the Earthdata YouTube website.", - "description_simplified": "The OMI NO2 data from Earthdata Search dataset provides daily nitrogen dioxide data.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?fi=OMI&fst0=Atmosphere&fsm0=Atmospheric%20Chemistry&fs10=Nitrogen%20Compounds&fs20=Nitrogen%20Dioxide", - "strengths": "Multiple datasets available within link", - "format": "Varies per dataset", - "geographic_coverage": "Global", - "data_visualization": "Panopoly", - "spatial_resolution": "varies- multiple datasets included", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies- multiple datasets included" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 103, - "fields": { - "dataset": "TROPOMI NO2 data from Earthdata Search", - "description": "The TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel 5, is an ESA Mission. ESA's TROPOMI NO2 provides additional information on this level 2 data product. It is important to note that, because of the very small numbers in tropospheric vertical column of NO2, you will need to change the scaling factor in Panoply (see image from June 2018 to right). Data are in NetCDF format, and can be opened using Panoply.", - "description_simplified": "The TROPOMI NO2 data from Earthdata Search dataset provides nitrogen dioxide data. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?fi=TROPOMI&fst0=Atmosphere&fsm0=Atmospheric%20Chemistry&fs10=Nitrogen%20Compounds", - "strengths": "Multiple datasets available within link", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets included", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies- multiple datasets included" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 104, - "fields": { - "dataset": "OMI NO2 data in Worldview", - "description": "", - "description_simplified": "The OMI NO2 data in Worldview dataset includes NRT (Near Real Time) nitrogen dioxide data with visualizations in Worldview.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only recent data included", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?l=OMI_Nitrogen_Dioxide_Tropo_Column,Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&sh=OMI_Nitrogen_Dioxide_Tropo_Column,C1239966842-GES_DISC&t=2022-04-10-T19%3A18%3A13Z", - "strengths": "NRT", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "375m x 375 m", - "temporal_extent": "2021-12-15 ongoing", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 105, - "fields": { - "dataset": "Trends over time: Ground level NO2 in Worldview", - "description": "", - "description_simplified": "The Trends over time: Ground level NO2 in Worldview dataset shows ground level nitrogen dioxide over time. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data, large gap in dates, only 4 years of data available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?l=Ground_Level_Nitrogen_Dioxide_3_Year_Running_Mean_2010-2012,Ground_Level_Nitrogen_Dioxide_3_Year_Running_Mean_1996-1998,Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2022-04-10-T19%3A21%3A19Z", - "strengths": "", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "0.1 x 0.1", - "temporal_extent": "1996-1998 and 2010-2012", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 106, - "fields": { - "dataset": "Global Nitrogen Dioxide Monitoring", - "description": "NO2 column observations from the Dutch-Finnish Ozone Monitoring Instrument (OMI) and ESA/EU Copernicus Sentinel 5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) are available from October, 2004 and April, 2018, respectively. OMI is a UV-Visible wavelength spectrometer on the polar-orbiting NASA Aura satellite. Aura, launched on 15 July 2004, follows a sun-synchronous orbit with an equator crossing time near 13:45, local time. TROPOMI is a similar instrument with enhanced spatial resolution and additional spectral coverage. It was launched on board the European Space Agency/European Union (ESA/EU) Copernicus Sentinel-5 Precursor satellite on October 13, 2017.", - "description_simplified": "The Global Nitrogen Dioxide Monitoring dataset provides imagery of daily nitrogen dioxide from OMI. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "no data download", - "project": "", - "source_link": "https://so2.gsfc.nasa.gov/no2/no2_index.html", - "strengths": "images useful for Path A users", - "format": "images", - "geographic_coverage": "Global", - "data_visualization": "Map Viewer", - "spatial_resolution": "0.1 x 0.1", - "temporal_extent": "2015-2019, 2020", - "temporal_resolution": "approx 7 days" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 107, - "fields": { - "dataset": "OMI O3 data from Earthdata Search", - "description": "OMI provides daily total column data; data are in HDF5 format, and can be opened using Panoply.", - "description_simplified": "The OMI O3 data from Earthdata Search provides daily total air column data for ozone. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?m=-0.0703125!0.140625!2!1!0!0%2C2&q=OMTO3&ok=OMTO3&fi=OMI&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Tropospheric%20Ozone&fst1=Atmosphere&fst2=Atmosphere", - "strengths": "Multiple datasets available within link", - "format": "HDF5", - "geographic_coverage": "Global", - "data_visualization": "Giovanni, Worldview, Panopoly", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies- multiple datasets available", - "temporal_resolution": "varies- multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 108, - "fields": { - "dataset": "AIRS O3 data from Earthdata Search", - "description": "AIRS measures abundances of trace components in the atmosphere including ozone. Data are available daily (AIRS3STD), over 8 days (AIRS3ST8), or monthly (AIRS3STM). The instrument measures the amount of O3 in the total vertical column profile of the atmosphere (from Earth\u2019s surface to top-of-atmosphere). Data are in HDF format, and can be opened using Panoply.", - "description_simplified": "The AIRS dataset measures abundances of trace components in the atmosphere including ozone. Data are available daily (AIRS3STD), over 8 days (AIRS3ST8), or monthly (AIRS3STM). The instrument measures the amount of O3 in the total vertical column profile of the atmosphere (from Earth\u2019s surface to top-of-atmosphere).", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?m=-97.681640625!-20.25439453125!0!1!0!0%2C2&q=AIRS3ST&ok=AIRS3ST&fi=AIRS&fst0=Atmosphere&fs10=Tropospheric%20Ozone&fst1=Atmosphere&fst2=Atmosphere", - "strengths": "Multiple datasets available within link", - "format": "HDF", - "geographic_coverage": "Global", - "data_visualization": "Giovanni, Panopoly", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies- multiple datasets available", - "temporal_resolution": "varies- multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 109, - "fields": { - "dataset": "TROPOMI O3 data from Earthdata Search", - "description": "ESA TROPOMI O3 provides additional information on this level 2 data product. Data are in NetCDF format, and can be opened using Panoply.", - "description_simplified": "The TROPOMI O3 data from Earthdata Search dataset provides ozone data. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?fi=TROPOMI&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Tropospheric%20Ozone", - "strengths": "Multiple datasets available within link", - "format": "NetCDF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies- multiple datasets available", - "temporal_resolution": "varies- multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 110, - "fields": { - "dataset": "VIIRS Thermal Anomalies (Day/Night)", - "description": "The VIIRS 375m I-band fire detections complements the MODIS fire detections; they both show good agreement in hotspot detection but the improved spatial resolution of the 375m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.The thermal anomalies are represented as red points (approximate center of a 375 m pixel).", - "description_simplified": "The VIIRS 375m I-band fire detections complements the MODIS fire detections; they both show good agreement in hotspot detection but the improved spatial resolution of the 375m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.The thermal anomalies are represented as red points (approximate center of a 375 m pixel).", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "only netcdf4 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?v=-140.65780521294596,-48.95980316248003,87.56620002935385,57.925105959330395&l=Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_NOAA20_Thermal_Anomalies_375m_Night(hidden),VIIRS_NOAA20_Thermal_Anomalies_375m_Day(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day,VIIRS_SNPP_Thermal_Anomalies_375m_Night,VIIRS_NOAA20_CorrectedReflectance_TrueColor(hidden),VIIRS_SNPP_CorrectedReflectance_TrueColor,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor(hidden)&lg=false&t=2022-03-04-T19%3A37%3A56Z", - "strengths": "daily temporal resolution", - "format": "NetCDF4", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "250 m", - "temporal_extent": "varies", - "temporal_resolution": "Daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 111, - "fields": { - "dataset": "MLS (Aura) Ozone", - "description": "The MLS Ozone (O3) Mixing Ratio 46hPa (hectopascals) layer is derived from the MLS Ozone product (ML2O3_NRT) available from the Microwave Limb Sounder (MLS) instrument on the Aura satellite. The product indicates ozone levels at the vertical atmospheric pressure level of 46hPa, and is measured in parts per billion by volume (ppbv).The sensor resolution is 5 km, imagery resolution is 2 km, and the temporal resolution is twice daily (day and night). MLS/Aura NRT L2 O3 Mixing Ratio", - "description_simplified": "The MLS (Aura) Ozone dataset indicates ozone levels in the atmosphere. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only hdf-eos5 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,MLS_O3_46hPa_Night,MLS_O3_46hPa_Day,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&v=-92.7421875,-49.47803771636507,66.5859375,55.94678771636507", - "strengths": "15 min temporal resolution", - "format": "HDF-EOS5", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "165 km x 3 km", - "temporal_extent": "2013 May 09 to present", - "temporal_resolution": "15 mins" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 112, - "fields": { - "dataset": "OMPS (Suomi NPP) Ozone", - "description": "The OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiler Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi NPP satellite. The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions. Additionally, this data product contains measurements of UV aerosol index and reflectivity at 331 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir.", - "description_simplified": "The OMPS (Suomi NPP) Ozone dataset provides total ozone measurements. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "Download unavailable", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,OMPS_Ozone_Total_Column,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&v=-92.7421875,-49.47803771636507,66.5859375,55.94678771636507", - "strengths": "NRT", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "", - "temporal_extent": "2012 Jan 06 to present", - "temporal_resolution": "2011-10-28 ongoing" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 113, - "fields": { - "dataset": "Reservoirs, v1.01 (2011)", - "description": "Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The reservoirs were delineated from high spatial resolution satellite imagery and are available as polygon shape files. The only attribute for the reservoirs is the area of the reservoir. The associated dams data set includes multiple attributes such as name of the dam and the impounded river, primary use, nearest city, area, and year of construction (or commissioning). While the main focus was to include all reservoirs with a storage capacity of more than 0.1 cubic kilometers, many smaller reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.", - "description_simplified": "The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) dataset contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. This dataset provides the area of each reservoir. gery and are available as polygon shape files. The only attribute for the reservoirs is the area of the reservoir and some related data such as reservoir names, primary use, nearest city, and year of construction. These data may aid in water availability research.", - "indicators": "Water Availability", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "GRAND - Global Reservoir and Dams", - "source_link": "https://doi.org/10.7927/H4HH6H08", - "strengths": "Global coverage and shapefile format for GIS", - "format": "Shapefile, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "2011-01-01 to 2011-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 114, - "fields": { - "dataset": "Dams, v1.01 (2011)", - "description": "The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.", - "description_simplified": "The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) dataset contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. Information available for each dam include the name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). These data may aid in water availability research.", - "indicators": "Water Availability", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "GRAND - Global Reservoir and Dams", - "source_link": "https://doi.org/10.7927/H4N877QK", - "strengths": "Shapefile format for use in GIS", - "format": "Shapefile, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "N/a", - "spatial_resolution": "", - "temporal_extent": "2011-01-01 to 2011-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 115, - "fields": { - "dataset": "Air Quality Data for Health-Related Applications", - "description": "The purpose of this data collection is to provide air quality data for health-related research and applications. Currently this collection consists of the Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000\u200a\u2013\u200a2016) and Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000\u200a\u2013\u200a2016) data sets. A similar NO2 data set is forthcoming.", - "description_simplified": "The Air Quality Data for Health-Related Applications data collection contains two datasets: Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States and Daily and Annual PM2.5 Concentrations for the Contiguous United States. Both datasets provide gridded air quality data useful in health-related research and applications.", - "indicators": "Disasters", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "Other", - "source_link": "https://sedac.ciesin.columbia.edu/data/collection/aqdh/sets/browse", - "strengths": "16 year temporal extent", - "format": "GeoTIFF, RDS,", - "geographic_coverage": "United States", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "1 km", - "temporal_extent": "2000-2016", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 116, - "fields": { - "dataset": "AirNow Air Quality Dashboard", - "description": "Interactive dashboard provided by AirNow, a partnering organization. Current and archival data available for dashboard. ", - "description_simplified": "The AirNow Air Quality Dashboard is an interactive dashboard provided by Airnow, a partnering organization. Current and past air quality data are available. Data may not be available for download.", - "indicators": "Health & Air Quality", - "intended_use": "Path A", - "latency": "", - "limitations": "Data format and download unclear", - "project": "Other", - "source_link": "https://gispub.epa.gov/airnow/index.html?tab=3", - "strengths": "Current and past data available", - "format": "", - "geographic_coverage": "North America", - "data_visualization": "Dashboard visualization", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 117, - "fields": { - "dataset": "AirNow International Air Quality Dashboard", - "description": "International version of AirNow dashboard for air quality visualization.", - "description_simplified": "The International AirNow Air Quality Dashboard is an interactive dashboard provided by Airnow, a partnering organization. Current and past air quality data are available. Data may not be available for download.", - "indicators": "Health & Air Quality", - "intended_use": "Path A", - "latency": "", - "limitations": "Data format and download unclear", - "project": "Other", - "source_link": "https://www.airnow.gov/index.cfm?action=airnow.international", - "strengths": "Current data available", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Dashboard visualization", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 118, - "fields": { - "dataset": "SEDAC Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), v4.03 (1998\u200a\u2013\u200a2019)", - "description": "The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03 consists of annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4. The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies. Gridded data sets are provided at a resolution of 0.01 degrees to allow users to agglomerate data as best meets their particular needs. Data sets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. The data are distributed as GeoTIFF files and are in WGS84 projection.", - "description_simplified": "The SEDAC Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) dataset provides air quality data for ground-level fine particulate matter that is 2.5 micrometers or smaller (PM2.5) for environmental health research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Data only available in GeoTIFF", - "project": "SDEI - Satellite-Derived Environmental Indicators", - "source_link": "https://doi.org/10.7927/fx80-4n39", - "strengths": "11-year temporal extent", - "format": "GeoTIFF, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "1.0 Decimal Degree", - "temporal_extent": "1998-01-01 to 2019-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 119, - "fields": { - "dataset": "Annual PM2.5 Concentrations for Countries and Urban Areas, v1 (1998\u200a\u2013\u200a2016)", - "description": "The Annual PM2.5 Concentrations for Countries and Urban Areas, 1998-2016, consists of mean concentrations of particulate matter (PM2.5) for countries and urban areas. The PM2.5 data are from the Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. The urban areas are from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02, and its time series runs from 1998 to 2016. The country averages are population-weighted such that concentrations in populated areas count more toward the country average than concentrations in less populated areas, and its time series runs from 2008 to 2015.", - "description_simplified": "The Annual PM2.5 Concentrations for Countries and Urban Areas dataset provides air quality data for mineral dust and sea-salt filtered fine particulate matter of 2.5 micrometers or smaller (PM2.5) in countries and urban areas for environmental health research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Data only available in GeoTIFF", - "project": "SDEI - Satellite-Derived Environmental Indicators", - "source_link": "https://doi.org/10.7927/rja8-8h89", - "strengths": "11-year temporal extent", - "format": "Shapefile, Excel", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "0.1 Decimal Degrees", - "temporal_extent": "1998-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 120, - "fields": { - "dataset": "Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000\u200a\u2013\u200a2016)", - "description": "The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set includes predictions of PM2.5 concentrations in grid cells at a resolution of 1 km for the years 2000 to 2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, as well as other predictors. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions.", - "description_simplified": "The Daily and Annual PM2.5 Concentrations for the Contiguous United States dataset provides predictions of PM2.5 concentrations for the years 2000 to 2016. These data are useful in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "", - "project": "AQDH - Air Quality Data for Health-Related Applications", - "source_link": "https://doi.org/10.7927/0rvr-4538", - "strengths": "16-year temporal extent", - "format": "GeoTIFF, Rdata, Shapefile", - "geographic_coverage": "United States of America", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "0.00833 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 121, - "fields": { - "dataset": "Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000\u200a\u2013\u200a2016)", - "description": "The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set contains estimates of ozone concentrations at a high resolution in space (1 km x 1 km grid cells) and time (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages.", - "description_simplified": "The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States dataset provides ground-level Ozone (O3) concentration data in the United States. These data may be useful in public health research to respectively estimate short and long-term effects on human health and related research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "AQDH - Air Quality Data for Health-Related Applications", - "source_link": "https://doi.org/10.7927/a4mb-4t86", - "strengths": "16-year temporal extent", - "format": "GeoTIFF, RData", - "geographic_coverage": "United States of America", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "0.00833 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2016-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 122, - "fields": { - "dataset": "Global Pesticide Grids (PEST-CHEMGRIDS), v1.01 (2015, 2020, 2025)", - "description": "The Global Pesticide Grids (PEST-CHEMGRIDS), Version 1.01 data set contains 20 of the most-used pesticide active ingredients on 6 dominant crops and 4 aggregated crop classes at 5 arc-minute resolution (about 10 km at the equator), estimated in year 2015, and then projected to 2020 and 2025. To estimate the global application rates of specific active ingredients, spatial statistical methods were used to re-analyze the U.S. Geological Survey Pesticide National Synthesis Project (USGS/PNSP) and the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) pesticide databases, along with other public inventories including globally gridded data of soil physical properties, hydro-climatic variables, agricultural quantities, and socioeconomic indices. The application rate (APR) of each active ingredient on each crop is in kilogram per hectare per year (kg/ha-year), and the harvest area of each crop is in hectare (ha). The data set also includes 200 data quality index maps corresponding to each active ingredient on each crop, as well as maps of the 10 dominant crops and 4 aggregated crop classes. Version 1.01 includes data in GeoTIFF and netCDF formats.", - "description_simplified": "The Global Pesticide Grids (PEST-CHEMGRIDS) dataset provides commonly-used pesticides crucial to assess humanand ecosystem exposure to potential toxicants for environmental modeling, assessment of agricultural chemical contamination and risk analysis, and other related research for the years 2015, 2020, and 2025.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "No data prior to 2015", - "project": "FERMAN - Global Agricultural Inputs", - "source_link": "https://doi.org/10.7927/weq9-pv30", - "strengths": "Includes past, recent, and projections", - "format": "GeoTIFF, netCDF-4, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "5 Arc-Minutes", - "temporal_extent": "2015-0101, 2020-01-01, 2025-01-01", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 123, - "fields": { - "dataset": "MODIS/Terra AOD from Earthdata Search", - "description": "Terrestrial AOD data (3km resolution, merged algorithm)", - "description_simplified": "The MODIS/Terra AOD from Earthdata Search dataset provides data related to the aerosols in the air that may be a result of air pollution and may contribute to human health issues. This dataset includes data over land.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=MOD04_3K&ok=MOD04_3K", - "strengths": "multiple datasets available", - "format": "HDF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets included", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies- multiple datasets included" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 124, - "fields": { - "dataset": "MODIS Terra/Aqua-MAIAC Retrieval AOD from Earthdata Search", - "description": "Multi-angle Implementation of Atmospheric Correction (MAIAC) Land AOD utilizes a new advanced algorithm which uses time series (TMS) analysis and a combination of pixel- and image-based processing to improve the accuracy of cloud detection, aerosol retrievals and atmospheric correction.", - "description_simplified": "The MODIS Terra/Aqua-MAIAC Retrieval AOD from Earthdata Search dataset provides data related to the aerosols in the air that may be a result of air pollution and may contribute to human and ecosystem health issues. This dataset provides data for atmosphere over the land and ocean.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "two datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=MAIAC&fi=MODIS&fst0=Atmosphere", - "strengths": "two datasets available within link", - "format": "HDF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets included", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "varies- multiple datasets included" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 125, - "fields": { - "dataset": "VIIRS AOD at 1 degree x 1 degree from Earthdata Search", - "description": "(daily global data coverage)", - "description_simplified": "The VIIRS AOD at 1 degree x 1 degree from Earthdata Search dataset provides daily global coverage aerosol data.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "two datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=AERDB_D3&ok=AERDB_D3", - "strengths": "two datasets available within link", - "format": "NetCDF4", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "1 degree x 1 degree", - "temporal_extent": "2012 ongoing", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 126, - "fields": { - "dataset": "VIIRS AOD at 6km from Earthdata Search", - "description": "(daily)", - "description_simplified": "The VIIRS AOD at 6km from Earthdata Search dataset provides daily global coverage aerosol data at a higher resolution.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "two datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=AERDB_L2", - "strengths": "two datasets available within link", - "format": "NetCDF4", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "6km", - "temporal_extent": "varies- multiple datasets included", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 127, - "fields": { - "dataset": "Monthly VIIRS AOD at 1 degree x 1 degree from Earthdata Search", - "description": "", - "description_simplified": "The Monthly VIIRS AOD at 1 degree x 1 degree from Earthdata Search provides monthly global coverage aerosol data.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "two datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=AERDB_M3&ok=AERDB_M3", - "strengths": "two datasets available within link", - "format": "NetCDF4", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "1 degree x 1 degree", - "temporal_extent": "2012 ongoing", - "temporal_resolution": "monthly" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 128, - "fields": { - "dataset": "OMI AOD in Giovanni", - "description": "The Ozone Monitoring Instrument (OMI) on Aura has a coarser spatial resolution than MODIS and VIIRS but provides data at individual wavelengths from the ultraviolet (UV) to the visible. Within Giovanni, you can plot daily data at these individual wavelengths. This is important because pollutants have different spectral signatures; for example, a wavelength range around 400 nm can be used to detect elevated layers of absorbing aerosols such as biomass burning and desert dust plumes.", - "description_simplified": "The Ozone Monitoring Instrument (OMI) on Aura has a coarser spatial resolution than MODIS and VIIRS but provides data at individual wavelengths from the ultraviolet (UV) to the visible. Within Giovanni, you can plot daily data at these individual wavelengths. This is important because pollutants have different spectral signatures; for example, a wavelength range around 400 nm can be used to detect elevated layers of absorbing aerosols such as biomass burning and desert dust plumes.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "3 days", - "limitations": "multiple datasets available within link", - "project": "", - "source_link": "https://giovanni.gsfc.nasa.gov/giovanni/#service=TmAvMp&starttime=&endtime=&variableFacets=dataFieldMeasurement%3AAerosol%20Optical%20Depth%3BdataProductPlatformInstrument%3AOMI%3B", - "strengths": "multiple datasets available within link", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Giovanni", - "spatial_resolution": "0.25/1 degree", - "temporal_extent": "10/2004- present", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 129, - "fields": { - "dataset": "AERONET Ground-based AOD Measurements", - "description": "", - "description_simplified": "The AERONET Data Display Interface allows users to find and download ground-based AOD (Aerosol Optical Depth, quantity of light removed from a beam by scattering or absorbing during its path through a medium and is a unitless measure) data for locations all across the world. Data are available from years 1993-2022, dependent on location.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "NRT", - "limitations": "", - "project": "", - "source_link": "https://aeronet.gsfc.nasa.gov/cgi-bin/draw_map_display_aod_v3", - "strengths": "NRT, 19-year temporal extent", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Map Viewer", - "spatial_resolution": "", - "temporal_extent": "1993-2022", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 130, - "fields": { - "dataset": "PM<2.5 micrometers in Worldview", - "description": "The Particulate Matter < 2.5\u00b5m, 2010-2012 layer is part of the Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR product, 1998-2016 consist of annual concentrations (micrograms per cubic meter) of ground-level fine particulate matter (PM2.5), with dust and sea-salt removed. This data set combines AOD retrievals from multiple satellite instruments including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration.", - "description_simplified": "The PM<2.5 micrometers in Worldview dataset includes annual concentrations of ground-level fine particulate matter with dust and sea-salt removed. For trends in PM2.5, Worldview utilizes both ground-based and remote sensing data. Data are available from 2001-2012. \n", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?l=Particulate_Matter_Below_2.5micrometers_2010-2012,Particulate_Matter_Below_2.5micrometers_2001-2010,Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor&lg=false&t=2022-04-10-T18%3A51%3A24Z", - "strengths": "Multiple datasets available within link", - "format": "NetCDF4", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "0.01 degrees", - "temporal_extent": "1998-2012", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 131, - "fields": { - "dataset": "Worldbank mean exposure to PM2.5 across the globe", - "description": "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)", - "description_simplified": "The Worldbank mean exposure to PM2.5 across the globe dataset provides mean annual exposure (micrograms per cubic meter) to PM2.5 air pollution.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "lacks recent data", - "project": "", - "source_link": "https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3?view=chart", - "strengths": "csv, xml, and excel formats available", - "format": "csv, xml, excel", - "geographic_coverage": "Global", - "data_visualization": "Graphing feature", - "spatial_resolution": "", - "temporal_extent": 2017, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 132, - "fields": { - "dataset": "OMI NO2 data in Giovanni", - "description": "Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type; for more information on choosing a type of plot, see the Giovanni User Manual. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.", - "description_simplified": "The OMI NO2 data in Giovanni dataset allows users to visualize OMI nitrogen dioxide data in a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through Giovanni.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "", - "project": "", - "source_link": "https://giovanni.gsfc.nasa.gov/giovanni/#service=TmAvMp&starttime=&endtime=&variableFacets=dataFieldDiscipline%3AAtmospheric%20Chemistry%3BdataFieldMeasurement%3ANO2%3B", - "strengths": "18-year temporal extent, recent data available", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Giovanni", - "spatial_resolution": 0.25, - "temporal_extent": "2004-10-01 to 2022-06-03", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 133, - "fields": { - "dataset": "OMI SO2 Data from Earthdata Search", - "description": "OMI provides daily total column data at a resolution of 13x24 km; data are in HDF5 format, and can be opened using Panoply.", - "description_simplified": "The OMI SO2 Data from Earthdata Search dataset provides daily total air column data for sulfur dioxide.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?fi=OMI&fst0=Atmosphere&fsm0=Atmospheric%20Chemistry&fs10=Sulfur%20Compounds&fs20=Sulfur%20Dioxide", - "strengths": "multiple datasets available", - "format": "Varies", - "geographic_coverage": "Global", - "data_visualization": "Giovanni, Worldview", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies- multiple datasets available", - "temporal_resolution": "varies- multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 134, - "fields": { - "dataset": "TROPOMI SO2 data from Earthdata Search", - "description": "ESA TROPOMI SO2 provides additional information on this level 2 data product. As with the NO2 data above, you will need to adjust the scaling factor. Data are in NetCDF format, and can be opened using Panoply.", - "description_simplified": "The TROPOMI SO2 data from Earthdata Search dataset provides sulfur dioxide data. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search/granules?p=C1442068508-GES_DISC&tl=1542053901!4!!&fi=TROPOMI&fst0=Atmosphere&fsm0=Atmospheric%20Chemistry&fst1=Atmosphere&fsm1=Atmospheric%20Chemistry&fs11=Sulfur%20Compounds&fs21=Sulfur%20Dioxide", - "strengths": "Many granules available in Earthdata Search, also available in Giovanni and Worldview", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Earthdata Search viewer", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies- multiple datasets available", - "temporal_resolution": "varies- multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 135, - "fields": { - "dataset": "global sulfur dioxide monitoring site", - "description": "provides imagery of daily SO2 from OMI, OMPS, and TROPOMI. The site also provides information on the source of emissions", - "description_simplified": "The global sulfur dioxide monitoring site dataset provides imagery of daily SO2 from OMI, OMPS, and TROPOMI. The site also provides information on the source of emissions. These data may aid air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://so2.gsfc.nasa.gov/", - "strengths": "Multiple datasets available within link", - "format": "Images, graphs", - "geographic_coverage": "Global", - "data_visualization": "images, graphs", - "spatial_resolution": "varies", - "temporal_extent": "varies", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 136, - "fields": { - "dataset": "AIRS CO data from Earthdata Search", - "description": "AIRS measures abundances of trace components in the atmosphere including CO. Data are available daily (AIRS3STD), over 8 days (AIRS3ST8), or monthly (AIRS3STM). The instrument measures the amount of CO in the total vertical column profile of the atmosphere (from Earth\u2019s surface to top-of-atmosphere). Data are in HDF format, and can be opened using Panoply.", - "description_simplified": "Available in giovanni and worldview", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=AIRS3&ok=AIRS3&fi=AIRS&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Carbon%20Monoxide", - "strengths": "Multiple datasets available within link", - "format": "HDF", - "geographic_coverage": "Global", - "data_visualization": "Giovanni, Worldview", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies- multiple datasets available", - "temporal_resolution": "varies- multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 137, - "fields": { - "dataset": "MOPITT CO data from Earthdata Search", - "description": "Measurements of Pollution in the Troposphere (MOPITT) measures the amount of CO present in the total vertical column of the lower atmosphere (troposphere) and is measured in mole per square centimeter (mol/cm2). Data are available daily or monthly. Data are acquired using the thermal and near-infrared channels. Data are in HDF5 format, and can be opened using Panoply.", - "description_simplified": "The Measurements of Pollution in the Troposphere (MOPITT) dataset measures the amount of carbon monoxide present in the total vertical column of the lower atmosphere (troposphere) and is measured in mole per square centimeter (mol/cm2).", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=V008&ok=V008&fi=MOPITT&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Carbon%20Monoxide", - "strengths": "ongoing data collection", - "format": "HDF5", - "geographic_coverage": "Global", - "data_visualization": "Giovanni, Worldview, Panopoly", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "2003-03-03 ongiong", - "temporal_resolution": "daily/monthly" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 138, - "fields": { - "dataset": "TROPOMI CO data from Earthdata Search", - "description": "ESA TROPOMI CO provides additional information on this level 2 data product. As with the NO2 data above, you will need to adjust the scaling factor. Data are in NetCDF format, and can be opened using Panoply.", - "description_simplified": "The TROPOMI CO data from Earthdata Search provides carbon monoxide data that may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?fi=TROPOMI&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Carbon%20Monoxide", - "strengths": "Multiple datasets available within link", - "format": "NetCDF", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies- multiple datasets available", - "temporal_resolution": "varies- multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 139, - "fields": { - "dataset": "OMI AI from Earthdata Search", - "description": "OMI provides an Ultraviolet Aerosol Index; data are in HDF5 format, and can be opened using Panoply. Note that when opening the data in Panoply, there are a number of different data fields from which to choose. Select \"UVAerosolIndex\".", - "description_simplified": "The OMI AI from Earthdata Search dataset provides an Ultraviolet Aerosol Index. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "Multiple datasets available within link", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=aerosol%20index%20OMAER&ok=aerosol%20index%20OMAER&fi=OMI&fst0=Atmosphere&fsm0=Aerosols", - "strengths": "Multiple datasets available within link", - "format": "HDF5", - "geographic_coverage": "Global", - "data_visualization": "Worldview, giovanni", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies- multiple datasets available", - "temporal_resolution": "varies- multiple datasets available" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 140, - "fields": { - "dataset": "TROPOMI AI data from Earthdata Search", - "description": "ESA TROPOMI AI provides additional information on this level 2 data product. Data are NetCDF format, and can be opened using Panoply.", - "description_simplified": "The TROPOMI AI data from Earthdata Search dataset provides UV aerosol index data. The Aerosol Index (AI) is a well-established data product that has been calculated for several different satellite instruments spanning a period of more than 40 years. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "", - "project": "", - "source_link": "http://www.tropomi.eu/data-products/uv-aerosol-index", - "strengths": "data available up to 2021", - "format": "NetCDF", - "geographic_coverage": "Global", - "data_visualization": "viewer", - "spatial_resolution": "", - "temporal_extent": "varies-2021", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 141, - "fields": { - "dataset": "OMPS AI data in Worldview", - "description": "OMPS Aerosol Index layer indicates the presence of ultraviolet (UV)-absorbing particles in the air.", - "description_simplified": "The OMPS AI data in Worldview dataset includes an OMPS Aerosol Index layer which indicates the presence of ultraviolet (UV)-absorbing particles in the air. These data may aid in air quality data.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,OMPS_Aerosol_Index,Reference_Features(hidden),Coastlines", - "strengths": "recent data available", - "format": "Giovanni", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "50 km x 50 km", - "temporal_extent": "2011-11-13 to 2022-06-05", - "temporal_resolution": "101 minutes" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 142, - "fields": { - "dataset": "AIRS Dust Score in Worldview", - "description": "A Dust Score indicates the level of atmospheric aerosols in the Earth\u2019s atmosphere over the ocean. The numerical scale is a qualitative representation of the presence of dust in the atmosphere, an indication of where large dust storms may form and the areas that may be affected. Measurement from the AIRS Infrared quality assurance subset; the imagery resolution is 2 km.", - "description_simplified": "The AIRS Dust Score in Worldview dataset indicates the level of atmospheric aerosols in the Earth\u2019s atmosphere over the ocean. The numerical scale is a qualitative representation of the presence of dust in the atmosphere, an indication of where large dust storms may form and the areas that may be affected.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "", - "project": "", - "source_link": "https://go.nasa.gov/36IE3xc", - "strengths": "high temporal resolution", - "format": "HDF-EOS", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "2kmm per pixel", - "temporal_extent": "2002-08-20 to present", - "temporal_resolution": "6 mins" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 143, - "fields": { - "dataset": "MODIS/Aqua Land Surface Reflectance Data from Earthdata Search", - "description": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product.", - "description_simplified": "The MODIS/Aqua Land Surface Reflectance Data from Earthdata Search dataset provides surface reflectance data over water used in air quality monitoring and research.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=MYD09&ok=MYD09&ff=Customizable", - "strengths": "recent data available", - "format": "HDF-EOS2", - "geographic_coverage": "Global", - "data_visualization": "Earthdata Search viewer", - "spatial_resolution": "1000 m x 1000 m", - "temporal_extent": "2000-03-03 to present", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 144, - "fields": { - "dataset": "MODIS/Terra Land Surface Reflectance Data from Earthdata Search", - "description": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product.", - "description_simplified": "The MODIS/Terra Land Surface Reflectance Data from Earthdata Search dataset provides surface reflectance data over land used in air quality monitoring and research.", - "indicators": "Health & Air Quality", - "intended_use": "Path C", - "latency": "", - "limitations": "", - "project": "", - "source_link": "https://search.earthdata.nasa.gov/search?q=MOD09&ok=MOD09&ff=Customizable&fst0=Land%20Surface&fsm0=Surface%20Radiative%20Properties&fs10=Reflectance", - "strengths": "recent data available", - "format": "HDF-EOS2", - "geographic_coverage": "Global", - "data_visualization": "Earthdata Search viewer", - "spatial_resolution": "1000 m x 1000 m", - "temporal_extent": "2000-03-03 to present", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 145, - "fields": { - "dataset": "VIIRS (Suomi NPP) Deep Blue Aerosol Optical Thickness (Land and Ocean)", - "description": "The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) deep blue aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, every 6 minutes, globally. The Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea\u2010viewing Wide Field\u2010of\u2010view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land. This L2 description pertains to the SNPP VIIRS Deep Blue Aerosol version-1.1 (V1.1) product, whose record starts from March 1st 2012.", - "description_simplified": "The VIIRS (Suomi NPP) Deep Blue Aerosol Optical Thickness (Land and Ocean) dataset provides Aerosol Optical Thickness (AOT) used in air quality monitoring and research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only netcdf4 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?v=-317.24369572854476,-108.29615826069045,151.58744572854482,114.76490826069045&l=VIIRS_SNPP_AOT_Deep_Blue_Best_Estimate,VIIRS_SNPP_Angstrom_Exponent_Deep_Blue_Best_Estimate(hidden),Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor(hidden)&lg=false&t=2022-03-04-T19%3A27%3A07Z", - "strengths": "NRT, daily temporal resolution", - "format": "NetCDF4", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "6km", - "temporal_extent": "2012-03-01 to present", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 146, - "fields": { - "dataset": "VIIRS (Suomi NPP) Deep Blue Aerosol Angstrom Exponent", - "description": "\u00c5ngstr\u00f6m exponent over land is defined between 412-470 nm for 'bright' surfaces, and 470-670 nm for 'dark' surfaces. The combined \u00c5ngstr\u00f6m exponent over land and ocean are for those retrieval pixels passing quality assurance tests. This layer is created from the Deep Blue (DB) algorithm over land and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water to determine atmospheric aerosol loading for day time cloud-free snow-free scenes. This data product is designed to facilitate continuity in the aerosol record provided by the Deep Blue aerosol project for other sensors including the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometers (MODIS).", - "description_simplified": "The VIIRS (Suomi NPP) Deep Blue Aerosol Angstrom Exponent provides aerosol data that may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only netcdf4 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?v=-297.3203500600552,-98.81700395435438,131.6641000600552,105.28575395435438&l=VIIRS_SNPP_AOT_Deep_Blue_Best_Estimate(hidden),VIIRS_SNPP_Angstrom_Exponent_Deep_Blue_Best_Estimate,Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor(hidden)&lg=false&t=2022-05-06-T12%3A01%3A03Z", - "strengths": "NRT, daily temporal resolution", - "format": "NetCDF4", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "2km", - "temporal_extent": "2012 Mar 1 to present", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 147, - "fields": { - "dataset": "MLS (Aura)", - "description": "The Microwave Limb Sounder (MLS) Carbon Monoxide (CO) Mixing Ratio layer at 215 hPa (hectopascals) indicates carbon monoxide levels at the vertical atmospheric pressure level of 215hPa, and is measured in parts per billion by volume (ppbv). L2 Carbon Monoxide (CO) MLS/Aura NRT L2 CO Mixing Ratio", - "description_simplified": "The MLS (Aura) dataset indicates carbon monoxide levels. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only hdf-eos5 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,MLS_CO_215hPa_Night,MLS_CO_215hPa_Day,Reference_Features(hidden),Coastlines&v=-92.7421875,-49.47803771636507,66.5859375,55.94678771636507", - "strengths": "NRT, daily temporal resolution", - "format": "HDF-EOS5", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "2km", - "temporal_extent": "2013 May 09 to present", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 148, - "fields": { - "dataset": "Corrected Reflectance imagery in Worldview", - "description": "MODIS and VIIRS Corrected Reflectance imagery are available only as near real-time imagery. The imagery can be visualized in Worldview and Global Imagery Browse Services (GIBS). More:", - "description_simplified": "The Corrected Reflectance imagery in Worldview dataset provides surface reflectance imagery in near real time. The imagery can be visualized in Worldview and Gloval Imagery Browse Services (GIBS). These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "", - "project": "", - "source_link": "https://go.nasa.gov/2IDvag7", - "strengths": "daily temporal resolution", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "250 m, 500 m, 1 km", - "temporal_extent": "varies", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 149, - "fields": { - "dataset": "MODIS Fires and Thermal Anomalies (Day/Night)", - "description": "The MODIS Fire and Thermal Anomalies product is available from the Terra (MOD14) and Aqua (MYD14) satellites as well as a combined Terra and Aqua (MCD14) satellite product. The thermal anomalies are represented as red points (approximate center of a 1 km pixel) in Worldview/GIBS.", - "description_simplified": "The MODIS Fires and Thermal Anomalies (Day/Night) dataset provides fire anomalies for both land and water. These data may aid in air quality and disaster research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "only hdf-eos5 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,MODIS_Fires_Terra,MODIS_Fires_All,MODIS_Fires_Aqua,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&v=-101.4609375,-49.47803771636507,57.8671875,55.94678771636507", - "strengths": "daily temporal resolution", - "format": "HDF-EOS", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "1 km", - "temporal_extent": "varies", - "temporal_resolution": "Daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 150, - "fields": { - "dataset": "MLS (Aura) Nitric Acid (46 hPa, Day/Night)", - "description": "The MLS Nitric Acid (HNO3) Mixing Ratio at 46hPa layer indicates nitric acid levels at the vertical atmospheric pressure level of 46hPa, and is measured in parts per billion by volume (ppbv). It is derived from the MLS Nitric Acid (ML2HNO3_NRT) MLS/Aura NRT L2 HNO3 Mixing Ratio", - "description_simplified": "The MLS (Aura) Nitric Acid (46 hPa, Day/Night) dataset indicates nitric acid levels in the atmosphere. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only hdf-eos5 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,MLS_HNO3_46hPa_Night,MLS_HNO3_46hPa_Day,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&v=-92.7421875,-49.47803771636507,66.5859375,55.94678771636507", - "strengths": "15 min temporal resolution", - "format": "HDF-EOS5", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "165 km x 3 km", - "temporal_extent": "2013 May 09 to present", - "temporal_resolution": "15 mins" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 151, - "fields": { - "dataset": "MLS (Aura) Nitrous Oxide", - "description": "The MLS Nitrous Oxide (N2O) Mixing Ratio layer at 46hPa (hectopascals) indicates nitrous oxide levels at the vertical atmospheric pressure level of 46hPa, and is measured in parts per billion by volume (ppbv). MLS/Aura NRT L2 N2O Mixing Ratio", - "description_simplified": "The MLS (Aura) Nitrous Oxide dataset indicates nitrous oxide levels in the atmosphere. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only hdf-eos5 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,MLS_N2O_46hPa_Night,MLS_N2O_46hPa_Day,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&v=-92.7421875,-49.47803771636507,66.5859375,55.94678771636507", - "strengths": "15 min temporal resolution", - "format": "HDF-EOS5", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "165 km x 3 km", - "temporal_extent": "2013 May 09 to present", - "temporal_resolution": "15 mins" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 152, - "fields": { - "dataset": "AIRS (Aqua) Sulfur Dioxide", - "description": "The AIRS Prata SO2 Index Day/Night layer indicates Sulfur Dioxide column amounts in the atmosphere, measured in Dobson Units (DU); it is science parameter is a derived parameter from the Level 1B Near-Real Time Infrared (IR) geolocated and calibrated radiances, (AIRIBRAD_NRT). The imagery resolution is 2 km and sensor resolution is 45 km. The temporal resolution is daily. L1B IR geolocated radiances", - "description_simplified": "The AIRS (Aqua) Sulfur Dioxide dataset indicates sulfur dioxide column amounts in the atmosphere. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only hdf-eos5 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,AIRS_Prata_SO2_Index_Night,AIRS_Prata_SO2_Index_Day,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&z=3&v=-226.2647951760128,-118.25872816900927,131.2039548239872,118.27252183099073", - "strengths": "daily temporal resolution", - "format": "HDF-EOS5", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "13.5 km x 13.5 km", - "temporal_extent": "2012 May 08 to present", - "temporal_resolution": "daily" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 153, - "fields": { - "dataset": "MLS (Aura) Sulfur Dioxide", - "description": "The MLS Sulfur Dioxide (SO2) Mixing Ratio layer at 147hPa (hectopascals) indicates sulfur dioxide levels at the vertical atmospheric pressure level of 147hPa, and is measured in parts per billion by volume (ppbv). The sensor resolution is 5 km, imagery resolution is 2 km and the temporal resolution is twice daily (day and night). MLS/Aura NRT L2 SO2 Mixing Ratio", - "description_simplified": "The MLS (Aura) Sulfur Dioxide dataset indicates sulfur dioxide levels. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "only hdf-eos5 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,MLS_SO2_147hPa_Night,MLS_SO2_147hPa_Day,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&z=3&v=-226.2647951760128,-118.25872816900927,131.2039548239872,118.27252183099073", - "strengths": "15 min temporal resolution", - "format": "HDF-EOS5", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "165 km x 3 km", - "temporal_extent": "2013 May 09 to present", - "temporal_resolution": "15 mins" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 154, - "fields": { - "dataset": "OMI (Aura) Sulfur Dioxide", - "description": "The OMI Sulfur Dioxide (SO2) Lower Troposphere layer indicates the column density of sulfur dioxide in the lower troposphere (corresponding to 2.5 km center of mass altitude (CMA)) and is measured in Dobson Units (DU). Sulfur Dioxide and Aerosol Index products are used to monitor volcanic clouds and detect pre-eruptive volcanic degassing globally. L2 Sulfur Dioxide (SO2) Total Column Swath 13x24 km", - "description_simplified": "The OMI (Aura) Sulfur Dioxide dataset indicates the column density of sulfur dioxide in the lower atmosphere. These products are used to monitor volcanic clouds and detect volcanic degassing globally.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "", - "limitations": "only hdf-eos5 available", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,OMI_SO2_Planetary_Boundary_Layer,OMI_SO2_Upper_Troposphere_and_Stratosphere,OMI_SO2_Middle_Troposphere,OMI_SO2_Lower_Troposphere,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&v=-226.2647951760128,-118.25872816900927,131.2039548239872,118.27252183099073", - "strengths": "daily temporal resolution", - "format": "HDF-EOS5", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "0.25 \u00b0 x 0.25 \u00b0", - "temporal_extent": "varies", - "temporal_resolution": "1 day" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 155, - "fields": { - "dataset": "OMPS (Suomi NPP) Sulfur Dioxide", - "description": "The OMPS Sulfur Dioxide (SO2) Planetary Boundary Layer (PBL) indicates the column density of sulfur dioxide in the tropospheric boundary-layer (corresponding to the center of mass altitude (CMA) of 0.9 km) and is measured in Dobson Units (DU). The planetary boundary layer is often used in studies on near-surface pollution.", - "description_simplified": "The OMPS (Suomi NPP) Sulfur Dioxide dataset indicates the column density of sulfur dioxide in the tropospheric boundary layer. These data may aid in air quality research.", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "", - "project": "", - "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,OMPS_SO2_Planetary_Boundary_Layer,OMPS_SO2_Upper_Troposphere_and_Stratosphere,OMPS_SO2_Middle_Troposphere,OMPS_SO2_Lower_Troposphere,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&v=-226.2647951760128,-118.25872816900927,131.2039548239872,118.27252183099073", - "strengths": "NRT, 10 year temporal extent", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "Worldview", - "spatial_resolution": "50 km x 50 km", - "temporal_extent": "2012 Jan 06 to present", - "temporal_resolution": "2011-10-28 ongoing" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 156, - "fields": { - "dataset": "ArcGIS Global Air Quality Story Map", - "description": "NASA's Socioeconomic Data and Applications Center (SEDAC) offers global annual gridded PM 2.5 data for various years. This data was aggregated to common global boundaries in order to help us see what air quality is like around the world.", - "description_simplified": "", - "indicators": "Health & Air Quality", - "intended_use": "", - "latency": "", - "limitations": "", - "project": "Other", - "source_link": "https://storymaps.arcgis.com/stories/a3d0b0835b9e45b69f55e5ce94d84ddf", - "strengths": "Interactive with data download available", - "format": "shapefile", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 157, - "fields": { - "dataset": "Global Multihazard Proportional Economic Loss Risk Deciles", - "description": "The Global Multihazard Proportional Economic Loss Risks is a 2.5 minute grid of a multihazard-based economic loss risk as a proportion of the economic productivity of the analytical unit, the grid cell. Representation of multihazard risk is not based on a multihazard index but rather on combinations of hazard risk categories, drought, seismic, and hydro. The drought category includes drought only. The seismic category consists of earthquake and volcano hazards. Cyclones, floods, and landslides are included in the hydro category. For each of the six hazards considered, a binary risk surface is constructed utilizing the three most-at-risk deciles of each hazard's global proportional economic loss risks data set (deciles 8-10). Each of the category risk surfaces are constructed by adding all the relevant hazard high-risk surfaces. These categorical risk surfaces are reclassified into binary high-risk surfaces. The combination of the category risk values forms a three digit identifier for determining those locations that are at higher-risk from multihazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Multihazard Proportional Economic Loss Risks shows the multihazard-based economic loss risk as a proportion of economic productivity. In other words, several different hazards are combined and a proportion is created with the economic productivity of an area. These hazards include drought, earthquakes, volcanoes, cyclones, landslides, and flood risk.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4WS8R5B", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, DBF, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-multihazard-proportional-economic-loss-risk-deciles/maps", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 158, - "fields": { - "dataset": "Geocoded Disasters (GDIS) Dataset, v1 (1960\u200a\u2013\u200a2018)", - "description": "The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).", - "description_simplified": "The Geocoded Disasters (GDIS) Dataset includes a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters (CRED) Emergency Events Database (EM_DAT). The data included come from 39,953 locations for 9,924 disasters that happened across the world from 1960-2018. Population data are also included. This dataset may aid disaster preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "PEND - Natural Disasters", - "source_link": "https://doi.org/10.7927/zz3b-8y61", - "strengths": "Significant temporal extent", - "format": "Geopackage, R-Workspace, Geodatabase, CSV, R-script Source Code", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1960-01-01 to 2018-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 159, - "fields": { - "dataset": "Food Insecurity Hotspots Data Set, v1 (2009\u200a\u2013\u200a2019)", - "description": "The Food Insecurity Hotspots Data Set consists of grids at 250 meter (~7.2 arc-seconds) resolution that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events. The gridded data are based on subnational food security analysis provided by FEWS NET (Famine Early Warning Systems Network) in five (5) regions, including Central America and the Caribbean, Central Asia, East Africa, Southern Africa, and West Africa. Based on the Integrated Food Security Phase Classification (IPC), food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", - "description_simplified": "The Food Insecurity Hotspots Dataset shows the level of intensity and frequency of food insecurity as well as hotspot areas that have experienced consecutive food insecurity events. Food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited geographic coverage.", - "project": "FOOD - Food Security", - "source_link": "https://doi.org/10.7927/cx02-2587", - "strengths": "10 years temporal extent", - "format": "GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/food-food-insecurity-hotspots/maps/services", - "spatial_resolution": "0.00833 Decimal Degrees", - "temporal_extent": "2009-01-01 to 20191231", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 160, - "fields": { - "dataset": "US Census Grids 2010", - "description": "The U.S. Census Grids (Summary File 1), 2010 data set contains grids of demographic and socioeconomic data from the year 2010 in ASCII and GeoTIFF formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2010 TIGER/Line Files and census variables (population, households, and housing variables).", - "description_simplified": "The US Census Grids 2010 provide gridded demographic data, including age, race, ethnicity, and housing for the US and Puerto Rico.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Data only available for the year 2010", - "project": "USCG - U.S. Census Grids", - "source_link": "https://doi.org/10.7927/H40Z716C", - "strengths": "Several socioeconomic factors available within dataset", - "format": "ASCII, GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; District of Columbia; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Puerto Rico; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/usgrid-summary-file1-2010/maps", - "spatial_resolution": "0.0083 Decimal Degrees", - "temporal_extent": "2010-01-01 to 2010-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 161, - "fields": { - "dataset": "Global Estimated Net Migration Grids By Decade", - "description": "The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of globally consistent data on migration, indirect estimation methods were used. The authors relied on a combination of data on spatial population distribution for four time slices (1970, 1980, 1990, and 2000) and subnational rates of natural increase in order to derive estimates of net migration on a 30 arc-second (~1km) grid cell basis. Net migration was estimated by subtracting the population in time period 2 from the population in time period 1, and then subtracting the natural increase (births minus deaths). The residual was considered to be net migration (in-migrants minus out-migrants). The authors ran 13 geospatial net migration estimation models based on outputs from the same number of imputation runs for urban and rural rates of natural increase.This data set represents the average of those runs. These data are reliable at broad scales of analysis (e.g. ecosystems or regions), but are generally not reliable for local level analyses. The data were produced for the United Kingdom Foresight project on Migration and Global Environmental Change.", - "description_simplified": "The Global Estimated Net Migration Grids By Decade provides estimates of overall net migration (in-migration minus out-migration) per decade for the 1970s, 1980s, and 1990s.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Lacks recent data", - "project": "POPDYNAMICS - Population Dynamics", - "source_link": "https://doi.org/10.7927/H4319SVC", - "strengths": "30 year temporal extent", - "format": "GeoTIFF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/popdynamics-global-est-net-migration-grids-1970-2000/metadata", - "spatial_resolution": "0.008333 Decimal Degrees", - "temporal_extent": "1970-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 162, - "fields": { - "dataset": "Global Rural-Urban Mapping Project (GRUMP) Urban Extent Polygons, v1", - "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02 is an update to Revision 01, which included new settlements and represented the first time that SEDAC released polygons (in Esri shapefile format) with the settlement name (or name of the largest city in the case of multi-city agglomerations). The shapefile consists of polygons defined by the extent of the nighttime lights and approximated urban extents (circles) based on buffered settlement points.", - "description_simplified": "The Global Rural-Urban Mapping Project (GRUMP) Urban Extent Polygons dataset maps urban settlements in a polygon or shapefile format defined by the extent of nighttime lights and approximated urban areas. These data are useful in studying urbanization and human migration.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Temporal extent only 1995; lacks recent data", - "project": "Other", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/grump-v1-urban-ext-polygons-rev02/data-download", - "strengths": "Population and urban extent in shapefile form", - "format": ".shp or csv", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": 1995, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 163, - "fields": { - "dataset": "Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, v1 (2000\u200a\u2013\u200a2100)", - "description": "The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds). An algorithm was developed and validated to downscale the 1/8-degree resolution data set to 1-km resolution. For a given decade, the downscaling algorithm allocates the 1/8-degree decadal amount of urban land expansion to 1-km grid cells in proportion to their total urban land amounts at the beginning of the decade. The algorithm uses an iterative process to collect any overflows from already highly-developed 1-km grid cells, and then allocates them to 1-km grid cells that are not yet fully developed. This iterative process repeats itself until all 1/8-degree amounts of urban land expansion are allocated to 1-km grid cells with no overflow. The downscaling process is applied decade by decade throughout the 21st century for each urban land expansion scenario. The final product is a set of global maps displaying the 1-km fraction of urban land, updated at decadal intervals throughout the 21st century, for five different urban land expansion scenarios consistent with the Shared Socioeconomic Pathways (SSPs).", - "description_simplified": "The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios dataset provides urban land projections based on the Shared Socioeconomic Pathways (SSPs) data. These data are useful in socioeconomic, environmental, and urban sprawl research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Recent years' data are projections from original dataset publishing date", - "project": "SSP - Shared Socioeconomic Pathways", - "source_link": "https://doi.org/10.7927/1z4r-ez63", - "strengths": "Includes projections for up to year 2100", - "format": "GeoTIFF, netCDF-4", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "0.00833 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2100-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 164, - "fields": { - "dataset": "Natural Resource Protection and Child Health Indicators, 2021 Release (2010\u200a\u2013\u200a2021)", - "description": "To assist in the country selection process for the Millennium Challenge Corporation (MCC) by providing indicators of natural resource protection and child health that complement the governance, social, and economic indicators used by MCC as country selection criteria.", - "description_simplified": "The Natural Resource Protection and Child Health Indicators dataset provides indicators of natural resource protection and child health that complement the governance, social, and economic indicators used by MCC (Millenium Challenge Corporation) as country selection criteria.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Only available in excel format", - "project": "NRMI - Natural Resource Management Index", - "source_link": "https://doi.org/10.7927/5bbs-e174", - "strengths": "Includes recent data", - "format": "Excel", - "geographic_coverage": "Afghanistan; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antigua and Barbuda; Argentina; Armenia; Aruba; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; British Virgin Islands; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Eswatini; Ethiopia; Fiji; Finland; France; Gabon; Gambia; Georgia; Germany; Ghana; Gibraltar; Global; Greece; Grenada; Guatemala; Guinea; Guinea-Bissau; Guyana; Haiti; Honduras; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Israel; Italy; Jamaica; Japan; Jordan; Kazakhstan; Kenya; Kiribati; Kosovo; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Lithuania; Luxembourg; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Mauritania; Mauritius; Mexico; Micronesia; Moldova; Monaco; Mongolia; Montenegro; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; New Zealand; Nicaragua; Niger; Nigeria; Niue; North Korea; Northern Mariana Islands; Norway; Oman; Pakistan; Palau; Palestine; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Poland; Portugal; Qatar; Romania; Russia; Rwanda; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Korea; South Sudan; Spain; Sri Lanka; St Kitts and Nevis; St Lucia; St Maarten; St Pierre and Miquelon; St Vincent and The Grenadines; Sudan; Suriname; Svalbard and Jan Mayen Islands; Sweden; Switzerland; Syria; Tajikistan; Tanzania; Thailand; Timor; Togo; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States of America; Uruguay; Uzbekistan; Vanuatu; Venezuela; Vietnam; Yemen; Zambia; Zimbabwe", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "2010-01-01 to 2021-11-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 165, - "fields": { - "dataset": "Global Subnational Infant Mortality Rates, v2.01 (2015)", - "description": "The Global Subnational Infant Mortality Rates dataset provides a global subnational map of infant mortality rate estimates for the year 2015. These data may aid interdisciplinary studies of health, poverty, and the environment. ", - "description_simplified": "The Global Subnational Infant Mortality Rates dataset provides a global subnational map of infant mortality rate estimates for the year 2015. These data may aid interdisciplinary studies of health, poverty, and the environment.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Only for 2015", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/0gdn-6y33", - "strengths": "Three data formats available: GeoTIFF, gdb, and excel", - "format": "GeoTIFF, Geodatabase, Excel, PDF, PNG", - "geographic_coverage": "Global", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "0.008333 Decimal Degrees", - "temporal_extent": "2015-01-01 to 2015-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 166, - "fields": { - "dataset": "Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020\u200a\u2013\u200a2100)", - "description": "The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.", - "description_simplified": "The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age dataset provides county population projections for the US essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Recent years' data are projections from original dataset publishing date", - "project": "POPDYNAMICS - Population Dynamics", - "source_link": "https://doi.org/10.7927/dv72-s254", - "strengths": "100-year temporal extent", - "format": "Shapefile, Excel", - "geographic_coverage": "United States of America", - "data_visualization": "SEDAC Map widget", - "spatial_resolution": "", - "temporal_extent": "2020-01-01 to 2100-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 167, - "fields": { - "dataset": "Global Gridded Geographically Based Economic Data (G-Econ), v4 (1990, 1995, 2000, 2005)", - "description": "The Global Gridded Geographically Based Economic Data (G-Econ), Version 4 contains derived one degree grid cells of Gross Domestic Product (GDP) data in Grid and ASCII formats for both Market Exchange Rate (MER) and Purchasing Power Parity (PPP) for the years 1990, 1995, 2000 and 2005. MER is the exchange rate between local and U.S. dollar currencies for a given time period established by the market. PPP is the exchange rate between a country's currency and U.S. dollars adjusted to reflect the actual cost in U.S. dollars of purchasing a standardized market basket of goods in that country using the country's currency. The original data from the G-Econ Project at Yale University is also available in tabular format and includes latitude and longitude geographic coordinates of the grid cells, area of grid cells, as well as country names, distance to coast, elevation, vegetation, population, precipitation and temperature.", - "description_simplified": "The Global Gridded Geographically Based Economic Data (G-Econ), v4 (1990, 1995, 2000, 2005) dataset provides global GDP economic data based on the state of the market in each subsequent year.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "", - "project": "SPATIALECON - Spatial Economic Data", - "source_link": "https://doi.org/10.7927/H42V2D1C", - "strengths": "lacks recent data", - "format": "ASCII, Grud, Excel, PDF, PNG", - "geographic_coverage": "Afghanistan; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antarctica; Antigua and Barbuda; Argentina; Armenia; Aruba; Australia; Austria; Azerbaijan; Bahamas; Bahrain; BakerIsland; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Bouvet Island; Brazil; British Indian Ocean Territory; British Virgin Islands; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Christmas Island; Cocos Islands; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Faeroe Islands; Falkland Islands; Fiji; Finland; France; French Guiana; French Polynesia; French Southern and Antarctic Lands; Gabon; Gambia; Gaza Strip; Georgia; Germany; Ghana; Gibraltar; Global; Greece; Greenland; Grenada; Guadeloupe; Guam; Guatemala; Guernsey; Guinea; Guinea-Bissau; Guyana; Haiti; Heard and Mcdonald Islands; Honduras; Hong Kong; Howland Island; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Isle of Man; Israel; Italy; Jamaica; Jan Mayen; Japan; Jarvis Island; Jersey; Johnston Atoll; Jordan; Kazakhstan; Kenya; Kiribati; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macau; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Martinique; Mauritania; Mauritius; Mayotte; Mexico; Micronesia; Midway Islands; Moldova; Monaco; Mongolia; Montserrat; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; Netherlands Antilles; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; Niue; Norfolk Island; North Korea; Northern Mariana Islands; Norway; Oman; Pakistan; Palau; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Pitcairn Islands; Poland; Portugal; Puerto Rico; Qatar; Reunion; Romania; Russia; Rwanda; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia and Montenegro; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Georgia Island; South Korea; South Sandwich Islands; Spain; Sri Lanka; St Helena; St Kitts and Nevis; St Lucia; St Pierre and Miquelon; St Vincent and The Grenadines; Sudan; Suriname; Svalbard and Jan Mayen; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Taiwan; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Tokelau; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Turks and Caicos Islands; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States of America; Uruguay; Uzbekistan; Vanuatu; Vatican City; Venezuela; Vietnam; Virgin Islands; Wake Island; Wallis and Futuna Islands; West Bank; Yemen; Zambia; Zimbabwe", - "data_visualization": "", - "spatial_resolution": "1.0 Decimal Degrees", - "temporal_extent": "1990-01-01, 1995-01-01, 2000-01-01; 2005-01-01", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 168, - "fields": { - "dataset": "Global Roads Open Access Data Set (gROADS), v1 (1980\u200a\u2013\u200a2010)", - "description": "The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.", - "description_simplified": "The Global Roads Open Access Data Set (gROADS), v1 (1980\u200a\u2013\u200a2010) dataset provides global roads coverage using UN data.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "GROADS - Global Roads", - "source_link": "https://doi.org/10.7927/H4VD6WCT", - "strengths": "Global coverage and shapefile format for GIS", - "format": "Shapefile, Geodatabase, PDF, PNG", - "geographic_coverage": "Africa; Asia, Europe; North America; Oceania; South America", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1980-01-01 to 2010-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 169, - "fields": { - "dataset": "Global Subnational Prevalence of Child Malnutrition, v1 (1990\u200a\u2013\u200a2002)", - "description": "The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition data set consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. Data are reported for the most recent year with subnational information available at the time of development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of underweight children under five (the rate numerator), and a tabular data set of the same and associated data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition dataset provides data related to the malnutrition of children globally. Data are reported for the most recent year. These data may aid poverty and food access research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/H4K64G12", - "strengths": "12-year temporal extent", - "format": "ASCII Grid, Excel, Shapefile, PDF, PNG, WMS", - "geographic_coverage": "Afghanistan; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antarctica; Antigua and Barbuda; Argentina; Armenia; Aruba; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; British Indian Ocean Territory; British Virgin Islands; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Christmas Island; Cocos Islands; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Faeroe Islands; Falkland Islands; Fiji; Finland; France; French Guiana; French Polynesia; French Southern Territories; Gabon; Gambia; Georgia; Germany; Ghana; Gibraltar; Global; Greece; Greenland; Grenada; Guadeloupe; Guam; Guatemala; Guernsey; Guinea; Guinea-Bissau; Guyana; Haiti; Heard and McDonald Islands; Honduras; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Isle of Man; Israel; Italy; Jamaica; Japan; Jersey; Jordan; Kazakhstan; Kenya; Kiribati; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Martinique; Mauritania; Mauritius; Mayotte; Mexico; Micronesia; Moldova; Monaco; Mongolia; Montserrat; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; Netherlands Antilles; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; Norfolk Island; North Korea; Northern Mariana Islands; Oman; Pakistan; Palau; Palestine; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Pitcairn Islands; Poland; Portugal; Puerto Rico; Qatar; Reunion; Romania; Russia; Rwanda; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia and Montenegro; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Georgia Island; South Korea; South Sandwich Islands; Spain; Spratly Islands; Sri Lanka; St Helena; St Kitts and Nevis; St Lucia; St Pierre and Miquelon; St Vincent and The Grenadines; Sudan; Suriname; Svalbard and Jan Mayen; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Taiwan; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Tokelau; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Turks and Caicos Islands; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States Minor Outlying Islands; United States of America; Uruguay; US Virgin Islands; Uzbekistan; Vanuatu; Vatican City; Venezuela; Vietnam; Wallis and Futuna Islands; Yemen; Zambia; Zimbabwe", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/povmap-global-subnational-prevalence-child-malnutrition/maps", - "spatial_resolution": "", - "temporal_extent": "1990-01-01 to 2002-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 170, - "fields": { - "dataset": "Unsatisfied Basic Needs, v1 (1998\u200a\u2013\u200a2001)", - "description": "The Poverty Mapping Project: Unsatisfied Basic Needs data set consists of measures of household level wellbeing and access to basic needs (such as adequate housing conditions, water, electricity, sanitation, education, and employment) for subnational administrative units of numerous countries in Latin America: Argentina, Bolivia, Brazil, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Peru. The data products include shapefiles (vector data) and tabular data sets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN), Economic Commission for Latin America and the Caribbean (ECLAC), and Centro Internacional de Agricultura Tropical (CIAT).", - "description_simplified": "The Poverty Mapping Project: Unsatisfied Basic Needs data set consists of measures of household level wellbeing and access to basic needs (such as adequate housing conditions, water, electricity, sanitation, education, and employment) for subnational administrative units of numerous countries in Latin America: Argentina, Bolivia, Brazil, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Peru. These data may aid in human dimensions EJ research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "climate justice", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/povmap-unsatisfied-basic-needs/data-download", - "strengths": "csv and shapefile available", - "format": "Excel, shapefile, csv", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1998-2001", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 171, - "fields": { - "dataset": "Global Subnational Infant Mortality Rates, v1 (2000)", - "description": "The Poverty Mapping Project: Global Subnational Infant Mortality Rates data set consists of estimates of infant mortality rates for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per 10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator), and a tabular data set of the same and associated data. Over 10,000 national and subnational units are represented in the tabular and grid data sets, while the shapefile uses approximately 1,000 units in order to protect the intellectual property of source data sets for Brazil, China, and Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Poverty Mapping Project: Global Subnational Infant Mortality Rates dataset consists of estimates of infant mortality rates for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first birthday for every 1,000 live births. These data may aid in poverty and human dimensions EJ research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/H4PZ56R2", - "strengths": "shapefile, grid, and csv available", - "format": "ASCII Grid, Excel, Shapefile, PDF, PNG, WMS", - "geographic_coverage": "Afghanistan; Albania; Algeria; American Samoa; Andorra; Angola; Anguilla; Antarctica; Antigua and Barbuda; Argentina; Armenia; Aruba; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bermuda; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; British Indian Ocean Territory; British Virgin Islands; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Cayman Islands; Central African Republic; Chad; Chile; China; Christmas Island; Cocos Islands; Colombia; Comoros; Congo; Congo, Democratic; Cook Islands; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Faeroe Islands; Falkland Islands; Fiji; Finland; France; French Guiana; French Polynesia; French Southern Territories; Gabon; Gambia; Georgia; Germany; Ghana; Gibraltar; Global; Greece; Greenland; Grenada; Guadeloupe; Guam; Guatemala; Guernsey; Guinea; Guinea-Bissau; Guyana; Haiti; Heard and McDonald Islands; Honduras; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Isle of Man; Israel; Italy; Jamaica; Japan; Jersey; Jordan; Kazakhstan; Kenya; Kiribati; Kuwait; Kyrgyzstan; Laos; Latvia; Lebanon; Lesotho; Liberia; Libya; Liechtenstein; Lithuania; Luxembourg; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Marshall Islands; Mauritania; Mauritius; Mayotte; Mexico; Micronesia; Moldova; Monaco; Mongolia; Montserrat; Morocco; Mozambique; Myanmar; Namibia; Nauru; Nepal; Netherlands; Netherlands Antilles; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; Niue; Norfolk Island; North Korea; Northern Mariana Islands; Norway; Oman; Pakistan; Palau; Palestine; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Pitcairn Islands; Poland; Portugal; Puerto Rico; Qatar; Reunion; Romania; Russia; Rwanda; Samoa; San Marino; Sao Tome and Principe; Saudi Arabia; Senegal; Serbia and Montenegro; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Georgia Island; South Korea; South Sandwich Islands; Spain; Spratly Islands; Sri Lanka; St Helena; St Kitts and Nevis; St Lucia; St Pierre and Miquelon; St Vincent and The Grenadines; Sudan; Suriname; Svalbard and Jan Mayen; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Taiwan; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Tokelau; Tonga; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Turks and Caicos Islands; Tuvalu; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States Minor Outlying Islands; United States of America; Uruguay; US Virgin Islands; Vanuatu; Vatican City; Venezuela; Vietnam; Wallis and Futuna Islands; Yemen; Zambia; Zimbabwe", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/povmap-global-subnational-infant-mortality-rates-v2-01/maps", - "spatial_resolution": "", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 172, - "fields": { - "dataset": "Small Area Estimates of Poverty and Inequality, v1 (1990\u200a\u2013\u200a2002)", - "description": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality data set consists of consumption-based poverty, inequality and related measures for subnational administrative units in approximately twenty countries throughout Africa, Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and survey data using small area estimates techniques. The collection of data have been compiled, integrated and standardized from the original data providers into a unified spatially referenced and globally consistent data set. The data products include shapefiles (vector data), tabular data sets (csv format), and centroids (csv file with latitude and longitude of a geographic unit and associated poverty estimates). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with a number of external data providers.", - "description_simplified": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality dataset consists of consumption-based poverty, inequality and related measures for twenty countries throughout Africa, Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and survey data using small area estimates techniques.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "climate justice", - "source_link": "https://sedac.ciesin.columbia.edu/data/set/povmap-small-area-estimates-poverty-inequality/data-download", - "strengths": "shapefile and csv available", - "format": "Excel, shapefile, csv", - "geographic_coverage": "Global", - "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/povmap-small-area-estimates-poverty-inequality/maps", - "spatial_resolution": "", - "temporal_extent": 2005, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 173, - "fields": { - "dataset": "Poverty and Food Security Case Studies, v1 (1998\u200a\u2013\u200a2002)", - "description": "The Poverty Mapping Project: Poverty and Food Security Case Studies data set consists of small area estimates of poverty, inequality, food security and related measures for subnational administrative units in Mexico, Ecuador, Kenya, Malawi, Bangladesh, Sri Lanka, Nigeria and Vietnam. These data come from country level cases studies that examine poverty and food security from a spatial analysis perspective. The data products include shapefiles (vector data) and tabular data sets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and Centro Internacional de Agricultura Tropical (CIAT). The data set was originally produced by CIAT, International Maize and Wheat Improvement Center (CIMMYT), International Livestock Research Institute (ILRI), International Food Policy Research Institute (IFPRI), International Rice Research Institute (IRRI), International Water Management Institute (IWMI), and International Institute for Tropical Agriculture (IITA).", - "description_simplified": "The Poverty Mapping Project: Poverty and Food Security Case Studies dataset consists of small area estimates of poverty, inequality, food security and related measures for Mexico, Ecuador, Kenya, Malawi, Bangladesh, Sri Lanka, Nigeria and Vietnam. These data come from country level cases studies that examine poverty and food security from a spatial analysis perspective.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "lacks recent data", - "project": "PMP - Poverty Mapping Project", - "source_link": "https://doi.org/10.7927/H4FF3Q9B", - "strengths": "Individual case studies provide closer look at poverty", - "format": "CSV, Shapefile", - "geographic_coverage": "Bangladesh; Eciador; Kenya; Malawi; Mexico; Nigeria; Vietnam; Sri Lanka", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1998-01-01 to 2002-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 174, - "fields": { - "dataset": "Country-Level GDP and Downscaled Projections Based on the SRES A1, A2, B1, and B2 Marker Scenarios, v1 (1990\u200a\u2013\u200a2100)", - "description": "The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Country-Level GDP and Downscaled Projections Based on the SRES A1, A2, B1, and B2 Marker Scenarios, v1 (1990\u200a\u2013\u200a2100) dataset provides economic (GDP) data based on different emissions scenarios related to climate change mitigation.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "projections based on original publishing date", - "project": "SDP - Socioeconomic Downscaled Projections", - "source_link": "https://doi.org/10.7927/H4XW4GQ1", - "strengths": "global coverage", - "format": "Excel, HTML", - "geographic_coverage": "Africa; Afghanistan; Albania; Algeria; Angola; Argentina; Armenia; Asia; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; Brunei; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Central African Republic; Chad; Chile; China; Colombia; Comoros; Congo; Congo, Democratic; Costa Rica; Cote D'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Fiji; Finland; France; French Polynesia; Gabon; Gambia; Gaza Strip; Georgia; Germany; Ghana; Global; Greece; Guadeloupe; Guam; Guatemala; Guinea; Guinea-Bissau; Guyana; Haiti; Honduras; Hong Kong; Hungary; Iceland; India; Indonesia; Iran; Iraq; Ireland; Israel; Italy; Jamaica; Japan; Jordan; Kazakhstan; Kenya; Kuwait; Kyrgyzstan; Laos; Latin America; Latvia; Lebanon; Lesotho; Liberia; Libya; Lithuania; Luxembourg; Macau; Macedonia; Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Martinique; Mauritania; Mauritius; Mexico; Middle East; Moldova; Mongolia; Morocco; Mozambique; Myanmar; Namibia; Nepal; Netherlands; Netherlands Antilles; New Caledonia; New Zealand; Nicaragua; Niger; Nigeria; North Korea; Norway; Oman; Pakistan; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Poland; Portugal; Puerto Rico; Qatar; Reunion; Romania; Russia; Rwanda; Samoa; Saudi Arabia; Senegal; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; Somalia; South Africa; South Korea; Southeastern Asia; Spain; Sri Lanka; Sudan; Suriname; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Tajikistan; Tanzania; Thailand; Timor-Leste; Togo; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States of America; Uruguay; Uzbekistan; Vanuatu; Venezuela; Vietnam; Western Sahara; Yemen; Yugoslavia; Zambia; Zimbabwe", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "1990-01-01 to 2100-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 175, - "fields": { - "dataset": "SEDAC Popgrid Mapper", - "description": "Enables users to visualize data and map layers related to socioeconomic, infrastructure, natural disasters, and environment and analyze potential impacts and exposure.", - "description_simplified": "The SEDAC Popgrid Mapper enables users to visualize data and map layers related to socioeconomic, infrastructure, natural disasters, and environment and analyze potential impacts and exposure.", - "indicators": "Human Dimensions", - "intended_use": "Path A", - "latency": "", - "limitations": "no data download", - "project": "", - "source_link": "https://sedac.ciesin.columbia.edu/mapping/popgrid/", - "strengths": "viewer only for data visualization", - "format": "view only", - "geographic_coverage": "United States", - "data_visualization": "viewer only", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 176, - "fields": { - "dataset": "NASA's Global Flood Proportional Economic Loss Risk Deciles", - "description": "The Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Flood Proportional Economic Loss Risk Deciles shows flood hazard economic loss as a proportion of Gross Domestic Product (GDP) of an area. The shown rates are calculated based on how often and to what extend the hazard of floods poses to area. These data can aid in storm preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4XS5S9Q", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 177, - "fields": { - "dataset": "NASA's Global Flood Total Economic Loss Risk Deciles", - "description": "The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", - "description_simplified": "The Global Flood Proportional Economic Loss Risk Deciles shows the total global flood economic loss using the Gross Domestic Product (GDP) of an area. The economic loss is assigned a decile or rank (1-10) based on how often and to what extend the hazard of floods poses to area. These data can aid in storm preparation and recovery research.", - "indicators": "Human Dimensions", - "intended_use": "Path B", - "latency": "", - "limitations": "Limited temporal extent", - "project": "NDH - Natural Disaster Hotspots", - "source_link": "https://doi.org/10.7927/H4T151KP", - "strengths": "Visualization available through SEDAC Map widget.", - "format": "ASCII, DBF, PDF, PNG, WMS", - "geographic_coverage": "Global", - "data_visualization": "SEDAC map widget", - "spatial_resolution": "0.0417 Decimal Degrees", - "temporal_extent": "2000-01-01 to 2000-12-31", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 178, - "fields": { - "dataset": "Berkeley Mapping for EJ", - "description": "Mapping dashboard showing cumulative impacts of environmental, public health, and socioeconomics disparities in one EJ indicator. Mapping available only for Virginia and Colorado currently. ", - "description_simplified": "", - "indicators": "Combination", - "intended_use": "Path A", - "latency": "", - "limitations": "Data download not easily accessible", - "project": "Other", - "source_link": "https://mappingforej.berkeley.edu/", - "strengths": "Very user-friendly visualization", - "format": "", - "geographic_coverage": "Virgina and Colorado, USA", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 179, - "fields": { - "dataset": "American Forests Tree Equity Score", - "description": "Mapping tool that assigns a \"tree equity score\" (TES) for each census block. TES is derived by culimating data on existing tree canopy, population density, income, employment, surface temperature, race, age, and health to provide a score between 1 and 100, with 100 being achieved Tree Equity. ", - "description_simplified": "", - "indicators": "Combination", - "intended_use": "Path A", - "latency": "", - "limitations": "", - "project": "Other", - "source_link": "https://www.treeequityscore.org/", - "strengths": "Data download easy to access", - "format": "shapefile", - "geographic_coverage": "TES Mapper: United States; TES Analyzer: Rhode Island", - "data_visualization": "", - "spatial_resolution": "varies", - "temporal_extent": "", - "temporal_resolution": "annual" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 180, - "fields": { - "dataset": "EPA EJScreen", - "description": "EPA mapping tool with environmental and demographic data per census block area. Tool can show mapping of one specific indicator, or side-by-side viewing. Metadata available for download. ", - "description_simplified": "", - "indicators": "Disasters", - "intended_use": "Path A", - "latency": "", - "limitations": "Lacks finer resolution", - "project": "Other", - "source_link": "https://ejscreen.epa.gov/mapper/", - "strengths": "User-friendly interface", - "format": "csv", - "geographic_coverage": "United States and territories", - "data_visualization": "", - "spatial_resolution": "census tract", - "temporal_extent": 2019, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 181, - "fields": { - "dataset": "State of Global Air 2020", - "description": "developed as part of the Institute for Health Metrics and Evaluation\u2019s (IHME) annual Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), provides an interactive tool to view and compare the latest air pollution and health data, create custom maps and graphs, and download the images and data.", - "description_simplified": "", - "indicators": "Health & Air Quality", - "intended_use": "Path A", - "latency": "", - "limitations": "Lacks finer resolution", - "project": "Other", - "source_link": "https://www.stateofglobalair.org/data/#/air/plot", - "strengths": "Graphs and maps provided", - "format": "PNG, JPEG, CSV", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "Countries/regions", - "temporal_extent": 2019, - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 182, - "fields": { - "dataset": "AirNow", - "description": "The Environmental Protection Agency\u2019s ground-based PM and Ozone combined Air Quality Index (AQI)", - "description_simplified": "", - "indicators": "Health & Air Quality", - "intended_use": "Path A", - "latency": "NRT", - "limitations": "", - "project": "Other", - "source_link": "https://www.airnow.gov/?city=Brunswick&state=GA&country=USA", - "strengths": "Interactive map with current and past data", - "format": "", - "geographic_coverage": "United States and territories", - "data_visualization": "", - "spatial_resolution": "city/individual air monitor", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 183, - "fields": { - "dataset": "AirNow International", - "description": "international program for AQI, with information provided from partnering organizations", - "description_simplified": "", - "indicators": "Health & Air Quality", - "intended_use": "Path A", - "latency": "NRT", - "limitations": "", - "project": "Other", - "source_link": "https://www.airnow.gov/index.cfm?action=airnow.international", - "strengths": "Interactive map with current and past data", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "city/individual air monitor", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 184, - "fields": { - "dataset": "National Air Quality: Status and Trends of Key Air Pollutants", - "description": "Trends on a national and regional level are available through the EPA\u2019s Air Quality Trends.", - "description_simplified": "", - "indicators": "Health & Air Quality", - "intended_use": "Path A", - "latency": "", - "limitations": "No data download", - "project": "Other", - "source_link": "https://www.epa.gov/air-trends", - "strengths": "Graphed data provided", - "format": "Excel, html", - "geographic_coverage": "National/regional", - "data_visualization": "", - "spatial_resolution": "Country", - "temporal_extent": "", - "temporal_resolution": "annual" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 185, - "fields": { - "dataset": "Explore 19 Years of Global Air Quality in Living Atlas", - "description": "Investigate how global air quality patterns have changed over time, and how poor air quality impacts the human population with a new layer added to ArcGIS Living Atlas of the World. The feature layer contains aggregated particulate matter 2.5 (PM 2.5) concentrations offered by NASA\u2019s Socioeconomic Data and Applications Center (SEDAC) at multiple geography levels:", - "description_simplified": "", - "indicators": "Health & Air Quality", - "intended_use": "Path A", - "latency": "", - "limitations": "", - "project": "Other", - "source_link": "https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/mapping/explore-19-years-of-global-air-quality-in-living-atlas/", - "strengths": "Interactive ESRI mapper", - "format": "shapefile", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 186, - "fields": { - "dataset": "Global Burden of Disease (GBD)", - "description": "Global Burden of Diseases, Injuries, and Risk Factors Study, out of the Institute for Health Metrics and Evaluation (IHME) is an independent population health research center at the University of Washington that provides rigorous and comparable measurement of the world's most important health problems and evaluates the strategies used to address them.", - "description_simplified": "", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "varies", - "limitations": "Site and tools can be confusing", - "project": "Other", - "source_link": "http://www.healthdata.org/gbd", - "strengths": "Several data visualizations available", - "format": "", - "geographic_coverage": "Global", - "data_visualization": "", - "spatial_resolution": "varies- multiple datasets available", - "temporal_extent": "varies", - "temporal_resolution": "varies" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 187, - "fields": { - "dataset": "Maryland's Environmental Justice Screening App", - "description": "The Maryland Environmental Justice Screen Tool (MD EJSCREEN) assesses environmental justice risks among census tracts in the state of Maryland. Developed by the Community Engagement, Environmental Justice, and Health Laboratory at the University of Maryland School of Public Health, this tool combines the average pollution burden of a community with the average population demographic characteristics to produce an Environmental Justice (EJ) score.1 Stakeholders advocacy resulted in the inclusion of six indicators of EJ risk specific to Maryland: asthma, emergency room discharges, percent non-White, proximity to treatment, storage and disposal facilities, myocardial infarction discharges, low birth weight infants, and particulate matter. Through this tool, Maryland residents can be better informed of disparities in EJ risk among different communities and their associated health impacts.", - "description_simplified": "", - "indicators": "Combination", - "intended_use": "Path A", - "latency": "", - "limitations": "Data download not easily accessible", - "project": "Other", - "source_link": "https://p1.cgis.umd.edu/ejscreen/", - "strengths": "Interactive mapper with different layers, available in ArcGIS mapper", - "format": "shapefile", - "geographic_coverage": "Maryland", - "data_visualization": "", - "spatial_resolution": "census tracts/ county lines", - "temporal_extent": "", - "temporal_resolution": "annual" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 188, - "fields": { - "dataset": "Illinois Solar for All\u2019s Environmental Justice Communities", - "description": "Communities (in orange) were designated as such through a calculation utilizing the US EPA tool, EJ Screen, and demonstrates a higher risk of exposure to pollution based on environmental and socioeconomic factors. The communities in blue are those that were created through the self designation process. Specific questions can be directed to: info@illinoisSFA.com", - "description_simplified": "", - "indicators": "Combination", - "intended_use": "Path A", - "latency": "", - "limitations": "Data download not easily accessible", - "project": "Other", - "source_link": "https://elevate.maps.arcgis.com/apps/webappviewer/index.html?id=cfd020c99ed844668450c6b77eacb411", - "strengths": "Interactive tool ", - "format": "shapefile", - "geographic_coverage": "Illinois", - "data_visualization": "", - "spatial_resolution": "census tracts", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 189, - "fields": { - "dataset": "University of Michigan Screening Tool for Environmental Justice", - "description": "A beta-version of an environmental justice screening tool for the state of Michigan.", - "description_simplified": "", - "indicators": "Combination", - "intended_use": "Path A", - "latency": "", - "limitations": "Data download not easily accessible", - "project": "Other", - "source_link": "https://umich.maps.arcgis.com/apps/webappviewer/index.html?id=dc4f0647dda34959963488d3f519fd24", - "strengths": "Interactive tool ", - "format": "shapefile", - "geographic_coverage": "Michican", - "data_visualization": "", - "spatial_resolution": "census tracts", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 190, - "fields": { - "dataset": "CalEnviroScreen", - "description": "California\u2019s CalEnviroScreen was created in 2013 and is currently in its 3rd version (released 2018). The map is used in key decision-making throughout the state, including targeting investment of proceeds from California\u2019s cap-and-trade program (AB 32).", - "description_simplified": "", - "indicators": "Combination", - "intended_use": "Path A", - "latency": "", - "limitations": "Data download not easily accessible", - "project": "Other", - "source_link": "https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40", - "strengths": "Interactive tool ", - "format": "shapefile", - "geographic_coverage": "California", - "data_visualization": "", - "spatial_resolution": "census tracts", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 191, - "fields": { - "dataset": "Washington\u2019s Environmental Health Disparities Map", - "description": "Washington\u2019s Environmental Health Disparities Map was created in 2018 and modelled after CalEnviroScreen to provide a cumulative environmental health impact score for each census tract reflecting pollutant exposures and factors that affect people\u2019s vulnerability to environmental pollution.", - "description_simplified": "", - "indicators": "Combination", - "intended_use": "Path A", - "latency": "", - "limitations": "Data download not easily accessible", - "project": "Other", - "source_link": "https://fortress.wa.gov/doh/wtn/WTNIBL/", - "strengths": "Interactive tool ", - "format": "shapefile", - "geographic_coverage": "Washington", - "data_visualization": "", - "spatial_resolution": "census tracts", - "temporal_extent": "", - "temporal_resolution": "" - } - }, - { - "model": "environmental_justice.environmentaljusticerow", - "pk": 192, - "fields": { - "dataset": "Purple Air", - "description": "PurpleAir makes sensors that empower community scientists who collect hyper-local air quality data and share it with the public.", - "description_simplified": "", - "indicators": "Health & Air Quality", - "intended_use": "Path B", - "latency": "NRT", - "limitations": "Data only available in JSON format", - "project": "Other", - "source_link": "https://map.purpleair.com/1/mAQI/a10/p604800/cC0#8.23/38.904/-77.169", - "strengths": "Interactive map and graphing tool", - "format": "JSON", - "geographic_coverage": "Global, but limited outside US", - "data_visualization": "", - "spatial_resolution": "individual sensors", - "temporal_extent": "", - "temporal_resolution": "NRT" - } - } -] +[{"model": "environmental_justice.environmentaljusticerow", "pk": 1, "fields": {"dataset": "ECCO (Estimating the Circulation and Climate of the Ocean) Global Mean Sea Level - Monthly Mean (Version 4 Release 4)", "description": "This dataset provides monthly-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense.", "description_simplified": "The ECCO (Estimating the Circulation and Climate of the Ocean) Global Mean Sea Level - Monthly Mean dataset provides monthly-averaged global mean sea level from the ECCO (Estimating the Circulation and Climate of the Ocean) Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. The dataset reconstructs average sea level for urban flooding and coastal vulnerability research.", "indicators": "Climate Change", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "ECCO - Estimating the Circulation and Climate of the Ocean", "source_link": "https://dx.doi.org/10.5067/ECTSM-MSL44", "strengths": "20-year temporal extent", "format": "netCDF-4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "50 kilometers", "temporal_extent": "1992-01-01 to 2018-01-01", "temporal_resolution": "Monthly - < Annual"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 2, "fields": {"dataset": "ECCO Global Mean Sea Level - Daily Mean (Version 4 Release 4)", "description": "This dataset provides daily-averaged global mean sea level from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense.", "description_simplified": "The ECCO (Estimating the Circulation and Climate of the Ocean) Global Mean Sea Level - Daily Mean dataset provides daily-averaged global mean sea level from the ECCO (Estimating the Circulation and Climate of the Ocean) Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. The dataset reconstructs average sea level for urban flooding and coastal vulnerability research.", "indicators": "Climate Change", "intended_use": "Path C", "latency": "", "limitations": "", "project": "ECCO - Estimating the Circulation and Climate of the Ocean", "source_link": "https://dx.doi.org/10.5067/ECTSD-MSL44", "strengths": "20-year temporal extent", "format": "netCDF-4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "50 kilometers", "temporal_extent": "1992-01-01 to 2018-01-01", "temporal_resolution": "Daily- < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 3, "fields": {"dataset": "Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1", "description": "This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 4.2 and version 5.x releases are described in detail in the user handbook.", "description_simplified": "The Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 dataset provides Global Mean Sea Level (GMSL) estimates.", "indicators": "Climate Change", "intended_use": "Path C", "latency": "4 months", "limitations": "Low latency", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://dx.doi.org/10.5067/GMSLM-TJ151", "strengths": "recent data available", "format": "ASCII", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1992-12-01 to Present", "temporal_resolution": "Weekly - < Monthly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 4, "fields": {"dataset": "Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1- 1 Granule", "description": "This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 4.2 and version 5.x releases are described in detail in the user handbook.", "description_simplified": "The Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters dataset includes globally averaged Sea Surface Height Anomalies (SSHA) from September 1992 to present. Adjustments for bias and glacial differences. 1-granule dataset", "indicators": "Climate Change", "intended_use": "Path C", "latency": "up to 4 months", "limitations": "Low latency", "project": "Other", "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2205556193-POCLOUD.html", "strengths": "30-year temporal extent", "format": "ASCII", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1992-09-01 ongoing", "temporal_resolution": "Weekly - < Monthly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 5, "fields": {"dataset": "Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1- 3 Granules", "description": "This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied.", "description_simplified": "The Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters dataset includes globally averaged Sea Surface Height Anomalies (SSHA) from September 1992 to present. Adjustments for bias and glacial differences. 3-granule dataset", "indicators": "Climate Change", "intended_use": "Path C", "latency": "up to 4 months", "limitations": "Low latency", "project": "Other", "source_link": "https://cmr.earthdata.nasa.gov/search/concepts/C2157848116-PODAAC.html", "strengths": "10-day temporal resolution", "format": "ASCII", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1992-12-31 ongoing", "temporal_resolution": "10 day"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 6, "fields": {"dataset": "Integrated Multi-Mission Ocean Altimeter Data for Climate Research complete time series Version 5.1", "description": "This dataset contains along track Sea Surface Height Anomalies (SSHA) from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 missions geo-referenced to a mean reference orbit. Altimeter data from the multi-mission Geophysical Data Records (GDRs) have been interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. Version 5.0 updates include improved Precise Orbit Determination (POD) with GSFC std2006 standards, and the application of internal tides.", "description_simplified": "This dataset contains along track Sea Surface Height Anomalies (SSHA) from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 missions geo-referenced to a mean reference orbit.", "indicators": "Climate Change", "intended_use": "Path C", "latency": "4 months", "limitations": "Low latency", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://dx.doi.org/10.5067/ALTTS-TJA51", "strengths": "30-year temporal extent", "format": "netCDF-4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1992-09-25 to Present", "temporal_resolution": "Weekly - < Monthly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 7, "fields": {"dataset": "Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1", "description": "This dataset contains along track Sea Surface Height Anomalies (SSHA) for individual 10-day cycles from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 missions geo-referenced to a mean reference orbit. Altimeter data from the multi-mission Geophysical Data Records (GDRs) have been interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. Version 5.0 updates include improved Precise Orbit Determination (POD) with GSFC std2006 standards, and the application of internal tides.", "description_simplified": "The Integrated Multi-Mission Ocean Altimeter Data for Climate Research dataset contains SSHA adjusted for satellite differences and tides.", "indicators": "Climate Change", "intended_use": "Path C", "latency": "4 months", "limitations": "Low latency", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://dx.doi.org/10.5067/ALTCY-TJA51", "strengths": "30-year temporal extent", "format": "netCDF-4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1992-09-25 to Present", "temporal_resolution": "Weekly - < Monthly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 8, "fields": {"dataset": "MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V011", "description": "The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. 2016-01-01 to 2019-12-31", "description_simplified": "The MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V011 dataset provides land Surface Phenology (seasonal changes in plant greening, flowering, etc) for North America. These datasets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping.", "indicators": "Climate Change", "intended_use": "Path C", "latency": "", "limitations": "Only available for North America", "project": "", "source_link": "https://doi.org/10.5067/Community/MuSLI/MSLSP30NA.011 ", "strengths": "Semi-recent data available", "format": "netCDF-4", "geographic_coverage": "North America", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2016-01-01 to 2019-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 9, "fields": {"dataset": "Reconstructed Global Mean Sea Level 1900-2018", "description": "This dataset contains reconstructed global-mean sea level evolution and the estimated contributing processes over 1900-2018. Reconstructed sea level is based on annual-mean tide-gauge observations and uses the virtual-station method to aggregate the individual observations into a global estimate. The contributing processes consist of thermosteric changes, glacier mass changes, mass changes of the Greenland and Antarctic Ice Sheet, and terrestrial water storage changes. The glacier, ice sheet, and terrestrial water storage are estimated by combining GRACE observations (2003-2018) with long-term estimates from in-situ observations and models. Steric estimates are based on in-situ temperature profiles.", "description_simplified": "The Reconstructed Global Mean Sea Level from GRACE and In Situ 1900 to 2018 dataset shows the average global sea level change and the processes that caused it over the years 1900-2018. Some of the contributing processes include changes in global temperature, glacier mass changes, ice sheet changes, and changes in water stored on land.", "indicators": "Climate Change", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://dx.doi.org/10.5067/GMSLT-FJPL1", "strengths": "18-year temporal extent", "format": "netCDF-4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1900-01-01 to 2018-12-31", "temporal_resolution": "Monthly - < Annual"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 10, "fields": {"dataset": "SEDAC Hazards Mapper", "description": "The SEDAC Hazards Mapper enables users to visualize data and map layers related to Socioeconomic, Infrastructure, Natural Disasters, and Environment and analyze potential impacts and exposure. The web app applies layers from various sources including SEDAC, NASA LANCE, NASA GIBS, USGS, NOAA, ESRI, and others.", "description_simplified": "The SEDAC Hazards Mapper enables users to visualize data and map layers related to Socioeconomic, Infrastructure, Natural Disasters, and Environment.", "indicators": "Disasters", "intended_use": "Path A", "latency": "Varies", "limitations": "Not for data download", "project": "", "source_link": "https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/sedac-hazards-mapper", "strengths": "Interactive mapper with assorted tools", "format": "Interactive Visualization", "geographic_coverage": "Global", "data_visualization": "Interactive Mapper", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 11, "fields": {"dataset": "ARIA (Advanced Rapid Imaging and Analysis) DPM (Damage Proxy Map) Puerto Rico", "description": "The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory in Pasadena, California, and Caltech, also in Pasadena, created this Damage Proxy Map (DPM) depicting areas of Eastern Puerto Rico that are likely damaged (shown by red and yellow pixels) as a result of Hurricane Maria (Category 4 at landfall in Puerto Rico on Sept. 20, 2017). The map is derived from synthetic aperture radar (SAR) images from the Copernicus Sentinel-1A and Sentinel-1B satellites, operated by the European Space Agency (ESA). The images were taken before (Mar. 25, 2017) and after (Sept. 21, 2017) the landfall of the storm.", "description_simplified": "The ARIA (Advanced Rapid Imaging and Analysis) DPM (Damage Proxy Map) Puerto Rico dataset shows areas of Eastern Puerto Rico that are likely to be damaged as a result of Hurricane Maria.", "indicators": "Disasters", "intended_use": "Path B", "latency": "", "limitations": "Data only available for case study of Puerto Rico (2017)", "project": "", "source_link": "https://appliedsciences.nasa.gov/our-impact/news/aria-damage-proxy-map-puerto-rico-after-hurricane-maria https://ghis.maps.arcgis.com/home/item.html?id=1ce3ccaacc6c4cd7b3b6cef4ea4980aa", "strengths": "Visualization available through ArcGIS viewer", "format": "KML", "geographic_coverage": "Puerto Rico", "data_visualization": "ArcGIS viewer", "spatial_resolution": "30 meters", "temporal_extent": "2017-03-25, 2017-09-21", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 12, "fields": {"dataset": "SAR (Synthetic Aperture Radar) Hurricane Monitoring for the Gulf and East Coast", "description": "The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composites of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event.", "description_simplified": "Visualization of Sentinel-1 Water Maps and other imagery over the Gulf and East Coast for the 2021 hurricane season. Blue shows water extent.", "indicators": "Disasters", "intended_use": "Path B", "latency": "", "limitations": "", "project": "", "source_link": "https://nasa.maps.arcgis.com/home/webmap/viewer.html?webmap=f33be724f04b4b4c942edd0c9bd18f48", "strengths": "Visualization of data to show pre and post storm conditions", "format": "Interactive Visualization", "geographic_coverage": "Unites States", "data_visualization": "ArcGIS Viewer", "spatial_resolution": "30 meters", "temporal_extent": "2021-01-01 to 2021-12-31", "temporal_resolution": "8 days"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 13, "fields": {"dataset": "ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015", "description": "This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks.", "description_simplified": "The ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015 dataset provides maps of landscape burn severity from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. These data may be useful in fire disaster and recovery research.", "indicators": "Disasters", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "ABoVE - Arctic-Boreal Vulnerability Experiment", "source_link": "https://dx.doi.org/10.3334/ORNLDAAC/1694", "strengths": "Pre-classified burn severity for ease of use", "format": "GeoTIFF", "geographic_coverage": "Northwest Territories, Canada", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2014-05-01 to 2015-10-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 14, "fields": {"dataset": "ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015", "description": "This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. 1985-01-01 to 2015-12-31\n", "description_simplified": "The ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015 dataset includes differenced Normalized Burned Ratio (dNBR) data for burn scars from fires within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project in Alaska and Canada 1985-2015. These data may be useful in fire disaster research.", "indicators": "Disasters", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "ABoVE - Arctic-Boreal Vulnerability Experiment", "source_link": "https://dx.doi.org/10.3334/ORNLDAAC/1564", "strengths": "30-year temporal extent", "format": "GeoTIFF", "geographic_coverage": "Alaska, Canada", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "1985-01-01 to 2015-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 15, "fields": {"dataset": "GEOS-5 Weather Maps", "description": "Within the viewer, select the parameter or field of interest, the area of interest, and then indicate the forecast time and the forecast lead hour. Animate shows the forecast for the given parameter over the time period indicated. Note that it may take time to load the images to animate. For those variables near the surface, make sure to select 850 as your level.", "description_simplified": "The GEOS-5 Weather Maps provides a viewer, allowing users to select a parameter or field of interest, area of interest, and a forcast time. Users can then produce an animation of the selected weather parameters.", "indicators": "Extreme Heat", "intended_use": "Path A", "latency": "", "limitations": "", "project": "", "source_link": "https://fluid.nccs.nasa.gov/wxmaps/", "strengths": "Multiple datasets available", "format": "", "geographic_coverage": "North America", "data_visualization": "dataset is a viewer", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 16, "fields": {"dataset": "Global Urban Heat Island (UHI) Data Set, v1 (2013)", "description": "The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC?s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC?s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).", "description_simplified": "SEDAC's Global Urban Heat Island (UHI) Data Set, v1 (2013) dataset provides a global dataset of average summer daytime maximum land surface temperatures and minimum nighttime land surface temperatures. These data may aid in urban heat research.", "indicators": "Extreme Heat", "intended_use": "Path A", "latency": "", "limitations": "Lacks recent data", "project": "SDEI - Satellite-Derived Environmental Indicators", "source_link": "https://doi.org/10.7927/H4H70CRF ", "strengths": "Shapefile format for use in GIS", "format": "Shapefile, Excel, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/sdei-global-uhi-2013/maps", "spatial_resolution": "", "temporal_extent": "2013-01-01 to 2013-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 17, "fields": {"dataset": "ECOSTRESS Land Surface Temperature and Emissivity Daily L2 Global 70m V001", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. The ECO2LSTE Version 1 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO2LSTE data product was derived using a physics-based Temperature and Emissivity Separation (TES) algorithm. The ECO2LSTE is provided as swath data and has a spatial resolution of 70 meters (m).", "description_simplified": "The ECOSTRESS Land Surface Temperature dataset provides land surface temperature data and urban heat analysis. These data may aid mitigation strategies for urban heat and heat stress.", "indicators": "Extreme Heat", "intended_use": "Path B", "latency": "", "limitations": "Latency unclear", "project": "ECOSTRESS - ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station", "source_link": "https://dx.doi.org/10.5067/ECOSTRESS/ECO2LSTE.001", "strengths": "Recent data available", "format": "HDF5", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "70 meters", "temporal_extent": "2018-07-09 to Present", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 18, "fields": {"dataset": "Global High Resolution Daily Extreme Urban Heat Exposure", "description": "The Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily), 1983-2016 data set contains a high-resolution, longitudinal global record of geolocated urban extreme heat events and urban population exposure estimates for more than 10,000 urban settlements worldwide for 1983-2016. Urban extreme heat events and urban population exposure are identified for each urban settlement in the data record for five combined temperature-humidity thresholds: two-day or longer periods where the daily maximum Heat Index (HImax) > 40.6 \u00b0C; one-day or longer periods where HImax > 46.1 \u00b0C; and one day or longer periods where the daily maximum Wet Bulb Globe Temperature (WBGTmax) > 28 \u00b0C, 30 \u00b0C, and 32 \u00b0C. The WBGTmax thresholds follow the International Standards Organization (ISO) criteria for risk of occupational heat related heat illness, whereas the HImax thresholds follow the U.S. National Weather Services' definition for an excessive heat warning. For each criteria, across urban settlements worldwide, the data set also contains the duration, intensity, and severity of each urban extreme heat event.", "description_simplified": "The Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily) dataset contains a record of urban extreme heat events and exposure estimates for over 10,000 urban settlements worldwide for the years 1983-2016.", "indicators": "Extreme Heat", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "SDEI - Satellite-Derived Environmental Indicators", "source_link": "https://doi.org/10.7927/fq7g-ny13 ", "strengths": "33-year temporal extent", "format": "Shapefile, CSV", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1983-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 19, "fields": {"dataset": "AIRS Relative Humidity from Earthdata Search", "description": "AIRS data are daily at 1 degree and the Level 3 data products are provided in either the descending (equatorial crossing North to South at 1:30 a.m. local time) or ascending (equatorial crossing South to North at 1:30 p.m. local time) orbit. Note that the data were acquired only until 2016.", "description_simplified": "The AIRS Relative Humidity from Earthdata Search dataset provide global humidity data. These data may aid in urban heat and resilience research.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "multiple datasets available", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search?q=AIRX3std", "strengths": "multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 20, "fields": {"dataset": "AIRS Surface Air Temperature from Earthdata Search", "description": "NASA's Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite gathers infrared energy emitted from Earth's surface and atmosphere globally every day. AIRS data are daily, 8-day, and monthly at 1 degree and the Level 3 data products are provided in either the descending (equatorial crossing North to South at 1:30 a.m. local time) or ascending (equatorial crossing South to North at 1:30 p.m. local time) orbit. When you open the file in HDF format (in a program like Panoply or QGIS), you will see an ascending option and a descending option each with SurfAirTemp.", "description_simplified": "The AIRS Surface Air Temperature from Earthdata Search dataset provides global air surface temperature. AIRS data are daily, 8-day, and monthly at 1 degree. These data may aid in urban heat research.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "difficult to determine differences between datasets included", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search?q=AIRS3ST&fi=AIRS&fs10=Surface%20Temperature&fsm0=Atmospheric%20Temperature&fs20=Air%20Temperature&fst0=Atmosphere", "strengths": "daily temporal resolution", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "daily, 8-day, and monthly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 21, "fields": {"dataset": "ASTER L2 Surface Temperature V003", "description": "The ASTER Surface Kinetic Temperature (AST_08) is generated using the five Thermal Infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 \u00b5m spectral range. It contains surface temperatures at 90 m spatial resolution for the land areas only. Surface kinetic temperature provides a vital input to studies of volcanism, thermal inertia, surface energy, and high-resolution mapping of fires.", "description_simplified": "The ASTER Surface Kinetic Temperature (AST_08) is generated using the five Thermal Infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 \u00b5m spectral range. Surface kinetic temperature provides a vital input to studies of volcanism, thermal inertia, surface energy, and high-resolution mapping of fires.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "only HDF-EOS2 format available", "project": "Terra - Earth Observing System (EOS), Terra", "source_link": "https://dx.doi.org/10.5067/ASTER/AST_08.003", "strengths": "22-year temporal extent", "format": "HDF-EOS2", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "90 meters", "temporal_extent": "2000-03-04 to Present", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 22, "fields": {"dataset": "HLS (Harmonized Landsat and Sentinel) Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0", "description": "The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe\u2019s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.", "description_simplified": "The Harmonized Landsat and Sentinel-2 (HLS) project provides surface reflectance data from the joint NASA/USGS Landsat 8 Satellite. Surface reflectance data may be useful in identifying vegetation coverage in the context of urban heat. The dataset includes global observations every 2-3 days at 30-meter spatial resolution. Data avaliable from 11/28/2015 to now.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "Latency unclear", "project": "", "source_link": "https://dx.doi.org/10.5067/HLS/HLSS30.002", "strengths": "Recent data available", "format": "COG", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2015-11-28 to Present", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 23, "fields": {"dataset": "HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0", "description": "The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe\u2019s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30-meter (m) spatial resolution.", "description_simplified": "The HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 dataset provides surface reflectance (SR) and top of atmosphere (TOA) brightness data from Landsat 8 satellite data.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "Latency unclear", "project": "", "source_link": "https://dx.doi.org/10.5067/HLS/HLSL30.002", "strengths": "Cloud-optimized", "format": "COG", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2013-04-11 to Present", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 24, "fields": {"dataset": "Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Collection 1 V1", "description": "The Landsat Enhanced Thematic Mapper Plus (ETM+) is a sensor carried onboard the Landsat 7 satellite and has acquired images of the Earth nearly continuously since July 1999, with a 16-day repeat cycle. Landsat ETM+ image data consist of eight spectral bands (band designations), with a spatial resolution of 30 meters for bands 1 to 5 and band 7. Resolution for band 6 (thermal infrared) is 60 meters and resolution for band 8 (panchromatic) is 15 meters. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). The Level 0R data product is reformatted raw data.", "description_simplified": "Landsat 7 satellite data provides continuous images from July 1999, repeating every 16 days. Surface reflectance data may be helpful in identifying vegetation coverage in the context of urban heat. Data is provided in its raw format.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search/granules?p=C1427459680-USGS_EROS&pg[0][v]=f&q=landsat%207&tl=1647148623.75!3!!", "strengths": "Multiple datasets available", "format": "varies- multiple datasets included", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies- multiple datasets included"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 25, "fields": {"dataset": "Landsat Collection 2 Surface Temperature", "description": "Landsat surface temperature measures the Earth\u2019s surface temperature in Kelvin and is an important geophysical parameter in global energy balance studies and hydrologic modeling. Surface temperature data are also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.", "description_simplified": "The Landsat Collection 2 Surface Temperature dataset measures Earth\u2019s surface temperature in Kelvin and is important global modeling. Surface temperature data are also useful for monitoring crop and vegetation health, and extreme heat events such as natural disasters (e.g., volcanic eruptions, wildfires), and urban heat island effects.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "Varies", "limitations": "", "project": "", "source_link": "https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature", "strengths": "Multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "image/example available", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 26, "fields": {"dataset": "MERRA-2 Humidity from Earthdata Search", "description": "There are several options available: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface specific humidity, specific humidity at 2 m, and relative humidity.", "description_simplified": "The MERRA-2 Humidity from Earthdata Search dataset provides data on three measureable types of humidity: surface specific humidity, humidity at 2m, and relative humidity. These data may aid in urban heat and resilience research.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "multiple datasets available", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search?q=merra-2&fs10=Surface%20Temperature&fsm0=Atmospheric%20Temperature&fs20=Air%20Temperature&fst0=Atmosphere", "strengths": "multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 27, "fields": {"dataset": "MERRA-2 Temperature from Earthdata Search", "description": "The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface skin temperature, the air temperature at 2 m, and the air temperature at 10 m.", "description_simplified": "The MERRA-2 (Modern-Era Retrospective analysis for Research and Applications) dataset provide air surface temperature data on surface skin temperature, the air temperature at 2m, and the air temperature at 10m. These data may aid in urban heat research.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "many datasets included, can be confusing", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search?q=merra-2&fs10=Surface%20Temperature&fsm0=Atmospheric%20Temperature&fs20=Air%20Temperature&fst0=Atmosphere", "strengths": "hourly temporal resolution", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1980-01-01 ongoing", "temporal_resolution": "1-hourly, 3-hourly, 6-hourly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 28, "fields": {"dataset": "NLDAS Noah Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V2.0 (NLDAS_NOAH0125_MC) at GES DISC", "description": "This monthly climatology data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1.", "description_simplified": "The NLDAS Level 4 Monthly Climatology dataset provides monthly heat and weather-related data. These data may aid in urban heat and resilience research.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data, Uncommon data format", "project": "NLDAS - North American Land Data Assimilation System", "source_link": "https://dx.doi.org/10.5067/QLW535AYJ498", "strengths": "29-year temporal resolution", "format": "GRIB", "geographic_coverage": "United States", "data_visualization": "", "spatial_resolution": "14 kilometers", "temporal_extent": "1981-01-01 to 2020-12-31", "temporal_resolution": "Monthly - < Annual"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 29, "fields": {"dataset": "Terra MODIS Land Surface Temperature from Earthdata Search", "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11A1) Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "description_simplified": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MOD11A1) Version 6.1 datatset provides daily Land Surface Temperature and Emissivity (LST&E) on a global scale. Many Land surface temperature datasets", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "multiple datasets available", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search?q=MOD11&fsm0=Surface%20Radiative%20Properties&fst0=Land%20Surface", "strengths": "multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 30, "fields": {"dataset": "Terra MODIS Land Surface Temperature/3-Band Emissivity", "description": "", "description_simplified": "This link includes multiple MODIS Land surface temperature datasets.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "multiple datasets available", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search?q=MOD21&fp=Terra", "strengths": "multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies- multiple datasets included"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 31, "fields": {"dataset": "VIIRS Land Surface Temperature from Earthdata Search", "description": "", "description_simplified": "This link includes multiple MODIS Land surface temperature datasets.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "multiple datasets available", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search?q=VNP21&fi=VIIRS", "strengths": "multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies- multiple datasets included"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 32, "fields": {"dataset": "VIIRS Land Surface Temperature/3-Band Emissivity", "description": "", "description_simplified": "This link includes multiple MODIS Land surface temperature datasets.", "indicators": "Extreme Heat", "intended_use": "Path C", "latency": "", "limitations": "multiple datasets available", "project": "Urban Heat", "source_link": "https://search.earthdata.nasa.gov/search?q=VNP21", "strengths": "multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies- multiple datasets included"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 33, "fields": {"dataset": "Famine Early Warning Systems Network", "description": "FEWS NET, the Famine Early Warning Systems Network, is a leading provider of early warning and analysis on acute food insecurity around the world. Created in 1985 by the United States Agency for International Development (USAID) in response to devastating famines in East and West Africa, FEWS NET provides unbiased, evidence-based analysis to governments and relief agencies who plan for and respond to humanitarian crises. FEWS NET analyses support resilience and development programming as well. FEWS NET analysts and specialists work with scientists, government ministries, international agencies, and NGOs to track and publicly report on conditions in the world\u2019s most food-insecure countries.", "description_simplified": "The Famine Early Warning Systems Network (FEWS NET) provides early warning and analysis on acute food insecurity around the world. These data may be useful in food insecuriy and response research.", "indicators": "Food Availability", "intended_use": "Path A", "latency": "", "limitations": "", "project": "", "source_link": "https://fews.net/", "strengths": "Multiple datasets available", "format": "Shapefile", "geographic_coverage": "Global", "data_visualization": "visualization available", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 34, "fields": {"dataset": "FLDAS - Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System", "description": "The FLDAS Global model (McNally et al. 2017) is a custom instance of the NASA Land Information System (LIS; http://lis.gsfc.nasa.gov/) that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. Adopting LIS allows FEWS NET to leverage existing land surface models and generate ensembles of soil moisture, ET, and other variables based on multiple meteorological inputs or land surface models. The goal of the FLDAS project is to achieve more effective use of limited available hydroclimatic observations and is designed to be adopted for routine use for FEWS NET decision support.", "description_simplified": "The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) dataset is a custom model incorporating various data and monitoring systems. These data may aid in food insecurity research.", "indicators": "Food Availability", "intended_use": "Path B", "latency": "", "limitations": "", "project": "FEWS NET - FEWS NET Land Data Assimilation System", "source_link": "https://ldas.gsfc.nasa.gov/fldas", "strengths": "Multiple datasets available", "format": "netCDF-3, KMZ, GeoTIFF", "geographic_coverage": "Global", "data_visualization": "visualization available", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 35, "fields": {"dataset": "AIRS O3 data from Earthdata Search", "description": "AIRS measures abundances of trace components in the atmosphere including ozone. Data are available daily (AIRS3STD), over 8 days (AIRS3ST8), or monthly (AIRS3STM). The instrument measures the amount of O3 in the total vertical column profile of the atmosphere (from Earth\u2019s surface to top-of-atmosphere). Data are in HDF format, and can be opened using Panoply.", "description_simplified": "The AIRS dataset measures abundances of trace components in the atmosphere including ozone. Data are available daily (AIRS3STD), over 8 days (AIRS3ST8), or monthly (AIRS3STM). The instrument measures the amount of O3 in the total vertical column profile of the atmosphere (from Earth\u2019s surface to top-of-atmosphere).", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?m=-97.681640625!-20.25439453125!0!1!0!0%2C2&q=AIRS3ST&ok=AIRS3ST&fi=AIRS&fst0=Atmosphere&fs10=Tropospheric%20Ozone&fst1=Atmosphere&fst2=Atmosphere", "strengths": "Multiple datasets available within link", "format": "HDF", "geographic_coverage": "Global", "data_visualization": "Giovanni, Panopoly", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets available", "temporal_resolution": "varies- multiple datasets available"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 36, "fields": {"dataset": "Global Nitrogen Dioxide Monitoring", "description": "NO2 column observations from the Dutch-Finnish Ozone Monitoring Instrument (OMI) and ESA/EU Copernicus Sentinel 5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) are available from October, 2004 and April, 2018, respectively. OMI is a UV-Visible wavelength spectrometer on the polar-orbiting NASA Aura satellite. Aura, launched on 15 July 2004, follows a sun-synchronous orbit with an equator crossing time near 13:45, local time. TROPOMI is a similar instrument with enhanced spatial resolution and additional spectral coverage. It was launched on board the European Space Agency/European Union (ESA/EU) Copernicus Sentinel-5 Precursor satellite on October 13, 2017.", "description_simplified": "The Global Nitrogen Dioxide Monitoring dataset provides imagery of daily nitrogen dioxide from OMI. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "No data download", "project": "Aura - Earth Observing System (EOS), Aura", "source_link": "https://so2.gsfc.nasa.gov/no2/no2_index.html", "strengths": "Images useful for Path A users", "format": "", "geographic_coverage": "Global", "data_visualization": "Map Viewer", "spatial_resolution": "10 kilometers", "temporal_extent": "2015-01-31 to 2020-12-31", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 37, "fields": {"dataset": "MLS/Aura Near-Real-Time L2 Ozone (O3) Mixing Ratio V005", "description": "The MLS Ozone (O3) Mixing Ratio 46hPa (hectopascals) layer is derived from the MLS Ozone product (ML2O3_NRT) available from the Microwave Limb Sounder (MLS) instrument on the Aura satellite. The product indicates ozone levels at the vertical atmospheric pressure level of 46hPa, and is measured in parts per billion by volume (ppbv).The sensor resolution is 5 km, imagery resolution is 2 km, and the temporal resolution is twice daily (day and night). MLS/Aura NRT L2 O3 Mixing Ratio", "description_simplified": "The MLS (Aura) Ozone dataset indicates ozone levels in the atmosphere. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "Only hdf-eos5 available", "project": "LANCE - Land, Atmosphere Near real-time Capability for EOS, Aura - Earth Observing System (EOS), Aura", "source_link": "https://disc.gsfc.nasa.gov/datacollection/ML2O3_NRT_005.html", "strengths": "15 min temporal resolution", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2021-09-21 to present", "temporal_resolution": "1 minute - < 1 hour"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 38, "fields": {"dataset": "OMI/Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column 1-orbit L2 Swath 13x24 km V003", "description": "The Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI \ufb01eld of view speci\ufb01c geometry dependent surface Lambertian Equivalent Re\ufb02ectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (e\ufb00ective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain re\ufb02ectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. ", "description_simplified": "The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "Only recent data included", "project": "Aura - Earth Observing System (EOS), Aura", "source_link": "https://doi.org/10.5067/Aura/OMI/DATA2017", "strengths": "NRT", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2004-10-01 to Present", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 39, "fields": {"dataset": "OMI O3 data from Earthdata Search", "description": "OMI provides daily total column data; data are in HDF5 format, and can be opened using Panoply.", "description_simplified": "The OMI O3 data from Earthdata Search provides daily total air column data for ozone. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?m=-0.0703125!0.140625!2!1!0!0%2C2&q=OMTO3&ok=OMTO3&fi=OMI&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Tropospheric%20Ozone&fst1=Atmosphere&fst2=Atmosphere", "strengths": "Multiple datasets available within link", "format": "HDF5", "geographic_coverage": "Global", "data_visualization": "Giovanni, Worldview, Panopoly", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets available", "temporal_resolution": "varies- multiple datasets available"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 40, "fields": {"dataset": "OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital", "description": "The OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiler Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi NPP satellite. The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions. Additionally, this data product contains measurements of UV aerosol index and reflectivity at 331 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir.", "description_simplified": "The OMPS (Suomi NPP) Ozone dataset provides total ozone measurements. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "", "project": "NPP-JPSS - National Polar Orbiting Partnership-Joint Polar Satellite System", "source_link": "https://dx.doi.org/10.5067/0WF4HAAZ0VHK", "strengths": "NRT", "format": "HDF5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "50 kilometers", "temporal_extent": "2011-11-13 to present", "temporal_resolution": "1 minute - < 1 hour"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 41, "fields": {"dataset": "Global 3-Year Running Mean Ground-Level Nitrogen Dioxide (NO2) Grids from GOME, SCIAMACHY and GOME-2", "description": "The Global 3-Year Running Mean Ground-Level Nitrogen Dioxide (NO2) Grids from GOME, SCIAMACHY and GOME-2 represent a series of three-year running mean grids (1996-2012) of ground level NO2 that were derived from Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment-2 (GOME-2) satellite retrievals. For each satellite-derived NO2 source, the relationship between satellite observations of tropospheric NO2 column densities and the NO2 concentrations at ground level relevant to human exposure is simulated, using the Goddard Earth Observing System chemical transport model (GEOS-Chem) to produce a mean NO2 concentration raster grid. The grid cell resolution is six arc-minutes (0.1 degree, or approximately 10 km at the equator) covering the global land surface.", "description_simplified": "The Global 3-Year Running Mean Ground-Level Nitrogen Dioxide (NO2) Grids from GOME, SCIAMACHY and GOME-2 represent a series of three-year running mean grids (1996-2012) of ground level NO2 that were derived from Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment-2 (GOME-2) satellite retrievals.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data, Large gap in dates, Only 4 years of data available", "project": "SDEI - Satellite-Derived Environmental Indicators", "source_link": "https://doi.org/10.7927/H4JW8BTT", "strengths": "", "format": "GeoTIFF, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "10 kilometers", "temporal_extent": "1996-01-01 to 2012-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 42, "fields": {"dataset": "VIIRS (S-NPP) I Band 375 m Active Fire Product NRT (Vector data)", "description": "The VIIRS 375m I-band fire detections complements the MODIS fire detections; they both show good agreement in hotspot detection but the improved spatial resolution of the 375m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.The thermal anomalies are represented as red points (approximate center of a 375 m pixel).", "description_simplified": "The VIIRS 375m I-band fire detections complements the MODIS fire detections. These data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.The thermal anomalies are represented as red points (approximate center of a 375 m pixel).", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "LANCE - Land, Atmosphere Near real-time Capability for EOS, FIRMS - Fire Information for Resource Management System", "source_link": "https://doi.org/10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002", "strengths": "Daily temporal resolution", "format": "TXT, SHP, KML, WMS", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "375 meters", "temporal_extent": "2016-01-01 to Present", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 43, "fields": {"dataset": "OMI NO2 data from Earthdata Search", "description": "The OMI, aboard the Aura spacecraft, provides daily gridded and non-gridded products at 13x24 km resolution; data are in HDF5 format and can be opened using Panoply. A tutorial on using OMI NO2 data is available as a PDF and a webinar on Analyzing NO2 data within Java and Excel is available from the Earthdata YouTube website.", "description_simplified": "The OMI NO2 data from Earthdata Search dataset provides daily nitrogen dioxide data.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?fi=OMI&fst0=Atmosphere&fsm0=Atmospheric%20Chemistry&fs10=Nitrogen%20Compounds&fs20=Nitrogen%20Dioxide", "strengths": "Multiple datasets available within link", "format": "Varies per dataset", "geographic_coverage": "Global", "data_visualization": "Panopoly", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies- multiple datasets included"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 44, "fields": {"dataset": "TROPOMI NO2 data from Earthdata Search", "description": "The TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel 5, is an ESA Mission. ESA's TROPOMI NO2 provides additional information on this level 2 data product. It is important to note that, because of the very small numbers in tropospheric vertical column of NO2, you will need to change the scaling factor in Panoply (see image from June 2018 to right). Data are in NetCDF format, and can be opened using Panoply.", "description_simplified": "The TROPOMI NO2 data from Earthdata Search dataset provides nitrogen dioxide data. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?fi=TROPOMI&fst0=Atmosphere&fsm0=Atmospheric%20Chemistry&fs10=Nitrogen%20Compounds", "strengths": "Multiple datasets available within link", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies- multiple datasets included"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 45, "fields": {"dataset": "TROPOMI O3 data from Earthdata Search", "description": "ESA TROPOMI O3 provides additional information on this level 2 data product. Data are in NetCDF format, and can be opened using Panoply.", "description_simplified": "The TROPOMI O3 data from Earthdata Search dataset provides ozone data. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?fi=TROPOMI&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Tropospheric%20Ozone", "strengths": "Multiple datasets available within link", "format": "NetCDF", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets available", "temporal_resolution": "varies- multiple datasets available"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 46, "fields": {"dataset": "Global Gridded Relative Deprivation Index (GRDI), Version 1", "description": "The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) data set characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final index raster. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage.", "description_simplified": "The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) data set characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "", "project": "PMP - Poverty Mapping Project", "source_link": "https://dx.doi.org/10.7927/3xxe-ap97", "strengths": "", "format": "GeoTIFF, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "1 kilometer", "temporal_extent": "2010-01-01 to 2020-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 47, "fields": {"dataset": "Gridded Population of the World (GPW), v4", "description": "The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010.", "description_simplified": "The Gridded Population of the World dataset includes basic demographic information such as age, sex, and population density for the year 2010.", "indicators": "Human Dimensions", "intended_use": "Path A", "latency": "", "limitations": "Single time slice (year) available", "project": "GPW - Gridded Population of the World", "source_link": "https://doi.org/10.7927/H46M34XX", "strengths": "Several data formats available", "format": "GeoTIFF, ASCII, netCDF-4, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "SEDAC map widget", "spatial_resolution": "1 kilometer", "temporal_extent": "2010-07-01 00:00:00", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 48, "fields": {"dataset": "Poverty Mapping Project: Small Area Estimates of Poverty and Inequality", "description": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality data set consists of consumption-based poverty, inequality and related measures for subnational administrative units in approximately twenty countries throughout Africa, Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and survey data using small area estimates techniques. The collection of data have been compiled, integrated and standardized from the original data providers into a unified spatially referenced and globally consistent data set. The data products include shapefiles (vector data), tabular data sets (csv format), and centroids (csv file with latitude and longitude of a geographic unit and associated poverty estimates). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with a number of external data providers.", "description_simplified": "The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality dataset shows poverty, inequality, and other related data for approximately twenty countries throughout Afirca, Asia, Europe, North America, and South America. These data may be useful in poverty and inequity research.", "indicators": "Human Dimensions", "intended_use": "Path A", "latency": "", "limitations": "Limited geographic coverage", "project": "PMP - Poverty Mapping Project", "source_link": "https://doi.org/10.7927/H49P2ZKM ", "strengths": "Country-level data that has been integrated and compiled, integrated and standardized into a unified spatially referenced and globally consistent data set", "format": "CSV, Shapefile", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/povmap-small-area-estimates-poverty-inequality/maps", "spatial_resolution": "", "temporal_extent": "1990-01-01 to 2002-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 49, "fields": {"dataset": "Food Insecurity Hotspots Data Set, v1 (2009\u200a\u2013\u200a2019)", "description": "The Food Insecurity Hotspots Data Set consists of grids at 250 meter (~7.2 arc-seconds) resolution that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events. The gridded data are based on subnational food security analysis provided by FEWS NET (Famine Early Warning Systems Network) in five (5) regions, including Central America and the Caribbean, Central Asia, East Africa, Southern Africa, and West Africa. Based on the Integrated Food Security Phase Classification (IPC), food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", "description_simplified": "The Food Insecurity Hotspots Dataset shows the level of intensity and frequency of food insecurity as well as hotspot areas that have experienced consecutive food insecurity events. Food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Limited geographic coverage", "project": "FOOD - Food Security Data Collection", "source_link": "https://doi.org/10.7927/cx02-2587 ", "strengths": "10 years temporal extent", "format": "GeoTIFF, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/food-food-insecurity-hotspots/maps/services", "spatial_resolution": "1 kilometer", "temporal_extent": "2009-01-01 to 2019-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 50, "fields": {"dataset": "Geocoded Disasters (GDIS) Dataset, v1 (1960\u200a\u2013\u200a2018)", "description": "The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).", "description_simplified": "The Geocoded Disasters (GDIS) Dataset includes a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters (CRED) Emergency Events Database (EM_DAT). This dataset may aid disaster preparation and recovery research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "PEND - Natural Disasters", "source_link": "https://doi.org/10.7927/zz3b-8y61 ", "strengths": "Significant temporal extent", "format": "Geopackage, Geodatabase, CSV", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1960-01-01 to 2018-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 51, "fields": {"dataset": "Global 15 x 15 Minute Grids of the Downscaled GDP Based on the SRES B2 Scenario, v1 (1990, 2025)", "description": "The Global 15x15 Minute Grids of the Downscaled Population Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of the downscaled population per unit area (population densities). These global grids were generated using the Country-level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. The 1990 GPW was used as the base distribution and the country-level downscaled projections were used to replace population estimates of 1990 in GPW and 2025. The fractional distribution of the population at each grid cell is the same as the 1990 GPW, sub-nationally. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Global 15 x 15 Minute Grids of the Downscaled GDP Based on the SRES B2 Scenario, v1 (1990, 2025) dataset provides Gross Domestic Product (GDP) estimates globally as well as projections for smaller scale areas within countries. These data may help estimate poverty and economic growth and success.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "projections based on original publishing date", "project": "SDP - Socioeconomic Downscaled Projections", "source_link": "https://doi.org/10.7927/H4HQ3WTH ", "strengths": "global coverage", "format": "ASCII, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "1700 kilometers", "temporal_extent": "1990-01-01, 2025-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 52, "fields": {"dataset": "Global Cyclone Proportional Economic Loss Risk Deciles", "description": "The Global Cyclone Proportional Economic Loss Risk Deciles is a 2.5 minute grid of cyclone hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Global Cyclone Proportional Economic Loss Risk Deciles shows cyclone hazard economic loss as a proportion of Gross Domestic Product (GDP) of an area. The shown rates are calculated based on how often and to what extend the hazard of cyclones poses to area. These data can aid in tropical storm preparation and recovery research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Limited temporal extent", "project": "NDH - Natural Disaster Hotspots", "source_link": "https://doi.org/10.7927/H44F1NNF", "strengths": "SEDAC Map widget available for visualization", "format": "ASCII, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-cyclone-proportional-economic-loss-risk-deciles/maps", "spatial_resolution": "5 kilometers", "temporal_extent": "2000-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 53, "fields": {"dataset": "Global Development Potential Indices, v1 (2016)", "description": "The Global Development Potential Indices (DPIs) data set contains thirteen sector-level DPIs for sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). The DPI for each sector represents land suitability that accounts for both resource potential and development feasibility. Each DPI is a 1-km spatially-explicit, global land suitability map that has been validated using locations of current and planned development, and examined for uncertainty and sensitivity. The DPIs can be used to identify lands with current favorable economic and physical conditions for individual sector expansion and assist in planning for sector and cumulative development across the globe.", "description_simplified": "The Global Development Potential Indices, v1 (2016) dataset provides Development Potential Indices for sectors related to renewable energy, fossil fuels, mining, and agriculture. Each DPI shows how suitable land is for the potential land use sector. These data may aid in urban sprawl and human impacts research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "lacks recent data", "project": "LULC - Land Use and Land Cover", "source_link": "https://doi.org/10.7927/k9t6-gh59 ", "strengths": "GeoTIFF format for GIS", "format": "GeoTIFF, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "3500 kilometers", "temporal_extent": "2016-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 54, "fields": {"dataset": "Global Estimated Net Migration Grids By Decade", "description": "The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of globally consistent data on migration, indirect estimation methods were used. The authors relied on a combination of data on spatial population distribution for four time slices (1970, 1980, 1990, and 2000) and subnational rates of natural increase in order to derive estimates of net migration on a 30 arc-second (~1km) grid cell basis. Net migration was estimated by subtracting the population in time period 2 from the population in time period 1, and then subtracting the natural increase (births minus deaths). The residual was considered to be net migration (in-migrants minus out-migrants). The authors ran 13 geospatial net migration estimation models based on outputs from the same number of imputation runs for urban and rural rates of natural increase.This data set represents the average of those runs. These data are reliable at broad scales of analysis (e.g. ecosystems or regions), but are generally not reliable for local level analyses. The data were produced for the United Kingdom Foresight project on Migration and Global Environmental Change.", "description_simplified": "The Global Estimated Net Migration Grids By Decade provides estimates of overall net migration (in-migration minus out-migration) per decade for the 1970s, 1980s, and 1990s.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "POPDYNAMICS - Population Dynamics", "source_link": "https://doi.org/10.7927/H4319SVC ", "strengths": "30 year temporal extent", "format": "GeoTIFF, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/popdynamics-global-est-net-migration-grids-1970-2000/metadata", "spatial_resolution": "1 kilometer", "temporal_extent": "1970-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 55, "fields": {"dataset": "Global Multihazard Proportional Economic Loss Risk Deciles", "description": "The Global Multihazard Proportional Economic Loss Risks is a 2.5 minute grid of a multihazard-based economic loss risk as a proportion of the economic productivity of the analytical unit, the grid cell. Representation of multihazard risk is not based on a multihazard index but rather on combinations of hazard risk categories, drought, seismic, and hydro. The drought category includes drought only. The seismic category consists of earthquake and volcano hazards. Cyclones, floods, and landslides are included in the hydro category. For each of the six hazards considered, a binary risk surface is constructed utilizing the three most-at-risk deciles of each hazard's global proportional economic loss risks data set (deciles 8-10). Each of the category risk surfaces are constructed by adding all the relevant hazard high-risk surfaces. These categorical risk surfaces are reclassified into binary high-risk surfaces. The combination of the category risk values forms a three digit identifier for determining those locations that are at higher-risk from multihazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Global Multihazard Proportional Economic Loss Risks shows the multihazard-based economic loss risk as a proportion of economic productivity. In other words, several different hazards are combined and a proportion is created with the economic productivity of an area. These hazards include drought, earthquakes, volcanoes, cyclones, landslides, and flood risk.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Limited temporal extent", "project": "NDH - Natural Disaster Hotspots", "source_link": "https://doi.org/10.7927/H4WS8R5B", "strengths": "Visualization available through SEDAC Map widget.", "format": "ASCII, DBF, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-multihazard-proportional-economic-loss-risk-deciles/maps", "spatial_resolution": "5 kilometers", "temporal_extent": "2000-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 56, "fields": {"dataset": "Global Multihazard Total Economic Loss Risk Deciles", "description": "The Global Multihazard Total Economic Loss Risk Deciles is a 2.5 minute grid of global multihazard total economic loss risks. First, for each of the considered hazards (cyclones, droughts, earthquakes, floods, landslides, and volcanoes), subnational distributions of Gross Domestic Product (GDP) are computed using a methodology developed from Sachs et al. (2003). Where applicable, the contributions of subnational units to national GDP estimates, the contribution ratio, are determined using data of varied origin. World Bank Development Indicators are substituted for GDP estimates of varied origin and the subnational GDP is estimated using the fore mentioned contribution ratios. A subnational, per capita GDP is derived and a final GDP estimate per grid cell is made based on grid cell population density. A raw, total economic loss is computed per grid cell using a regional economic loss rate derived from EM-DAT records. To more accurately reflect the confidence surrounding the economic loss estimate, the range of losses are classified into deciles, 10 classes of an approximately equal number of grid cells. A multihazard index is generated by summing the top three deciles of the individual hazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Global Multihazard Proportional Economic Loss Risks shows the total economic loss risk by combining the risk of several types of hazards. These hazards include drought, earthquakes, volcanoes, cyclones, landslides, and flood risk. This dataset may aid disaster preparation and recovery research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Limited temporal extent", "project": "NDH - Natural Disaster Hotspots", "source_link": "https://doi.org/10.7927/H4S180F9", "strengths": "Visualization available through SEDAC Map widget", "format": "ASCII, DBF, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-multihazard-total-economic-loss-risk-deciles/maps", "spatial_resolution": "5 kilometers", "temporal_extent": "2000-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 57, "fields": {"dataset": "Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, v1 (2000\u200a\u2013\u200a2100)", "description": "The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes). Spatial urban land projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts and adaptation. This data set presents a set of global, spatially explicit urban land scenarios that are consistent with the Shared Socioeconomic Pathways (SSPs) to produce an empirically-grounded set of urban land spatial distributions over the 21st century. A data-science approach is used exploiting 15 diverse data sets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations from the Landsat satellite series. The SSPs are developed to support future climate and global change research, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with Special Reports.", "description_simplified": "The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios dataset provides global urban land projections based on the SSP (Shared Socioeconomic Pathways) data. These data may be useful in climate, socioeconomic, environmental, or other related research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Recent years' data are projections from original dataset publishing date", "project": "SSP - Shared Socioeconomic Pathways", "source_link": "https://doi.org/10.7927/nj0x-8y67 ", "strengths": "Includes past data and projections", "format": "GeoTIFF, netCDF-4, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "15 kilometers", "temporal_extent": "2000-01-01 to 21000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 58, "fields": {"dataset": "Global Subnational Prevalence of Child Malnutrition, v1 (1990\u200a\u2013\u200a2002)", "description": "The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition data set consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. Data are reported for the most recent year with subnational information available at the time of development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of underweight children under five (the rate numerator), and a tabular data set of the same and associated data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition dataset provides data related to the malnutrition of children globally. Data are reported for the most recent year. These data may aid poverty and food access research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "PMP - Poverty Mapping Project", "source_link": "https://doi.org/10.7927/H4K64G12 ", "strengths": "12-year temporal extent", "format": "ASCII Grid, Excel, Shapefile, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/povmap-global-subnational-prevalence-child-malnutrition/maps", "spatial_resolution": "", "temporal_extent": "1990-01-01 to 2002-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 59, "fields": {"dataset": "Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence, v1 (2005)", "description": "The West Africa Coastal Vulnerability Mapping: Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence represents the HarvestChoice Subnational Poverty and Extreme Poverty Prevalence data set as a one kilometer raster, and includes values within 200 kilometers of the coast. Poverty levels affect the \"defenselessness\" of populations in the low elevation coastal zone. These data were developed by the Harvest Choice project funded by the Bill and Melinda Gates Foundation. Harvest Choice measured 2005 poverty levels using 2005 purchasing power parity data for two thresholds: $1.25/day and $2/day international poverty lines. The $2/day threshold was selected for this mapping exercise.", "description_simplified": "The Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence, v1 (2005) dataset shows poverty levels based on Harvest Choice Project using purchasing power data with two thresholds: $1.25/day and $2/day international poverty lines.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data, Limited to West Africa", "project": "WACVM - West Africa Coastal Vulnerability Mapping", "source_link": "https://doi.org/10.7927/H44T6G9K ", "strengths": "", "format": "GeoTIFF", "geographic_coverage": "Western Africa", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "2005-01-01 to 2005-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 60, "fields": {"dataset": "IPCC (Intergovernmental Panel on Climate Change) INFORM Global Risk Index 2019 Mid Year, v0.3.7, (2019)", "description": "The INFORM Global Risk Index 2019 Mid Year, v0.3.7 data set identifies the countries at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM Global Risk Index (GRI) model is based on risk concepts published in the scientific literature and envisages three dimensions of risk: Hazard & Exposure, Vulnerability, and Lack of Coping Capacity. The INFORM GRI model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk. The INFORM GRI model supports a proactive crisis management framework, and will be helpful for an objective allocation of resources for disaster management, as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies. Only the two main sections, Vulnerability and Lack of Coping Capacity, not the Hazard & Exposure section, were used in the IPCC AR6.", "description_simplified": "The IPCC (Intergovernmental Panel on Climate Change) INFORM Global Risk Index allows the user to assess country-level vulnerability and coping capacity related to climate change. This idex is based on Chapter 8 of the Sixth Assessment Report (AR6) by the IPCC. This dataset identifies the country high at risk of humanitarian crisis that are more likely to need assistance.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Data only available excel format for 2019", "project": "IPCC - Intergovernmental Panel on Climate Change", "source_link": "https://doi.org/10.7927/yzp7-sm30 ", "strengths": "Visualization available through SEDAC map widget", "format": "Excel", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "", "temporal_extent": "2019-01-01 to 2019-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 61, "fields": {"dataset": "IPCC (Intergovernmental Panel on Climate Change) Socio-Economic Baseline Dataset", "description": "The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset consists of population, human development, economic, water resources, land cover, land use, agriculture, food, energy and biodiversity data . This dataset was collated by IPCC from a variety of sources such as The World Bank, United Nations Environment Programme (UNEP), and Food and Agriculture Organization of the United Nations (FAO), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset combines several factors including population, human development, economic, water resources, land cover, land use, agriculture, food, energy, and biodiversity data. This dataset may aid environmental hazard research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Limited temporal extent", "project": "IPCC - Intergovernmental Panel on Climate Change", "source_link": "https://doi.org/10.7927/H4WM1BB7 ", "strengths": "Many socioeconomic factors included", "format": "Excel", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1990-01-01, 1991-01-01, 1980-01-01, 1992-01-01, 1993-01-01, 1994-01-01, 1995-01-01, 2025-01-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 62, "fields": {"dataset": "Natural Resource Protection and Child Health Indicators, 2021 Release (2010\u200a\u2013\u200a2021)", "description": "The Natural Resource Protection and Child Health Indicators, 2021 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 135 countries and is calculated based on the weighted average percentage of biomes under protected status.", "description_simplified": "The Natural Resource Protection and Child Health Indicators dataset provides indicators of natural resource protection and child health that complement the governance, social, and economic indicators used by MCC (Millenium Challenge Corporation) as country selection criteria.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Only available in excel format", "project": "NRMI - Natural Resource Management Index", "source_link": "https://doi.org/10.7927/5bbs-e174 ", "strengths": "Includes recent data", "format": "Excel", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "", "temporal_extent": "2010-01-01 to 2021-11-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 63, "fields": {"dataset": "US Social Vulnerability Index", "description": "The U.S. Social Vulnerability Index Grids data set contains gridded layers for the overall Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) using four sub-category themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) based on census tract level inputs from 15 variables for the years 2000, 2010, 2014, 2016, and 2018. SVI values range between 0 and 1 based on their percentile position among all census tracts in the U.S., with 0 representing lowest vulnerability census tracts and 1 representing highest vulnerability census tracts. SEDAC has gridded these vector inputs to create 1 km spatial resolution raster surfaces allowing users to obtain vulnerability metrics for any user-defined area within the U.S. Utilizing inputs from CIESIN's Gridded Population of the World, Version 4, Revision 11 (GPWv4.11), a mask is applied for water, and optionally, for no population. The data are provided in two different projection formats, NAD83 as a U.S. specific standard, and WGS84 as a global standard. The goal of the SVI is to help identify vulnerable communities by ranking them on these inputs across the U.S.", "description_simplified": "The U.S. Social Vulnerability Index Grids identifies socially vulnerable populations at higher risk due to four main factors: socioeconomic status, household composition & disability, minority status & language, and housing type & transportation. These data are produced by the CDC (Centers for Disease Control and Prevention based on census data from the years 200, 2010, 2014, 2016, and 2018.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "USCG - U.S. Census Grids", "source_link": "https://doi.org/10.7927/6s2a-9r49 ", "strengths": "Temporal extent varies", "format": "GeoTIFF, PDF, PNG", "geographic_coverage": "United States", "data_visualization": "", "spatial_resolution": "1 kilometer", "temporal_extent": "2000-01-01, 2010-01-01, 2014-01-01, 2016-01-01, 2018-01-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 64, "fields": {"dataset": "Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019", "description": "To provide a literature database tracking the use of a global scenarios framework consisting of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), for climate, socioeconomic, environmental, and other related research. The Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019 consists of biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019 that used the SSPs.", "description_simplified": "The Shared Socioeconomic Pathways (SSPs) dataset compiles biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019, generated from a Google Scholar search. This dataset may aid in climate, socioeconomic, environmental, and other related research.", "indicators": "Human Dimensions", "intended_use": "Path C", "latency": "", "limitations": "Compiled literature data, Not spatial data", "project": "SSP - Shared Socioeconomic Pathways", "source_link": "https://doi.org/10.7927/hn96-9703", "strengths": "Significant temporal extent", "format": "Excel", "geographic_coverage": "", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "2014-01-01 to 2019-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 65, "fields": {"dataset": "Global Flood Hazard Frequency and Distribution", "description": "The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Global Flood Hazard Frequency and Distribution compiles data from extreme flood events between 1985 and 2003. Each grid is assigned a value based on the frequency of floods in that area. The higher the number, the more frequent flood events are in that area.", "indicators": "Urban Flooding", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "NDH - Natural Disaster Hotspots", "source_link": "https://doi.org/10.7927/H4668B3D ", "strengths": "18 years temporal extent", "format": "ASCII, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution/maps", "spatial_resolution": "1 meter", "temporal_extent": "1985-01-01 to 2003-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 66, "fields": {"dataset": "Global Precipitation Measurement Data Directory", "description": "Precipitation data from the GPM and TRMM missions are made available free to the public in a variety of formats from several sources at NASA Goddard Space Flight Center. This section outlines the different types of data available, the levels of processing, the sources to download the data, and some helpful tips for utilizing precipitation data in your research.", "description_simplified": "The Global Precipitation Measurement Data Directory includes various datasets related to precipitation and atmospheric monitoring.", "indicators": "Urban Flooding", "intended_use": "Path B", "latency": "Varies", "limitations": "", "project": "GPM - Global Precipitation Measurement", "source_link": "https://gpm.nasa.gov/data/directory", "strengths": "Multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "visualization avaible, varies per dataset", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 67, "fields": {"dataset": "NASA's Precipitation Processing System", "description": "The Precipitation Processing System (PPS) evolved from the Tropical Rainfall Measuring Mission (TRMM) Science Data and Information System (TSDIS). The purpose of the PPS is to process, analyze and archive data from the Global Precipitation Measurement (GPM) mission, partner satellites and the TRMM mission. The PPS also supports TRMM by providing validation products from TRMM ground radar sites. All GPM, TRMM and Partner public data products are available to the science community and the general public from the TRMM/GPM FTPS and HTTPS Data Archives.", "description_simplified": "The Precipitation Processing System (PPS) processes, combines, analyzes, and archives data from other past and current precipitation datasets. Data can be used to track urban flooding, overall precipitation and drought trends, and significant storm events.", "indicators": "Urban Flooding", "intended_use": "Path B", "latency": "Varies", "limitations": "", "project": "GPM - Global Precipitation Measurement", "source_link": "https://arthurhou.pps.eosdis.nasa.gov/", "strengths": "Several tools and datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "Several Available", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 68, "fields": {"dataset": "Urban-Rural Population and Land Area Estimates, v3 (1990, 2000, 2015)", "description": "The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications.", "description_simplified": "The Urban-Rural Population and Land Area Estimates dataset provides estimates of urban and rural populations and land areas for the years 1990, 2000 and 2015 for 234 countries and other areas with coastal elevations of no more than 5m above sea level. These data may be useful in tracking urbanareas at risk of coastal hazards.", "indicators": "Urban Flooding", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "LECZ - Low Elevation Coastal Zone", "source_link": "https://doi.org/10.7927/d1x1-d702", "strengths": "Includes data for 234 countries", "format": "GeoTIFF, XLSM", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "", "temporal_extent": "1990-01-01, 2000-01-01, 2015-01-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 69, "fields": {"dataset": "JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height Coastal Resolution Improvement (CRI) Filtered Release 06.1 Version 03", "description": "This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.1Mv03). This version of the data employs a Coastal Resolution Improvement (CRI) filter that reduces signal leakage errors across coastlines. These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. The water storage/height anomalies are given in equivalent water thickness units (cm).", "description_simplified": "This dataset includes monthly global water storage/height to be used in studying groundwater availability. These data may aid in freshwater availability and access research.", "indicators": "Water Availability", "intended_use": "Path C", "latency": "", "limitations": "Only netCDF available", "project": "GRACE - Gravity Recovery and Climate Experiment, GRACE-FO - Gravity Recovery and Climate Experiment Follow-On", "source_link": "https://dx.doi.org/10.5067/TEMSC-3JC63", "strengths": "Cloud-optimized", "format": "netCDF-4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "55 kilometers", "temporal_extent": "2002-04-04 to Present", "temporal_resolution": "Monthly - < Annual"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 70, "fields": {"dataset": "NASA's Black Marble Night Lights Disaster Recovery in Puerto Rico Visualization", "description": "NASA's Black Marble night lights data product from the NASA/NOAA Suomi National Polar-orbiting Partnership satellite with USGS-NASA Landsat data and Google's OpenStreetMap were combined to develop a neighborhood-scale map of energy use in communities across Puerto Rico and New Orleans as the electricity grid was slowly restored after Hurrican Maria and Hurricane Ida. They then analyzed the relationship between restoration rates in terms of days without electricity and the remoteness of communities from major cities.", "description_simplified": "NASA's Black Marble night lights data provide a neighborhood-scale map of energy use in communities in Puerto Rico and New Orleans as the electricity grid was slowly restored after Hurricane Maria and Hurricane Ida.", "indicators": "Disasters", "intended_use": "Path A", "latency": "", "limitations": "Not for data download", "project": "", "source_link": "https://svs.gsfc.nasa.gov/4658#:~:text=NASA%27s%20Black%20Marble%20night%20lights%20used%20to%20examine%20disaster%20recovery%20in%20Puerto%20Rico,-Visualizations%20by%20Kel&text=At%20night%2C%20Earth%20is%20lit,Puerto%20Rico%27s%20lights%20went%20out.", "strengths": "Specific examples and visualizations given for users", "format": "TIFF, JPEG, MPEG", "geographic_coverage": "Puerto Rico, New Orleans", "data_visualization": "Images", "spatial_resolution": "", "temporal_extent": "Varies", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 71, "fields": {"dataset": "NASA Flood Dashboard", "description": "The Flood Dashboard brings together multiple NASA soil moisture and flood products with products from the National Weather Service and USGS to give a more complete picture of potential flooding in the United States. Includes data from Soil Moisture Map, USGS Stream Gauges, and MODIS Flood Maps.", "description_simplified": "The Flood Dashboard brings together mutliple NASA, NWS, and USGS soil moisture and flood products to provide insight to flood potential across the globe.", "indicators": "Urban Flooding", "intended_use": "Path A", "latency": "1 Day", "limitations": "Does not display data in real-time", "project": "", "source_link": "https://maps.disasters.nasa.gov/arcgis/apps/opsdashboard/index.html#/a70a27ff74f94fa9a23123b58b3ee613", "strengths": "Interactive dashboard with visualizations of data from various sources", "format": "Interactive Visualization", "geographic_coverage": "Global", "data_visualization": "Interactive Mapper", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 72, "fields": {"dataset": "Maps of Subsidence in New Orleans", "description": "Through a combination of airborne radar and ground-based GPS, a research team has developed detailed models of how much land is sinking and rising in southern Louisiana.", "description_simplified": "Through a combination of airborne radar and ground-based GPS, a research team has developed detailed models of how much land is sinking and rising in southern Louisiana.", "indicators": "Urban Flooding", "intended_use": "Path A", "latency": "", "limitations": "Not for data download", "project": "", "source_link": "https://visibleearth.nasa.gov/images/88078/scientists-improve-maps-of-subsidence-in-new-orleans?size=large", "strengths": "Images for easy visualization", "format": "PNG", "geographic_coverage": "Louisiana", "data_visualization": "Images", "spatial_resolution": "", "temporal_extent": "2009-06-01 to 2012-07-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 73, "fields": {"dataset": "Global Cyclone Total Economic Loss Risk Deciles", "description": "The Global Cyclone Total Economic Loss Risk Deciles is a 2.5 minute grid of global cyclone total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national GDP is spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using population distribution data. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell as determined from Gridded Population of the World, Version 3 (GPWv3) data. Once a GDP value is determined on a per grid cell basis, then the regionally variable loss rate, as derived from the historical records of EM-DAT, is used to determine the total economic loss risks posed to a grid cell by cyclone hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Global Cyclone Proportional Economic Loss Risk Deciles shows the total global cyclone economic loss using the Gross Domestic Product (GDP) of an area. These data can aid in tropical storm preparation and recovery research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Limited temporal extent", "project": "NDH - Natural Disaster Hotspots", "source_link": "https://doi.org/10.7927/H40P0WXQ", "strengths": "Visualization available through SEDAC Map widget", "format": "ASCII, DBF, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-cyclone-total-economic-loss-risk-deciles/maps", "spatial_resolution": "1 meter", "temporal_extent": "2000-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 74, "fields": {"dataset": "Global Flood Proportional Economic Loss Risk Deciles", "description": "The Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Global Flood Proportional Economic Loss Risk Deciles shows flood hazard economic loss as a proportion of Gross Domestic Product (GDP) of an area. These data can aid in storm preparation and recovery research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Limited temporal extent", "project": "NDH - Natural Disaster Hotspots", "source_link": "https://doi.org/10.7927/H4XS5S9Q", "strengths": "Visualization available through SEDAC Map widget", "format": "ASCII, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-flood-proportional-economic-loss-risk-deciles/maps", "spatial_resolution": "1 meter", "temporal_extent": "2000-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 75, "fields": {"dataset": "Global Flood Total Economic Loss Risk Deciles", "description": "The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Global Flood Proportional Economic Loss Risk Deciles shows the total global flood economic loss using the Gross Domestic Product (GDP) of an area. These data can aid in storm preparation and recovery research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Limited temporal extent", "project": "NDH - Natural Disaster Hotspots", "source_link": "https://doi.org/10.7927/H4T151KP", "strengths": "Visualization available through SEDAC Map widget", "format": "ASCII, DBF, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ndh-flood-total-economic-loss-risk-deciles/maps", "spatial_resolution": "1 meter", "temporal_extent": "2000-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 76, "fields": {"dataset": "Geocoded Disasters (GDIS) Dataset", "description": "The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).", "description_simplified": "The Geocoded Disasters (GDIS) Dataset includes a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters (CRED) Emergency Events Database (EM_DAT). The data included come from 39,953 locations for 9,924 disasters that happened across the world from 1960-2018.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "PEND - Natural Disasters", "source_link": "https://doi.org/10.7927/zz3b-8y61", "strengths": "Significant temporal extent", "format": "Geodatabase, GeoPackage, CSV, RData", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/pend-gdis-1960-2018/maps", "spatial_resolution": "", "temporal_extent": "1960-01-01 to 2018-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 77, "fields": {"dataset": "VIIRS (Visual Infrared Imaging Radiometer Suite) Plus DMSP (Defense Meteorological Satellite System) Change in Lights", "description": "The VIIRS Plus DMSP Change in Lights (VIIRS+DMSP dLIGHT) data set fuses nighttime lights imagery from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) with a stable night light composite from the next generation Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band to map the spatial distribution and temporal evolution of global nighttime lights between 1992 and 2015. The product visualizes changes in both brightness and extent of nocturnal low lights over two decades while minimizing the spatial overextent (overglow) and bright saturation that compromise the DMSP-OLS composites.", "description_simplified": "The VIIRS (Visual Infrared Imaging Radiometer Suite) Plus DMSP (Defense Meteorological Satellite System) Change in Lights dataset allows users to visualize changes in brightness and the overall extent of global nighttime lights over a period of two decades. These data are useful in studying urbanization or disaster recovery.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Temporal extent only includes 3 years", "project": "SDEI - Satellite-Derived Environmental Indicators", "source_link": "https://dx.doi.org/10.7927/9ryj-6467", "strengths": "Visualization available through SEDAC map widget", "format": "GeoTIFF", "geographic_coverage": "Global", "data_visualization": "SEDAC map widget", "spatial_resolution": "", "temporal_extent": "1992-01-01, 2002-01-01, 2013-01-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 78, "fields": {"dataset": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3", "description": "The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper \"Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter\" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR).", "description_simplified": "The Urban-Rural Population and Land Area Estimates dataset provides estimates of urban and rural populations and land areas for the years 1990, 2000 and 2015 for 234 countries and other areas with coastal elevations of no more than 5m above sea level. These data may be useful in tracking urban areas at risk of coastal hazards. ", "indicators": "Urban Flooding", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "LECZ - Low Elevation Coastal Zone", "source_link": "https://doi.org/10.7927/d1x1-d702 ", "strengths": "Includes data for 234 countries", "format": "GeoTIFF, XLSM", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v3/maps", "spatial_resolution": "1 kilometer", "temporal_extent": "1990-01-01, 2000-01-01 2015-01-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 79, "fields": {"dataset": "NASADEM (NASA Digital Elevation Model) Topography Data", "description": "NASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days. In addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 2 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling. NASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass.", "description_simplified": "NASA Digital Elevation Model (NASADEM) Topography Data provide a digital elevation model of all land between 60 degrees north and 56 degrees south, nearly 80% of Earth's landmass. Topography data can be useful in urban flooding and hurricane research.", "indicators": "Urban Flooding", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "", "source_link": "https://lpdaac.usgs.gov/news/release-nasadem-data-products/", "strengths": "High resolution and large geographic coverage", "format": "", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2000-02-11 to 2000-02-21", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 80, "fields": {"dataset": "NLDAS Noah Land Surface Model L4 Hourly 0.125 x 0.125 degree V2.0", "description": "Runoff after storm events can impact the amount of water entering a channel or water body. Satellites cannot measure runoff directly but information that can be used to assess predicted runoff can be measured using remote sensing. These data are input, along with ground-based data, into atmosphere-land models from LDAS to estimate runoff. Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni.", "description_simplified": "Satellites cannot measure surface runoff directly but information that can be used to assess predicted runoff can be measured using remote sensing. These data products can be visualized many different ways and aid in natural disaster research.", "indicators": "Urban Flooding", "intended_use": "Path C", "latency": "", "limitations": "Giovanni requires experience", "project": "NLDAS - North American Land Data Assimilation System", "source_link": "https://doi.org/10.5067/T4OW83T8EXDO", "strengths": "Includes very recent data, up to a few weeks before current date. Visualizations available in Giovanni", "format": "netCDF-3", "geographic_coverage": "North America", "data_visualization": "Giovanni", "spatial_resolution": "1.5 kilometers", "temporal_extent": "1979-01-02 to Present", "temporal_resolution": "Hourly - < Daily"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 81, "fields": {"dataset": "ARIA (Advanced Rapid Imaging and Analysis) Data Products", "description": "The ARIA Project, a joint effort of the California Institute of Technology and NASA's Jet Propulsion Laboratory, is developing the infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response.", "description_simplified": "The ARIA Project, a joint effort of the California Institute of Technology and NASA's Jet Propulsion Laboratory, is developing the infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response. These data can aid in natural disaster research.", "indicators": "Disasters", "intended_use": "Path C", "latency": "NRT", "limitations": "No visualization available", "project": "ARIA - Advanced Rapid Imaging and Analysis", "source_link": "https://aria-share.jpl.nasa.gov/", "strengths": "Near real time data to aid natural disaster response and preparation", "format": "GeoTIFF, KMZ", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 82, "fields": {"dataset": "Berkeley Mapping for EJ", "description": "Mapping dashboard showing cumulative impacts of environmental, public health, and socioeconomics disparities in one EJ indicator. Mapping available only for Virginia and Colorado currently. ", "description_simplified": "Mapping dashboard showing cumulative impacts of environmental, public health, and socioeconomics disparities in one EJ indicator. Mapping available only for Virginia and Colorado currently. ", "indicators": "Combination", "intended_use": "Path A", "latency": "", "limitations": "Data download not easily accessible", "project": "Other", "source_link": "https://mappingforej.berkeley.edu/", "strengths": "Very user-friendly visualization", "format": "", "geographic_coverage": "Virgina and Colorado, USA", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 83, "fields": {"dataset": "American Forests Tree Equity Score", "description": "Mapping tool that assigns a \"tree equity score\" (TES) for each census block. TES is derived by culimating data on existing tree canopy, population density, income, employment, surface temperature, race, age, and health to provide a score between 1 and 100, with 100 being achieved Tree Equity. ", "description_simplified": "Mapping tool that assigns a \"tree equity score\" (TES) for each census block. TES is derived by culimating data on existing tree canopy, population density, income, employment, surface temperature, race, age, and health to provide a score between 1 and 100, with 100 being achieved Tree Equity. ", "indicators": "Combination", "intended_use": "Path A", "latency": "", "limitations": "", "project": "Other", "source_link": "https://www.treeequityscore.org/", "strengths": "Data download easy to access", "format": "shapefile", "geographic_coverage": "TES Mapper: United States; TES Analyzer: Rhode Island", "data_visualization": "", "spatial_resolution": "varies", "temporal_extent": "", "temporal_resolution": "annual"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 84, "fields": {"dataset": "EPA EJScreen", "description": "EPA mapping tool with environmental and demographic data per census block area. Tool can show mapping of one specific indicator, or side-by-side viewing. Metadata available for download. ", "description_simplified": "EPA mapping tool with environmental and demographic data per census block area. Tool can show mapping of one specific indicator, or side-by-side viewing. Metadata available for download. ", "indicators": "Disasters", "intended_use": "Path A", "latency": "", "limitations": "Lacks finer resolution", "project": "Other", "source_link": "https://ejscreen.epa.gov/mapper/", "strengths": "User-friendly interface", "format": "csv", "geographic_coverage": "United States and territories", "data_visualization": "", "spatial_resolution": "census tract", "temporal_extent": "2019", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 85, "fields": {"dataset": "State of Global Air 2020", "description": "developed as part of the Institute for Health Metrics and Evaluation\u2019s (IHME) annual Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), provides an interactive tool to view and compare the latest air pollution and health data, create custom maps and graphs, and download the images and data.", "description_simplified": "developed as part of the Institute for Health Metrics and Evaluation\u2019s (IHME) annual Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), provides an interactive tool to view and compare the latest air pollution and health data, create custom maps and graphs, and download the images and data.", "indicators": "Health & Air Quality", "intended_use": "Path A", "latency": "", "limitations": "Lacks finer resolution", "project": "Other", "source_link": "https://www.stateofglobalair.org/data/#/air/plot", "strengths": "Graphs and maps provided", "format": "PNG, JPEG, CSV", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "Countries/regions", "temporal_extent": "2019", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 86, "fields": {"dataset": "AirNow", "description": "The Environmental Protection Agency\u2019s ground-based PM and Ozone combined Air Quality Index (AQI)", "description_simplified": "The Environmental Protection Agency\u2019s ground-based PM and Ozone combined Air Quality Index (AQI)", "indicators": "Health & Air Quality", "intended_use": "Path A", "latency": "NRT", "limitations": "", "project": "Other", "source_link": "https://www.airnow.gov/?city=Brunswick&state=GA&country=USA", "strengths": "Interactive map with current and past data", "format": "", "geographic_coverage": "United States and territories", "data_visualization": "", "spatial_resolution": "city/individual air monitor", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 87, "fields": {"dataset": "AirNow International", "description": "international program for AQI, with information provided from partnering organizations", "description_simplified": "international program for AQI, with information provided from partnering organizations", "indicators": "Health & Air Quality", "intended_use": "Path A", "latency": "NRT", "limitations": "", "project": "Other", "source_link": "https://www.airnow.gov/index.cfm?action=airnow.international", "strengths": "Interactive map with current and past data", "format": "", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "city/individual air monitor", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 88, "fields": {"dataset": "National Air Quality: Status and Trends of Key Air Pollutants", "description": "Trends on a national and regional level are available through the EPA\u2019s Air Quality Trends.", "description_simplified": "Trends on a national and regional level are available through the EPA\u2019s Air Quality Trends.", "indicators": "Health & Air Quality", "intended_use": "Path A", "latency": "", "limitations": "No data download", "project": "Other", "source_link": "https://www.epa.gov/air-trends", "strengths": "Graphed data provided", "format": "Excel, html", "geographic_coverage": "National/regional", "data_visualization": "", "spatial_resolution": "Country", "temporal_extent": "", "temporal_resolution": "annual"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 89, "fields": {"dataset": "Explore 19 Years of Global Air Quality in Living Atlas", "description": "Investigate how global air quality patterns have changed over time, and how poor air quality impacts the human population with a new layer added to ArcGIS Living Atlas of the World. The feature layer contains aggregated particulate matter 2.5 (PM 2.5) concentrations offered by NASA\u2019s Socioeconomic Data and Applications Center (SEDAC) at multiple geography levels:", "description_simplified": "Investigate how global air quality patterns have changed over time, and how poor air quality impacts the human population with a new layer added to ArcGIS Living Atlas of the World. The feature layer contains aggregated particulate matter 2.5 (PM 2.5) concentrations offered by NASA\u2019s Socioeconomic Data and Applications Center (SEDAC) at multiple geography levels:", "indicators": "Health & Air Quality", "intended_use": "Path A", "latency": "", "limitations": "", "project": "Other", "source_link": "https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/mapping/explore-19-years-of-global-air-quality-in-living-atlas/", "strengths": "Interactive ESRI mapper", "format": "shapefile", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 90, "fields": {"dataset": "Global Burden of Disease (GBD)", "description": "Global Burden of Diseases, Injuries, and Risk Factors Study, out of the Institute for Health Metrics and Evaluation (IHME) is an independent population health research center at the University of Washington that provides rigorous and comparable measurement of the world's most important health problems and evaluates the strategies used to address them.", "description_simplified": "Global Burden of Diseases, Injuries, and Risk Factors Study, out of the Institute for Health Metrics and Evaluation (IHME) is an independent population health research center at the University of Washington that provides rigorous and comparable measurement of the world's most important health problems and evaluates the strategies used to address them.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "varies", "limitations": "Site and tools can be confusing", "project": "Other", "source_link": "http://www.healthdata.org/gbd", "strengths": "Several data visualizations available", "format": "", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "varies- multiple datasets available", "temporal_extent": "varies", "temporal_resolution": "varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 91, "fields": {"dataset": "Maryland's Environmental Justice Screening App", "description": "The Maryland Environmental Justice Screen Tool (MD EJSCREEN) assesses environmental justice risks among census tracts in the state of Maryland. Developed by the Community Engagement, Environmental Justice, and Health Laboratory at the University of Maryland School of Public Health, this tool combines the average pollution burden of a community with the average population demographic characteristics to produce an Environmental Justice (EJ) score.1 Stakeholders advocacy resulted in the inclusion of six indicators of EJ risk specific to Maryland: asthma, emergency room discharges, percent non-White, proximity to treatment, storage and disposal facilities, myocardial infarction discharges, low birth weight infants, and particulate matter. Through this tool, Maryland residents can be better informed of disparities in EJ risk among different communities and their associated health impacts.", "description_simplified": "The Maryland Environmental Justice Screen Tool (MD EJSCREEN) assesses environmental justice risks among census tracts in the state of Maryland. Developed by the Community Engagement, Environmental Justice, and Health Laboratory at the University of Maryland School of Public Health, this tool combines the average pollution burden of a community with the average population demographic characteristics to produce an Environmental Justice (EJ) score.1 Stakeholders advocacy resulted in the inclusion of six indicators of EJ risk specific to Maryland: asthma, emergency room discharges, percent non-White, proximity to treatment, storage and disposal facilities, myocardial infarction discharges, low birth weight infants, and particulate matter. Through this tool, Maryland residents can be better informed of disparities in EJ risk among different communities and their associated health impacts.", "indicators": "Combination", "intended_use": "Path A", "latency": "", "limitations": "Data download not easily accessible", "project": "Other", "source_link": "https://p1.cgis.umd.edu/ejscreen/", "strengths": "Interactive mapper with different layers, available in ArcGIS mapper", "format": "shapefile", "geographic_coverage": "Maryland", "data_visualization": "", "spatial_resolution": "census tracts/ county lines", "temporal_extent": "", "temporal_resolution": "annual"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 92, "fields": {"dataset": "Illinois Solar for All\u2019s Environmental Justice Communities", "description": "Communities (in orange) were designated as such through a calculation utilizing the US EPA tool, EJ Screen, and demonstrates a higher risk of exposure to pollution based on environmental and socioeconomic factors. The communities in blue are those that were created through the self designation process. Specific questions can be directed to: info@illinoisSFA.com", "description_simplified": "Communities (in orange) were designated as such through a calculation utilizing the US EPA tool, EJ Screen, and demonstrates a higher risk of exposure to pollution based on environmental and socioeconomic factors. The communities in blue are those that were created through the self designation process. Specific questions can be directed to: info@illinoisSFA.com", "indicators": "Combination", "intended_use": "Path A", "latency": "", "limitations": "Data download not easily accessible", "project": "Other", "source_link": "https://elevate.maps.arcgis.com/apps/webappviewer/index.html?id=cfd020c99ed844668450c6b77eacb411", "strengths": "Interactive tool ", "format": "shapefile", "geographic_coverage": "Illinois", "data_visualization": "", "spatial_resolution": "census tracts", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 93, "fields": {"dataset": "University of Michigan Screening Tool for Environmental Justice", "description": "A beta-version of an environmental justice screening tool for the state of Michigan.", "description_simplified": "A beta-version of an environmental justice screening tool for the state of Michigan.", "indicators": "Combination", "intended_use": "Path A", "latency": "", "limitations": "Data download not easily accessible", "project": "Other", "source_link": "https://umich.maps.arcgis.com/apps/webappviewer/index.html?id=dc4f0647dda34959963488d3f519fd24", "strengths": "Interactive tool ", "format": "shapefile", "geographic_coverage": "Michican", "data_visualization": "", "spatial_resolution": "census tracts", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 94, "fields": {"dataset": "CalEnviroScreen", "description": "California\u2019s CalEnviroScreen was created in 2013 and is currently in its 3rd version (released 2018). The map is used in key decision-making throughout the state, including targeting investment of proceeds from California\u2019s cap-and-trade program (AB 32).", "description_simplified": "California\u2019s CalEnviroScreen was created in 2013 and is currently in its 3rd version (released 2018). The map is used in key decision-making throughout the state, including targeting investment of proceeds from California\u2019s cap-and-trade program (AB 32).", "indicators": "Combination", "intended_use": "Path A", "latency": "", "limitations": "Data download not easily accessible", "project": "Other", "source_link": "https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40", "strengths": "Interactive tool ", "format": "shapefile", "geographic_coverage": "California", "data_visualization": "", "spatial_resolution": "census tracts", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 95, "fields": {"dataset": "Washington\u2019s Environmental Health Disparities Map", "description": "Washington\u2019s Environmental Health Disparities Map was created in 2018 and modelled after CalEnviroScreen to provide a cumulative environmental health impact score for each census tract reflecting pollutant exposures and factors that affect people\u2019s vulnerability to environmental pollution.", "description_simplified": "Washington\u2019s Environmental Health Disparities Map was created in 2018 and modelled after CalEnviroScreen to provide a cumulative environmental health impact score for each census tract reflecting pollutant exposures and factors that affect people\u2019s vulnerability to environmental pollution.", "indicators": "Combination", "intended_use": "Path A", "latency": "", "limitations": "Data download not easily accessible", "project": "Other", "source_link": "https://fortress.wa.gov/doh/wtn/WTNIBL/", "strengths": "Interactive tool ", "format": "shapefile", "geographic_coverage": "Washington", "data_visualization": "", "spatial_resolution": "census tracts", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 96, "fields": {"dataset": "Purple Air", "description": "PurpleAir makes sensors that empower community scientists who collect hyper-local air quality data and share it with the public.", "description_simplified": "PurpleAir makes sensors that empower community scientists who collect hyper-local air quality data and share it with the public.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "Data only available in JSON format", "project": "Other", "source_link": "https://map.purpleair.com/1/mAQI/a10/p604800/cC0#8.23/38.904/-77.169", "strengths": "Interactive map and graphing tool", "format": "JSON", "geographic_coverage": "Global, but limited outside US", "data_visualization": "", "spatial_resolution": "individual sensors", "temporal_extent": "", "temporal_resolution": "NRT"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 97, "fields": {"dataset": "ECOSTRESS Cloud Mask Daily L2 Global 70m V001", "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. The ECO2CLD Version 1 data product provides a cloud mask that can be used to determine cloud cover for the ECO1BRAD, ECO2LSTE, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The ECOSTRESS Level 2 cloud product is derived using the five calibrated thermal bands in a multispectral cloud-conservative thresholding approach.", "description_simplified": "The ECO2CLD Version 1 data product provides a cloud mask that can be used to determine cloud cover for the ECO1BRAD, ECO2LSTE, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products.", "indicators": "Extreme Heat", "intended_use": "Path B", "latency": "", "limitations": "Latency unclear", "project": "ECOSTRESS - ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station", "source_link": "https://dx.doi.org/10.5067/ECOSTRESS/ECO2CLD.001", "strengths": "Recent data available", "format": "HDF5", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "70 meters", "temporal_extent": "2018-07-09 to Present", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 98, "fields": {"dataset": "Global Man-made Impervious Surface (GMIS) Dataset From Landsat, v1 (2010)", "description": "The Global Man-made Impervious Surface (GMIS) Dataset From Landsat consists of global estimates of fractional impervious cover derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. The GMIS dataset consists of two components: 1) global percent of impervious cover; and 2) per-pixel associated uncertainty for the global impervious cover. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. The spatial extent covers the entire globe except Antarctica and some small islands. This dataset is one of the first global, 30m datasets of man-made impervious cover to be derived from the GLS data for 2010 and is a companion dataset to the Global Human Built-up And Settlement Extent (HBASE) dataset. The dataset is expected to have a rather broad spectrum of users, from those wishing to examine/study the fine details of urban land cover over the globe at full 30m resolution to global modelers trying to understand the climate/environmental impacts of man-made surfaces at continental to global scales. For example, the data are applicable to local modeling studies of urban impacts on the energy, water, and carbon cycles, as well as analyses at the individual country level.", "description_simplified": "The Global Man-made Impervious Surface (GMIS) Dataset From Landsat consists of global estimates of fractional impervious cover derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. These data show the extent of land cover that does not absorb water such as concrete and roads. These data may aid in urban flooding and urban heat research. ", "indicators": "Extreme Heat", "intended_use": "Path B", "latency": "", "limitations": "lacks recent data, only gmis format available", "project": "ULANDSAT - Global High Resolution Urban Data from Landsat", "source_link": "https://doi.org/10.7927/H4P55KKF ", "strengths": "makes landsat data more user-friendly", "format": "GeoTIFF, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/ulandsat-gmis-v1/maps", "spatial_resolution": "30 meters", "temporal_extent": "2010-01-01 to 2010-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 99, "fields": {"dataset": "U.S. Census Grids (Summary File 1), 2010", "description": "The U.S. Census Grids (Summary File 1), 2010 data set contains grids of demographic and socioeconomic data from the year 2010 in ASCII and GeoTIFF formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2010 TIGER/Line Files and census variables (population, households, and housing variables).", "description_simplified": "The US Census Grids 2010 provide gridded demographic data, including age, race, ethnicity, and housing for the US and Puerto Rico.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Data only available for the year 2010", "project": "USCG - U.S. Census Grids", "source_link": "https://doi.org/10.7927/H40Z716C ", "strengths": "Several socioeconomic factors available within dataset", "format": "ASCII, GeoTIFF, Shapefile", "geographic_coverage": "United States, Puerto Rico", "data_visualization": "SEDAC map widget", "spatial_resolution": "1 kilometer", "temporal_extent": "2010-01-01 to 2010-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 100, "fields": {"dataset": "Global Estimated Net Migration Grids By Decade, v1 (1970\u200a\u2013\u200a2000)", "description": "The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of globally consistent data on migration, indirect estimation methods were used. The authors relied on a combination of data on spatial population distribution for four time slices (1970, 1980, 1990, and 2000) and subnational rates of natural increase in order to derive estimates of net migration on a 30 arc-second (~1km) grid cell basis. Net migration was estimated by subtracting the population in time period 2 from the population in time period 1, and then subtracting the natural increase (births minus deaths). The residual was considered to be net migration (in-migrants minus out-migrants). The authors ran 13 geospatial net migration estimation models based on outputs from the same number of imputation runs for urban and rural rates of natural increase.This data set represents the average of those runs. These data are reliable at broad scales of analysis (e.g. ecosystems or regions), but are generally not reliable for local level analyses. The data were produced for the United Kingdom Foresight project on Migration and Global Environmental Change.", "description_simplified": "The Global Estimated Net Migration Grids By Decade provides estimates of overall net migration (in-migration minus out-migration) per decade for the 1970s, 1980s, and 1990s.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "POPDYNAMICS - Population Dynamics", "source_link": "https://doi.org/10.7927/H4319SVC ", "strengths": "30 year temporal extent", "format": "GeoTIFF, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "SEDAC map widget", "spatial_resolution": "1 kilometer", "temporal_extent": "1970-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 101, "fields": {"dataset": "Facebook High Resolution Population Density Maps", "description": "Facebook collaborates with SEDAC on data; high resolution and updated frequently", "description_simplified": "Facebook High Resolution Population Density Maps are available through the collaboration of SEDAC and Facebook. These data include demographic information that is high resolution and updated frequently. \n", "indicators": "Human Dimensions", "intended_use": "Path A", "latency": "", "limitations": "Data format and download unclear", "project": "Dashboard, other", "source_link": "Facebook Data For Good High Resolution Population Density Maps", "strengths": "High resolution, updated frequently.", "format": "", "geographic_coverage": "", "data_visualization": "Facebook visualization", "spatial_resolution": "", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 102, "fields": {"dataset": "Global Rural-Urban Mapping Project (GRUMP) Urban Extent Polygons, v1", "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02 is an update to Revision 01, which included new settlements and represented the first time that SEDAC released polygons (in Esri shapefile format) with the settlement name (or name of the largest city in the case of multi-city agglomerations). The shapefile consists of polygons defined by the extent of the nighttime lights and approximated urban extents (circles) based on buffered settlement points. Revision 01 also included new urban extents identified from multiple sources and corrected georeferencing for some settlements (see separate documentation for Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 for the data and methods). Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with CUNY Institute for Demographic Research (CIDR). Revision 02 was produced by CIESIN.", "description_simplified": "The Global Rural-Urban Mapping Project (GRUMP) Urban Extent Polygons dataset maps urban settlements in a polygon or shapefile format defined by the extent of nighttime lights and approximated urban areas. These data are useful in studying urbanization and human migration.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Temporal extent only 1995, Lacks recent data", "project": "GRUMP - Global Rural-Urban Mapping Project", "source_link": "https://doi.org/10.7927/np6p-qe61 ", "strengths": "Population and urban extent in shapefile form", "format": "Shapefile, CSV", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "1 kilometer", "temporal_extent": "1995-07-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 103, "fields": {"dataset": "Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, v1 (2000\u200a\u2013\u200a2100)", "description": "The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds). An algorithm was developed and validated to downscale the 1/8-degree resolution data set to 1-km resolution. For a given decade, the downscaling algorithm allocates the 1/8-degree decadal amount of urban land expansion to 1-km grid cells in proportion to their total urban land amounts at the beginning of the decade.", "description_simplified": "The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios dataset provides urban land projections based on the Shared Socioeconomic Pathways (SSPs) data. These data are useful in socioeconomic, environmental, and urban sprawl research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Recent years' data are projections from original dataset publishing date", "project": "SSP - Shared Socioeconomic Pathways", "source_link": "https://dx.doi.org/10.7927/1z4r-ez63", "strengths": "Includes projections for up to year 2100", "format": "GeoTIFF, netCDF-4", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "1 kilometer", "temporal_extent": "2000-01-01 to 2100-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 104, "fields": {"dataset": "Global Subnational Infant Mortality Rates, v2.01 (2015)", "description": "The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.", "description_simplified": "The Global Subnational Infant Mortality Rates dataset provides a global subnational map of infant mortality rate estimates for the year 2015. These data may aid interdisciplinary studies of health, poverty, and the environment.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Only for 2015", "project": "PMP - Poverty Mapping Project", "source_link": "https://doi.org/10.7927/0gdn-6y33 ", "strengths": "Three data formats available: GeoTIFF, gdb, and excel", "format": "GeoTIFF, Geodatabase, Excel, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "1 kilometer", "temporal_extent": "2015-01-01 to 2015-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 105, "fields": {"dataset": "Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020\u200a\u2013\u200a2100)", "description": "The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.", "description_simplified": "The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age dataset provides county population projections for the US essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Recent years' data are projections from original dataset publishing date", "project": "POPDYNAMICS - Population Dynamics", "source_link": "https://dx.doi.org/10.7927/dv72-s254", "strengths": "100-year temporal extent", "format": "Shapefile, Excel", "geographic_coverage": "United States", "data_visualization": "SEDAC Map widget", "spatial_resolution": "", "temporal_extent": "2020-01-01 to 2100-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 106, "fields": {"dataset": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m V001", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. 2013-01-01 to 2016-06-30\n", "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m V001 dataset provides cropland extent data over Africa for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", "indicators": "Food Availability", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001", "strengths": "", "format": "GeoTIFF", "geographic_coverage": "Africa", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2013-01-01 to 2016-06-30", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 107, "fields": {"dataset": "Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. 2013-01-01 to 2016-12-31\n", "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001 dataset provides cropland extent data over Australia, New Zealand, China, and Mongolia for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", "indicators": "Food Availability", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AUNZCNMOCE.001", "strengths": "", "format": "GeoTIFF", "geographic_coverage": "Australia, New Zealand, China, Mongolia", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2013-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 108, "fields": {"dataset": "Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast and Northeast Asia product 30 m V001", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. 2013-01-01 to 2016-12-31\n", "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast and Northeast Asia product 30 m V001 dataset provides cropland extent data over Southeast and Northeast Asia for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", "indicators": "Food Availability", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SEACE.001", "strengths": "", "format": "GeoTIFF", "geographic_coverage": "Southeastern Asia, Eastern Asia", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2013-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 109, "fields": {"dataset": "Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East product 30 m V001", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security.", "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East product 30 m V001 dataset provides cropland extent data for Europe, Central Asia, Russia, and the Middle East for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", "indicators": "Food Availability", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001", "strengths": "Includes all of Northern Hemisphere", "format": "GeoTIFF", "geographic_coverage": "Europe, Asia", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2013-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 110, "fields": {"dataset": "Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South America product 30 m V001", "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security.", "description_simplified": "The Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East product 30 m V001 dataset provides cropland extent data for South America for the year 2015. These data may be useful in agricultural cropland studies related to water sustainability and food security.", "indicators": "Food Availability", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data, Limited to South America", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SACE.001", "strengths": "", "format": "GeoTIFF", "geographic_coverage": "South America", "data_visualization": "", "spatial_resolution": "30 meters", "temporal_extent": "2013-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 111, "fields": {"dataset": "Air Quality Data for Health-Related Applications", "description": "The purpose of this data collection is to provide air quality data for health-related research and applications. Currently this collection consists of the Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000\u200a\u2013\u200a2016) and Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000\u200a\u2013\u200a2016) data sets. A similar NO2 data set is forthcoming.", "description_simplified": "The Air Quality Data for Health-Related Applications data collection contains two datasets: Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States and Daily and Annual PM2.5 Concentrations for the Contiguous United States. Both datasets provide gridded air quality data useful in health-related research and applications.", "indicators": "Disasters", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "Other", "source_link": "https://sedac.ciesin.columbia.edu/data/collection/aqdh/sets/browse", "strengths": "16 year temporal extent", "format": "GeoTIFF, RDS,", "geographic_coverage": "United States", "data_visualization": "SEDAC map widget", "spatial_resolution": "", "temporal_extent": "2000-2016", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 112, "fields": {"dataset": "AirNow Air Quality Dashboard", "description": "Interactive dashboard provided by AirNow, a partnering organization. Current and archival data available for dashboard. ", "description_simplified": "The AirNow Air Quality Dashboard is an interactive dashboard provided by Airnow, a partnering organization. Current and past air quality data are available. Data may not be available for download.", "indicators": "Health & Air Quality", "intended_use": "Path A", "latency": "Varies", "limitations": "", "project": "", "source_link": "https://gispub.epa.gov/airnow/index.html?tab=3", "strengths": "Current and past data available", "format": "", "geographic_coverage": "GLobal", "data_visualization": "Dashboard visualization", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 113, "fields": {"dataset": "AirNow International Air Quality Dashboard", "description": "International version of AirNow dashboard for air quality visualization.", "description_simplified": "The International AirNow Air Quality Dashboard is an interactive dashboard provided by Airnow, a partnering organization. Current and past air quality data are available. Data may not be available for download.", "indicators": "Health & Air Quality", "intended_use": "Path A", "latency": "", "limitations": "Data format and download unclear", "project": "Other", "source_link": "https://www.airnow.gov/index.cfm?action=airnow.international", "strengths": "Current data available", "format": "", "geographic_coverage": "Global", "data_visualization": "Dashboard visualization", "spatial_resolution": "", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 114, "fields": {"dataset": "SEDAC Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), v4.03 (1998\u200a\u2013\u200a2019)", "description": "The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03 consists of annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4. The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies. Gridded data sets are provided at a resolution of 0.01 degrees to allow users to agglomerate data as best meets their particular needs. Data sets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. The data are distributed as GeoTIFF files and are in WGS84 projection.", "description_simplified": "The SEDAC Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) dataset provides air quality data for ground-level fine particulate matter that is 2.5 micrometers or smaller (PM2.5) for environmental health research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Data only available in GeoTIFF", "project": "SDEI - Satellite-Derived Environmental Indicators", "source_link": "https://doi.org/10.7927/fx80-4n39 ", "strengths": "11-year temporal extent", "format": "GeoTIFF, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "111 kilometers", "temporal_extent": "1998-01-01 to 2019-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 115, "fields": {"dataset": "Annual PM2.5 Concentrations for Countries and Urban Areas, v1 (1998\u200a\u2013\u200a2016)", "description": "The Annual PM2.5 Concentrations for Countries and Urban Areas, 1998-2016, consists of mean concentrations of particulate matter (PM2.5) for countries and urban areas. The PM2.5 data are from the Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. The urban areas are from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02, and its time series runs from 1998 to 2016. The country averages are population-weighted such that concentrations in populated areas count more toward the country average than concentrations in less populated areas, and its time series runs from 2008 to 2015.", "description_simplified": "The Annual PM2.5 Concentrations for Countries and Urban Areas dataset provides air quality data for mineral dust and sea-salt filtered fine particulate matter of 2.5 micrometers or smaller (PM2.5) in countries and urban areas for environmental health research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Data only available in GeoTIFF", "project": "SDEI - Satellite-Derived Environmental Indicators", "source_link": "https://doi.org/10.7927/rja8-8h89 ", "strengths": "11-year temporal extent", "format": "Shapefile, Excel", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "10 kilometers", "temporal_extent": "1998-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 116, "fields": {"dataset": "Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000\u200a\u2013\u200a2016)", "description": "The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set includes predictions of PM2.5 concentrations in grid cells at a resolution of 1 km for the years 2000 to 2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, as well as other predictors. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions.", "description_simplified": "The Daily and Annual PM2.5 Concentrations for the Contiguous United States dataset provides predictions of PM2.5 concentrations for the years 2000 to 2016. These data are useful in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "AQDH - Air Quality Data for Health-Related Applications", "source_link": "https://doi.org/10.7927/0rvr-4538 ", "strengths": "16-year temporal extent", "format": "GeoTIFF, Shapefile", "geographic_coverage": "United States", "data_visualization": "SEDAC Map widget", "spatial_resolution": "1 kilometer", "temporal_extent": "2000-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 117, "fields": {"dataset": "Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000\u200a\u2013\u200a2016)", "description": "The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set contains estimates of ozone concentrations at a high resolution in space (1 km x 1 km grid cells) and time (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages.", "description_simplified": "The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States dataset provides ground-level Ozone (O3) concentration data in the United States. These data may be useful in public health research to respectively estimate short and long-term effects on human health and related research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "AQDH - Air Quality Data for Health-Related Applications", "source_link": "https://doi.org/10.7927/a4mb-4t86 ", "strengths": "16-year temporal extent", "format": "GeoTIFF, Shapefile", "geographic_coverage": "United States", "data_visualization": "SEDAC Map widget", "spatial_resolution": "1 kilometer", "temporal_extent": "2000-01-01 to 2016-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 118, "fields": {"dataset": "Global Pesticide Grids (PEST-CHEMGRIDS), v1.01 (2015, 2020, 2025)", "description": "The Global Pesticide Grids (PEST-CHEMGRIDS), Version 1.01 data set contains 20 of the most-used pesticide active ingredients on 6 dominant crops and 4 aggregated crop classes at 5 arc-minute resolution (about 10 km at the equator), estimated in year 2015, and then projected to 2020 and 2025. To estimate the global application rates of specific active ingredients, spatial statistical methods were used to re-analyze the U.S. Geological Survey Pesticide National Synthesis Project (USGS/PNSP) and the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) pesticide databases, along with other public inventories including globally gridded data of soil physical properties, hydro-climatic variables, agricultural quantities, and socioeconomic indices. The application rate (APR) of each active ingredient on each crop is in kilogram per hectare per year (kg/ha-year), and the harvest area of each crop is in hectare (ha). The data set also includes 200 data quality index maps corresponding to each active ingredient on each crop, as well as maps of the 10 dominant crops and 4 aggregated crop classes. Version 1.01 includes data in GeoTIFF and netCDF formats.", "description_simplified": "The Global Pesticide Grids (PEST-CHEMGRIDS) dataset provides commonly-used pesticides crucial to assess humanand ecosystem exposure to potential toxicants for environmental modeling, assessment of agricultural chemical contamination and risk analysis, and other related research for the years 2015, 2020, and 2025.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "No data prior to 2015", "project": "FERMANV1 - Global Agricultural Inputs", "source_link": "https://doi.org/10.7927/weq9-pv30 ", "strengths": "Includes past, recent, and projections", "format": "GeoTIFF, netCDF-4, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "10 kilometers", "temporal_extent": "2015-0101, 2020-01-01, 2025-01-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 119, "fields": {"dataset": "MODIS/Terra AOD from Earthdata Search", "description": "Terrestrial AOD data (3km resolution, merged algorithm)", "description_simplified": "The MODIS/Terra AOD from Earthdata Search dataset provides data related to the aerosols in the air that may be a result of air pollution and may contribute to human health issues. This dataset includes data over land.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?q=MOD04_3K&ok=MOD04_3K", "strengths": "multiple datasets available", "format": "HDF", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies- multiple datasets included"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 120, "fields": {"dataset": "MODIS Terra/Aqua-MAIAC Retrieval AOD from Earthdata Search", "description": "Multi-angle Implementation of Atmospheric Correction (MAIAC) Land AOD utilizes a new advanced algorithm which uses time series (TMS) analysis and a combination of pixel- and image-based processing to improve the accuracy of cloud detection, aerosol retrievals and atmospheric correction.", "description_simplified": "The MODIS Terra/Aqua-MAIAC Retrieval AOD from Earthdata Search dataset provides data related to the aerosols in the air that may be a result of air pollution and may contribute to human and ecosystem health issues. This dataset provides data for atmosphere over the land and ocean.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "two datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?q=MAIAC&fi=MODIS&fst0=Atmosphere", "strengths": "two datasets available within link", "format": "HDF", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "varies- multiple datasets included"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 121, "fields": {"dataset": "VIIRS AOD at 1 degree x 1 degree from Earthdata Search", "description": "(daily global data coverage)", "description_simplified": "The VIIRS AOD at 1 degree x 1 degree from Earthdata Search dataset provides daily global coverage aerosol data.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "two datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?q=AERDB_D3&ok=AERDB_D3", "strengths": "two datasets available within link", "format": "NetCDF4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "2012 ongoing", "temporal_resolution": "daily"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 122, "fields": {"dataset": "VIIRS AOD at 6km from Earthdata Search", "description": "(daily)", "description_simplified": "The VIIRS AOD at 6km from Earthdata Search dataset provides daily global coverage aerosol data at a higher resolution.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "two datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?q=AERDB_L2", "strengths": "two datasets available within link", "format": "NetCDF4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets included", "temporal_resolution": "daily"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 123, "fields": {"dataset": "Monthly VIIRS AOD at 1 degree x 1 degree from Earthdata Search", "description": "", "description_simplified": "The Monthly VIIRS AOD at 1 degree x 1 degree from Earthdata Search provides monthly global coverage aerosol data.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "two datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?q=AERDB_M3&ok=AERDB_M3", "strengths": "two datasets available within link", "format": "NetCDF4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "2012 ongoing", "temporal_resolution": "monthly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 124, "fields": {"dataset": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo L3 1 day 1.0 degree x 1.0 degree V3", "description": "The OMI science team produces this Level-3 daily global gridded product OMAERUVd (1 deg Lat/Lon grids). The OMAERUVd product is produced with all data pixels that fall in a grid box with quality filtered and then averaged, based on the pixel level OMI Level-2 Aerosol data product OMAERUV. The OMAERUV data product is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data. The OMAERUVd data product contains extinction and absorption optical depths at three wavelenghts (355 nm, 388 nm and 500 nm).", "description_simplified": "The OMI science team produces this Level-3 daily global gridded product OMAERUVd (1 deg Lat/Lon grids). The OMAERUVd data product contains extinction and absorption optical depths at three wavelenghts (355 nm, 388 nm and 500 nm).", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "3 days", "limitations": "", "project": "Aura - Earth Observing System (EOS), Aura", "source_link": "https://dx.doi.org/10.5067/Aura/OMI/DATA3003", "strengths": "", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Giovanni", "spatial_resolution": "111 kilometers", "temporal_extent": "2004-10-01 to present", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 125, "fields": {"dataset": "OMI/Aura Multi-wavelength Aerosol Optical Depth and Single Scattering Albedo L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3", "description": "The OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The selection criteria is based on the shortest optical path length (secant of solar zenith angle + secant of viewing zenith angle).\n", "description_simplified": "The OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. ", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "2 days", "limitations": "", "project": "Aura - Earth Observing System (EOS), Aura", "source_link": "https://dx.doi.org/10.5067/Aura/OMI/DATA3004", "strengths": "", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Giovanni", "spatial_resolution": "30 kilometers", "temporal_extent": "2004-10-01 to present", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 126, "fields": {"dataset": "AERONET Ground-based AOD Measurements", "description": "The AERONET Data Display Interface allows users to find and download ground-based AOD (Aerosol Optical Depth, quantity of light removed from a beam by scattering or absorbing during its path through a medium and is a unitless measure) data for locations all across the world. Data are available from years 1993-2022, dependent on location.", "description_simplified": "The AERONET Data Display Interface allows users to find and download ground-based AOD (Aerosol Optical Depth, quantity of light removed from a beam by scattering or absorbing during its path through a medium and is a unitless measure) data for locations all across the world. Data are available from years 1993-2022, dependent on location.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "NRT", "limitations": "", "project": "AERONET - Aerosol Robotic Network", "source_link": "https://aeronet.gsfc.nasa.gov/cgi-bin/draw_map_display_aod_v3", "strengths": "NRT, 19-year temporal extent", "format": "", "geographic_coverage": "Global", "data_visualization": "Map Viewer", "spatial_resolution": "", "temporal_extent": "1993-01-01 to Present", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 127, "fields": {"dataset": "Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1 (1998\u200a\u2013\u200a2016)", "description": "The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016 consist of annual concentrations (micrograms per cubic meter) of ground-level fine particulate matter (PM2.5), with dust and sea-salt removed. This data set combines AOD retrievals from multiple satellite instruments including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies", "description_simplified": "The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016 consist of annual concentrations (micrograms per cubic meter) of ground-level fine particulate matter (PM2.5), with dust and sea-salt removed. This data set combines AOD retrievals from multiple satellite instruments including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS).", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "", "project": "SDEI - Satellite-Derived Environmental Indicators", "source_link": "https://doi.org/10.7927/H4ZK5DQS", "strengths": "", "format": "GeoTIFF, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "10 kilometers", "temporal_extent": "1998-01-01 to 2019-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 128, "fields": {"dataset": "Worldbank Mean Exposure to PM2.5 Across the Globe", "description": "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)", "description_simplified": "The Worldbank mean exposure to PM2.5 across the globe dataset provides mean annual exposure (micrograms per cubic meter) to PM2.5 air pollution.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "Lacks recent data", "project": "", "source_link": "https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3?view=chart", "strengths": "CSV, XML, and Excel formats available", "format": "CSV, XML, Excel", "geographic_coverage": "Global", "data_visualization": "Graphing feature", "spatial_resolution": "", "temporal_extent": "1990-01-01 to 2019-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 129, "fields": {"dataset": "OMI/Aura NO2 Cloud-Screened Total and Tropospheric Column L3 Global Gridded 0.25 degree x 0.25 degree", "description": "This is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and \"averaged\" into 0.25x0.25 degree global grids. This product contains Total column NO2 and Total Tropospheric Column NO2, for all atmospheric conditions, and for sky conditions where cloud fraction is less than 30 percent. Nitrogen dioxide is an important chemical species in both, the stratosphere where it plays a key role in ozone chemistry, and in the troposphere where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality.", "description_simplified": "This is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and \"averaged\" into 0.25x0.25 degree global grids. This product contains Total column NO2 and Total Tropospheric Column NO2, for all atmospheric conditions, and for sky conditions where cloud fraction is less than 30 percent.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "Aura - Earth Observing System (EOS), Aura", "source_link": "https://dx.doi.org/10.5067/Aura/OMI/DATA3007", "strengths": "18-year temporal extent, Recent data available", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Giovanni", "spatial_resolution": "30 kilometers", "temporal_extent": "2004-10-01 to Present", "temporal_resolution": "Daily"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 130, "fields": {"dataset": "OMI SO2 Data from Earthdata Search", "description": "OMI provides daily total column data at a resolution of 13x24 km; data are in HDF5 format, and can be opened using Panoply.", "description_simplified": "The OMI SO2 Data from Earthdata Search dataset provides daily total air column data for sulfur dioxide.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?fi=OMI&fst0=Atmosphere&fsm0=Atmospheric%20Chemistry&fs10=Sulfur%20Compounds&fs20=Sulfur%20Dioxide", "strengths": "multiple datasets available", "format": "Varies", "geographic_coverage": "Global", "data_visualization": "Giovanni, Worldview", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets available", "temporal_resolution": "varies- multiple datasets available"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 131, "fields": {"dataset": "TROPOMI SO2 data from Earthdata Search", "description": "ESA TROPOMI SO2 provides additional information on this level 2 data product. As with the NO2 data above, you will need to adjust the scaling factor. Data are in NetCDF format, and can be opened using Panoply.", "description_simplified": "The TROPOMI SO2 data from Earthdata Search dataset provides sulfur dioxide data. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search/granules?p=C1442068508-GES_DISC&tl=1542053901!4!!&fi=TROPOMI&fst0=Atmosphere&fsm0=Atmospheric%20Chemistry&fst1=Atmosphere&fsm1=Atmospheric%20Chemistry&fs11=Sulfur%20Compounds&fs21=Sulfur%20Dioxide", "strengths": "Many granules available in Earthdata Search, also available in Giovanni and Worldview", "format": "", "geographic_coverage": "Global", "data_visualization": "Earthdata Search viewer", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets available", "temporal_resolution": "varies- multiple datasets available"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 132, "fields": {"dataset": "Global Sulfur Dioxide Monitoring Home Page", "description": "The global sulfur dioxide monitoring site dataset provides imagery of daily SO2 from OMI, OMPS, and TROPOMI. The site also provides information on the source of emissions. These data may aid air quality research.", "description_simplified": "The global sulfur dioxide monitoring site dataset provides imagery of daily SO2 from OMI, OMPS, and TROPOMI. The site also provides information on the source of emissions. These data may aid air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "", "source_link": "https://so2.gsfc.nasa.gov/", "strengths": "Multiple datasets available within link", "format": "", "geographic_coverage": "Global", "data_visualization": "images, graphs", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": "Varies"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 133, "fields": {"dataset": "AIRS CO data from Earthdata Search", "description": "AIRS measures abundances of trace components in the atmosphere including CO. Data are available daily (AIRS3STD), over 8 days (AIRS3ST8), or monthly (AIRS3STM). The instrument measures the amount of CO in the total vertical column profile of the atmosphere (from Earth\u2019s surface to top-of-atmosphere). Data are in HDF format, and can be opened using Panoply.", "description_simplified": "Available in giovanni and worldview", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?q=AIRS3&ok=AIRS3&fi=AIRS&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Carbon%20Monoxide", "strengths": "Multiple datasets available within link", "format": "HDF", "geographic_coverage": "Global", "data_visualization": "Giovanni, Worldview", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets available", "temporal_resolution": "varies- multiple datasets available"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 134, "fields": {"dataset": "MOPITT CO data from Earthdata Search", "description": "Measurements of Pollution in the Troposphere (MOPITT) measures the amount of CO present in the total vertical column of the lower atmosphere (troposphere) and is measured in mole per square centimeter (mol/cm2). Data are available daily or monthly. Data are acquired using the thermal and near-infrared channels. Data are in HDF5 format, and can be opened using Panoply.", "description_simplified": "The Measurements of Pollution in the Troposphere (MOPITT) dataset measures the amount of carbon monoxide present in the total vertical column of the lower atmosphere (troposphere) and is measured in mole per square centimeter (mol/cm2).", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?q=V008&ok=V008&fi=MOPITT&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Carbon%20Monoxide", "strengths": "ongoing data collection", "format": "HDF5", "geographic_coverage": "Global", "data_visualization": "Giovanni, Worldview, Panopoly", "spatial_resolution": "", "temporal_extent": "2003-03-03 ongiong", "temporal_resolution": "daily/monthly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 135, "fields": {"dataset": "TROPOMI CO data from Earthdata Search", "description": "ESA TROPOMI CO provides additional information on this level 2 data product. As with the NO2 data above, you will need to adjust the scaling factor. Data are in NetCDF format, and can be opened using Panoply.", "description_simplified": "The TROPOMI CO data from Earthdata Search provides carbon monoxide data that may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?fi=TROPOMI&fst0=Atmosphere&fsm0=Air%20Quality&fs10=Carbon%20Monoxide", "strengths": "Multiple datasets available within link", "format": "NetCDF", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets available", "temporal_resolution": "varies- multiple datasets available"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 136, "fields": {"dataset": "Sentinel-5P TROPOMI Tropospheric Ozone Column V2", "description": "Copernicus Sentinel-5P tropospheric ozone data products are retrieved by the convective-cloud-differential (CCD) algorithm to derive the tropospheric ozone columns and by the cloud slicing algorithm (CSA) to derive mean upper tropospheric ozone volume mixing ratios above the clouds. The S5P_TROPOZ_CCD algorithm uses TROPOMI Level-2 ozone column measurements and the cloud parameters provided by the S5P_CLOUD_OCRA and S5P_CLOUD_ROCINN, the average values of the tropospheric ozone columns below 270 hpa can be determined. The S5P_TROPOZ_CSA algorithm uses the correlation between could top pressure and the ozone column above the cloud. The retrieval depends on the amount of measurements with a high cloud cover.", "description_simplified": "Copernicus Sentinel-5P tropospheric ozone data products are retrieved by the convective-cloud-differential (CCD) algorithm to derive the tropospheric ozone columns and by the cloud slicing algorithm (CSA) to derive mean upper tropospheric ozone volume mixing ratios above the clouds.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "", "project": "Sentinel-5P - Copernicus Sentinel-5 Precursor", "source_link": "https://dx.doi.org/10.5270/S5P-hcp1l2m", "strengths": "", "format": "netCDF-4", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "2018-04-30 to Present", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 137, "fields": {"dataset": "OMI AI from Earthdata Search", "description": "OMI provides an Ultraviolet Aerosol Index; data are in HDF5 format, and can be opened using Panoply. Note that when opening the data in Panoply, there are a number of different data fields from which to choose. Select \"UVAerosolIndex\".", "description_simplified": "The OMI AI from Earthdata Search dataset provides an Ultraviolet Aerosol Index. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Multiple datasets available within link", "project": "", "source_link": "https://search.earthdata.nasa.gov/search?q=aerosol%20index%20OMAER&ok=aerosol%20index%20OMAER&fi=OMI&fst0=Atmosphere&fsm0=Aerosols", "strengths": "Multiple datasets available within link", "format": "HDF5", "geographic_coverage": "Global", "data_visualization": "Worldview, giovanni", "spatial_resolution": "", "temporal_extent": "varies- multiple datasets available", "temporal_resolution": "varies- multiple datasets available"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 138, "fields": {"dataset": "TROPOMI AI data from Earthdata Search", "description": "ESA TROPOMI AI provides additional information on this level 2 data product. Data are NetCDF format, and can be opened using Panoply.", "description_simplified": "The TROPOMI AI data from Earthdata Search dataset provides UV aerosol index data. The Aerosol Index (AI) is a well-established data product that has been calculated for several different satellite instruments spanning a period of more than 40 years. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "", "source_link": "http://www.tropomi.eu/data-products/uv-aerosol-index", "strengths": "data available up to 2021", "format": "NetCDF", "geographic_coverage": "Global", "data_visualization": "viewer", "spatial_resolution": "", "temporal_extent": "varies-2021", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 139, "fields": {"dataset": "OMPS-NPP L2 NM Aerosol Index swath orbital", "description": "The OMPS-NPP L2 NM Aerosol Index swath orbital product provides aerosol index values from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. This is now the official NASA aerosol index product, replacing the aerosol index found in the OMPS-NPP L2 NM Total Ozone product. The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir.", "description_simplified": "The OMPS-NPP L2 NM Aerosol Index swath orbital product provides aerosol index values from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. This is now the official NASA aerosol index product, replacing the aerosol index found in the OMPS-NPP L2 NM Total Ozone product.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "NPP-JPSS - National Polar Orbiting Partnership-Joint Polar Satellite System", "source_link": "https://doi.org/10.5067/40L92G8144IV", "strengths": "Recent data available", "format": "HDF5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "50 kilometers", "temporal_extent": "2011-11-07 to Present", "temporal_resolution": "Hourly - < Daily"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 140, "fields": {"dataset": "Aqua/AIRS L2 Near Real Time (NRT) Support Retrieval (AIRS-only) V7.0", "description": "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_7.0) differs from the routine product (AIRS2SUP_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed.", "description_simplified": "The AIRS Dust Score indicates the level of atmospheric aerosols in the Earth\u2019s atmosphere over the ocean. The numerical scale is a qualitative representation of the presence of dust in the atmosphere, an indication of where large dust storms may form and the areas that may be affected.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "", "project": "LANCE - Land, Atmosphere Near real-time Capability for EOS", "source_link": "https://doi.org/10.5067/MOQOVNHNERGG", "strengths": "High temporal resolution", "format": "HDF-EOS", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "50 kilometers", "temporal_extent": "2002-08-20 to Present", "temporal_resolution": "1 minute - < 1 hour"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 141, "fields": {"dataset": "MODIS/Terra+Aqua BRDF/Albedo Gap-Filled Snow-Free Daily L3 Global 30ArcSec CMG V006", "description": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product.", "description_simplified": "The MODIS/Aqua Land Surface Reflectance Data from Earthdata Search dataset provides surface reflectance data over water used in air quality monitoring and research.", "indicators": "Health & Air Quality", "intended_use": "Path C", "latency": "", "limitations": "", "project": "Terra - Earth Observing System (EOS), Terra, Aqua - Earth Observing System (EOS), Aqua\n ", "source_link": "https://dx.doi.org/10.5067/MODIS/MCD43GF.006", "strengths": "Recent data available", "format": "HDF-EOS2", "geographic_coverage": "Global", "data_visualization": "Earthdata Search viewer", "spatial_resolution": "1 kilometer", "temporal_extent": "2000-03-03 to 2017-12-31", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 142, "fields": {"dataset": "VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km (v2.0)", "description": "The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) deep blue aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, every 6 minutes, globally. The Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea\u2010viewing Wide Field\u2010of\u2010view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land. This L2 description pertains to the SNPP VIIRS Deep Blue Aerosol version-1.1 (V1.1) product, whose record starts from March 1st 2012.", "description_simplified": "The VIIRS (Suomi NPP) Deep Blue Aerosol Optical Thickness (Land and Ocean) dataset provides Aerosol Optical Thickness (AOT) used in air quality monitoring and research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "Only netcdf4 available", "project": "", "source_link": "https://doi.org/10.5067/VIIRS/AERDB_L2_VIIRS_SNPP_NRT.002", "strengths": "NRT, Daily temporal resolution", "format": "NetCDF-4", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2023-06-01 to present", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 143, "fields": {"dataset": "VIIRS (Suomi NPP) Deep Blue Aerosol Angstrom Exponent", "description": "\u00c5ngstr\u00f6m exponent over land is defined between 412-470 nm for 'bright' surfaces, and 470-670 nm for 'dark' surfaces. The combined \u00c5ngstr\u00f6m exponent over land and ocean are for those retrieval pixels passing quality assurance tests. This layer is created from the Deep Blue (DB) algorithm over land and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water to determine atmospheric aerosol loading for day time cloud-free snow-free scenes. This data product is designed to facilitate continuity in the aerosol record provided by the Deep Blue aerosol project for other sensors including the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometers (MODIS).", "description_simplified": "The VIIRS (Suomi NPP) Deep Blue Aerosol Angstrom Exponent provides aerosol data that may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "only netcdf4 available", "project": "", "source_link": "https://worldview.earthdata.nasa.gov/?v=-297.3203500600552,-98.81700395435438,131.6641000600552,105.28575395435438&l=VIIRS_SNPP_AOT_Deep_Blue_Best_Estimate(hidden),VIIRS_SNPP_Angstrom_Exponent_Deep_Blue_Best_Estimate,Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor(hidden)&lg=false&t=2022-05-06-T12%3A01%3A03Z", "strengths": "NRT, daily temporal resolution", "format": "NetCDF4", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2012 Mar 1 to present", "temporal_resolution": "daily"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 144, "fields": {"dataset": "MLS/Aura Near-Real-Time L2 Carbon Monoxide (CO) Mixing Ratio V005", "description": "The Microwave Limb Sounder (MLS) Carbon Monoxide (CO) Mixing Ratio layer at 215 hPa (hectopascals) indicates carbon monoxide levels at the vertical atmospheric pressure level of 215hPa, and is measured in parts per billion by volume (ppbv). L2 Carbon Monoxide (CO) MLS/Aura NRT L2 CO Mixing Ratio", "description_simplified": "The MLS (Aura) dataset indicates carbon monoxide levels. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "Only hdf-eos5 available", "project": "LANCE - Land, Atmosphere Near real-time Capability for EOS, Aura - Earth Observing System (EOS), Aura", "source_link": "https://disc.gsfc.nasa.gov/datacollection/ML2CO_NRT_005.html", "strengths": "NRT, Daily temporal resolution", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2021-09-21 to Present", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 145, "fields": {"dataset": "Corrected Reflectance imagery in Worldview", "description": "MODIS and VIIRS Corrected Reflectance imagery are available only as near real-time imagery. The imagery can be visualized in Worldview and Global Imagery Browse Services (GIBS). More:", "description_simplified": "The Corrected Reflectance imagery in Worldview dataset provides surface reflectance imagery in near real time. The imagery can be visualized in Worldview and Gloval Imagery Browse Services (GIBS). These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "", "source_link": "https://go.nasa.gov/2IDvag7", "strengths": "daily temporal resolution", "format": "", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "varies", "temporal_resolution": "daily"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 146, "fields": {"dataset": "MODIS/Aqua Thermal Anomalies/Fire 5-Min L2 Swath 1km V061", "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MYD14 Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths) at a 1 kilometer (km) spatial resolution. The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). \n", "description_simplified": "The MODIS Fires and Thermal Anomalies (Day/Night) dataset provides fire anomalies for both land and water. These data may aid in air quality and disaster research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Only hdf-eos5 available", "project": "Aqua - Earth Observing System (EOS), Aqua", "source_link": "https://dx.doi.org/10.5067/MODIS/MYD14.061", "strengths": "Daily temporal resolution", "format": "HDF-EOS", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "1 kilometer", "temporal_extent": "2002-07-04 to Present", "temporal_resolution": "1 minute - < 1 hour"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 147, "fields": {"dataset": "MLS/Aura Near-Real-Time L2 Nitric Acid (HNO3) Mixing Ratio V005", "description": "The MLS Nitric Acid (HNO3) Mixing Ratio at 46hPa layer indicates nitric acid levels at the vertical atmospheric pressure level of 46hPa, and is measured in parts per billion by volume (ppbv). It is derived from the MLS Nitric Acid (ML2HNO3_NRT) MLS/Aura NRT L2 HNO3 Mixing Ratio", "description_simplified": "The MLS (Aura) Nitric Acid (46 hPa, Day/Night) dataset indicates nitric acid levels in the atmosphere. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "Only hdf-eos5 available", "project": "LANCE - Land, Atmosphere Near real-time Capability for EOS, Aura - Earth Observing System (EOS), Aura", "source_link": "https://disc.gsfc.nasa.gov/datacollection/ML2HNO3_NRT_005.html", "strengths": "15 min temporal resolution", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2021-09-21 to present", "temporal_resolution": "1 minute - < 1 hour"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 148, "fields": {"dataset": "MLS (Aura) Nitrous Oxide", "description": "The MLS Nitrous Oxide (N2O) Mixing Ratio layer at 46hPa (hectopascals) indicates nitrous oxide levels at the vertical atmospheric pressure level of 46hPa, and is measured in parts per billion by volume (ppbv). MLS/Aura NRT L2 N2O Mixing Ratio", "description_simplified": "The MLS (Aura) Nitrous Oxide dataset indicates nitrous oxide levels in the atmosphere. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "only hdf-eos5 available", "project": "", "source_link": "https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,MLS_N2O_46hPa_Night,MLS_N2O_46hPa_Day,Reference_Labels(hidden),Reference_Features(hidden),Coastlines&v=-92.7421875,-49.47803771636507,66.5859375,55.94678771636507", "strengths": "15 min temporal resolution", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2013 May 09 to present", "temporal_resolution": "15 mins"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 149, "fields": {"dataset": "AIRS/Aqua L1B Near Real Time (NRT) Infrared (IR) geolocated and calibrated radiances V005", "description": "The AIRS Level 1B Near Real Time (NRT) product (AIRIBRAD_NRT_005) differs from the routine product (AIRIBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. ", "description_simplified": "The AIRS (Aqua) Sulfur Dioxide dataset indicates sulfur dioxide column amounts in the atmosphere. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "Only HDF-EOS5 available", "project": "LANCE - Land, Atmosphere Near real-time Capability for EOS, Aqua - Earth Observing System (EOS), Aqua", "source_link": "https://disc.gsfc.nasa.gov/datacollection/AIRIBRAD_NRT_005.html", "strengths": "Daily temporal resolution", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2015-12-15 to Present", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 150, "fields": {"dataset": "MLS/Aura Near-Real-Time L2 Sulfur Dioxide (SO2) Mixing Ratio V005", "description": "The MLS Sulfur Dioxide (SO2) Mixing Ratio layer at 147hPa (hectopascals) indicates sulfur dioxide levels at the vertical atmospheric pressure level of 147hPa, and is measured in parts per billion by volume (ppbv). The temporal resolution is twice daily (day and night). MLS/Aura NRT L2 SO2 Mixing Ratio", "description_simplified": "The MLS (Aura) Sulfur Dioxide dataset indicates sulfur dioxide levels. These data may aid in air quality research.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "NRT", "limitations": "Only HDF-EOS5 available", "project": "LANCE - Land, Atmosphere Near real-time Capability for EOS, Aura - Earth Observing System (EOS), Aura", "source_link": "https://disc.gsfc.nasa.gov/datacollection/ML2SO2_NRT_005.html", "strengths": "15 min temporal resolution", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "", "temporal_extent": "2021-09-21 to Present", "temporal_resolution": "1 minute - < 1 hour"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 151, "fields": {"dataset": "OMI/Aura Sulfur Dioxide (SO2) Total Column L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3", "description": "The OMI Sulfur Dioxide (SO2) Lower Troposphere layer indicates the column density of sulfur dioxide in the lower troposphere (corresponding to 2.5 km center of mass altitude (CMA)) and is measured in Dobson Units (DU). Sulfur Dioxide and Aerosol Index products are used to monitor volcanic clouds and detect pre-eruptive volcanic degassing globally. L2 Sulfur Dioxide (SO2) Total Column Swath 13x24 km", "description_simplified": "The OMI (Aura) Sulfur Dioxide dataset indicates the column density of sulfur dioxide in the lower atmosphere. These products are used to monitor volcanic clouds and detect volcanic degassing globally.", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "Only HDF-EOS5 available", "project": "Aura - Earth Observing System (EOS), Aura", "source_link": "https://doi.org/10.5067/Aura/OMI/DATA3008", "strengths": "Daily temporal resolution", "format": "HDF-EOS5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "30 kilometers", "temporal_extent": "2004-10-01 to Present", "temporal_resolution": "Daily - < Weekly"}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 152, "fields": {"dataset": "OMPS/NPP PCA SO2 Total Column 1-Orbit L2 Swath 50x50km V2", "description": "The OMPS_NPP_NMSO2_PCA_L2 product is part of the MEaSUREs (Making Earth Science Data Records for Use in Research Environments) suite of products. It is retrieved from the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) spectrometer and provides contiguous daily global monitoring of anthropogenic and volcanic sulfur dioxide (SO2), an important pollutant and aerosol precursor that affects both air quality and the climate. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017), and continues (Zhang et al., 2017) NASA's Earth Observing System (EOS) standard Aura/Ozone Monitoring Instrument SO2 product (OMSO2).", "description_simplified": "The OMPS_NPP_NMSO2_PCA_L2 product is part of the MEaSUREs (Making Earth Science Data Records for Use in Research Environments) suite of products. It is retrieved from the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) spectrometer and provides contiguous daily global monitoring of anthropogenic and volcanic sulfur dioxide (SO2), an important pollutant and aerosol precursor that affects both air quality and the climate. ", "indicators": "Health & Air Quality", "intended_use": "Path B", "latency": "", "limitations": "", "project": "MEaSUREs - Making Earth System Data Records for Use in Research Environments", "source_link": "https://dx.doi.org/10.5067/MEASURES/SO2/DATA205", "strengths": "", "format": "HDF5", "geographic_coverage": "Global", "data_visualization": "Worldview", "spatial_resolution": "50 kilometers", "temporal_extent": "2012-01-26 to Present", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 153, "fields": {"dataset": "ArcGIS Global Air Quality Story Map", "description": "NASA's Socioeconomic Data and Applications Center (SEDAC) offers global annual gridded PM 2.5 data for various years. This data was aggregated to common global boundaries in order to help us see what air quality is like around the world.", "description_simplified": "NASA's Socioeconomic Data and Applications Center (SEDAC) offers global annual gridded PM 2.5 data for various years. This data was aggregated to common global boundaries in order to help us see what air quality is like around the world.", "indicators": "Health & Air Quality", "intended_use": "Path A", "latency": "", "limitations": "", "project": "", "source_link": "https://storymaps.arcgis.com/stories/a3d0b0835b9e45b69f55e5ce94d84ddf ", "strengths": "Interactive with data download available", "format": "Shapefile", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 154, "fields": {"dataset": "US Census Grids 2010", "description": "The U.S. Census Grids (Summary File 1), 2010 data set contains grids of demographic and socioeconomic data from the year 2010 in ASCII and GeoTIFF formats. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2010 TIGER/Line Files and census variables (population, households, and housing variables).", "description_simplified": "The US Census Grids 2010 provide gridded demographic data, including age, race, ethnicity, and housing for the US and Puerto Rico.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Data only available for the year 2010", "project": "USCG - U.S. Census Grids", "source_link": "https://doi.org/10.7927/H40Z716C ", "strengths": "Several socioeconomic factors available within dataset", "format": "ASCII, GeoTIFF, PDF, PNG, WMS, Shapefile", "geographic_coverage": "United States. Puerto Rico", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/usgrid-summary-file1-2010/maps", "spatial_resolution": "1 kilometer", "temporal_extent": "2010-01-01 to 2010-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 155, "fields": {"dataset": "Global Gridded Geographically Based Economic Data (G-Econ), v4 (1990, 1995, 2000, 2005)", "description": "The Global Gridded Geographically Based Economic Data (G-Econ), Version 4 contains derived one degree grid cells of Gross Domestic Product (GDP) data in Grid and ASCII formats for both Market Exchange Rate (MER) and Purchasing Power Parity (PPP) for the years 1990, 1995, 2000 and 2005. MER is the exchange rate between local and U.S. dollar currencies for a given time period established by the market. PPP is the exchange rate between a country's currency and U.S. dollars adjusted to reflect the actual cost in U.S. dollars of purchasing a standardized market basket of goods in that country using the country's currency. The original data from the G-Econ Project at Yale University is also available in tabular format and includes latitude and longitude geographic coordinates of the grid cells, area of grid cells, as well as country names, distance to coast, elevation, vegetation, population, precipitation and temperature.", "description_simplified": "The Global Gridded Geographically Based Economic Data (G-Econ), v4 (1990, 1995, 2000, 2005) dataset provides global GDP economic data based on the state of the market in each subsequent year.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "SPATIALECON - Spatial Economic Data", "source_link": "https://doi.org/10.7927/H42V2D1C ", "strengths": "", "format": "ASCII, Grid, Excel, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "111 kilometers", "temporal_extent": "1990-01-01, 1995-01-01, 2000-01-01, 2005-01-01", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 156, "fields": {"dataset": "Global Roads Open Access Data Set (gROADS), v1 (1980\u200a\u2013\u200a2010)", "description": "The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.", "description_simplified": "The Global Roads Open Access Data Set (gROADS), v1 (1980\u200a\u2013\u200a2010) dataset provides global roads coverage using UN data.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "GROADS - Global Roads Open Access Data Set", "source_link": "https://doi.org/10.7927/H4VD6WCT ", "strengths": "Global coverage and shapefile format for GIS", "format": "Shapefile, Geodatabase, PDF, PNG", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1980-01-01 to 2010-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 157, "fields": {"dataset": "Unsatisfied Basic Needs, v1 (1998\u200a\u2013\u200a2001)", "description": "The Poverty Mapping Project: Unsatisfied Basic Needs data set consists of measures of household level wellbeing and access to basic needs (such as adequate housing conditions, water, electricity, sanitation, education, and employment) for subnational administrative units of numerous countries in Latin America: Argentina, Bolivia, Brazil, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Peru. The data products include shapefiles (vector data) and tabular data sets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN), Economic Commission for Latin America and the Caribbean (ECLAC), and Centro Internacional de Agricultura Tropical (CIAT).", "description_simplified": "The Poverty Mapping Project: Unsatisfied Basic Needs data set consists of measures of household level wellbeing and access to basic needs (such as adequate housing conditions, water, electricity, sanitation, education, and employment) for subnational administrative units of numerous countries in Latin America: Argentina, Bolivia, Brazil, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Peru. These data may aid in human dimensions EJ research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "PMP - Poverty Mapping Project", "source_link": "https://dx.doi.org/10.7927/H45X26V8", "strengths": "CSV and Shapefile available", "format": "CSV, Shapefile", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1998-01-01 to 2001-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 158, "fields": {"dataset": "Global Subnational Infant Mortality Rates, v1 (2000)", "description": "The Poverty Mapping Project: Global Subnational Infant Mortality Rates data set consists of estimates of infant mortality rates for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per 10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator), and a tabular data set of the same and associated data. Over 10,000 national and subnational units are represented in the tabular and grid data sets, while the shapefile uses approximately 1,000 units in order to protect the intellectual property of source data sets for Brazil, China, and Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Poverty Mapping Project: Global Subnational Infant Mortality Rates dataset consists of estimates of infant mortality rates for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first birthday for every 1,000 live births. These data may aid in poverty and human dimensions EJ research.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "PMP - Poverty Mapping Project", "source_link": "https://doi.org/10.7927/H4PZ56R2 ", "strengths": "Shapefile, grid, and CSV available", "format": "ASCII Grid, Excel, Shapefile, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "https://sedac.ciesin.columbia.edu/data/set/povmap-global-subnational-infant-mortality-rates-v2-01/maps", "spatial_resolution": "", "temporal_extent": "2000-01-01 to 2000-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 159, "fields": {"dataset": "Poverty and Food Security Case Studies, v1 (1998\u200a\u2013\u200a2002)", "description": "The Poverty Mapping Project: Poverty and Food Security Case Studies data set consists of small area estimates of poverty, inequality, food security and related measures for subnational administrative units in Mexico, Ecuador, Kenya, Malawi, Bangladesh, Sri Lanka, Nigeria and Vietnam. These data come from country level cases studies that examine poverty and food security from a spatial analysis perspective. The data products include shapefiles (vector data) and tabular data sets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and Centro Internacional de Agricultura Tropical (CIAT). The data set was originally produced by CIAT, International Maize and Wheat Improvement Center (CIMMYT), International Livestock Research Institute (ILRI), International Food Policy Research Institute (IFPRI), International Rice Research Institute (IRRI), International Water Management Institute (IWMI), and International Institute for Tropical Agriculture (IITA).", "description_simplified": "The Poverty Mapping Project: Poverty and Food Security Case Studies dataset consists of small area estimates of poverty, inequality, food security and related measures for Mexico, Ecuador, Kenya, Malawi, Bangladesh, Sri Lanka, Nigeria and Vietnam. These data come from country level cases studies that examine poverty and food security from a spatial analysis perspective.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "PMP - Poverty Mapping Project", "source_link": "https://doi.org/10.7927/H4FF3Q9B ", "strengths": "Individual case studies provide closer look at poverty", "format": "CSV, Shapefile", "geographic_coverage": "Mexico, Ecuador, Kenya, Malawi, Bangladesh, Sri Lanka, Nigeria, Vietnam", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1998-01-01 to 2002-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 160, "fields": {"dataset": "Country-Level GDP and Downscaled Projections Based on the SRES A1, A2, B1, and B2 Marker Scenarios, v1 (1990\u200a\u2013\u200a2100)", "description": "The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "description_simplified": "The Country-Level GDP and Downscaled Projections Based on the SRES A1, A2, B1, and B2 Marker Scenarios, v1 (1990\u200a\u2013\u200a2100) dataset provides economic (GDP) data based on different emissions scenarios related to climate change mitigation.", "indicators": "Human Dimensions", "intended_use": "Path B", "latency": "", "limitations": "projections based on original publishing date", "project": "SDP - Socioeconomic Downscaled Projections", "source_link": "https://doi.org/10.7927/H4XW4GQ1 ", "strengths": "global coverage", "format": "Excel", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "1990-01-01 to 2100-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 161, "fields": {"dataset": "SEDAC Popgrid Mapper", "description": "The POPGRID Viewer explores the intercomparison of Population Counts and Settlement mapping from leading global data sources. The POPGRID Data Collaborative aims to advance the use and impact of geospatial population and infrastructure data by bringing together and expanding the international community of data providers, users, and stakeholders from both the public and private sectors to accelerate the development and use of high quality, georeferenced data on population, human settlements, and infrastructure.", "description_simplified": "The SEDAC Popgrid Mapper enables users to visualize data and map layers related to socioeconomic, infrastructure, natural disasters, and environment and analyze potential impacts and exposure.", "indicators": "Human Dimensions", "intended_use": "Path A", "latency": "", "limitations": "No data download", "project": "", "source_link": "https://sedac.ciesin.columbia.edu/mapping/popgrid/", "strengths": "Viewer only for data visualization", "format": "", "geographic_coverage": "United States", "data_visualization": "viewer only", "spatial_resolution": "", "temporal_extent": "", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 162, "fields": {"dataset": "FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System", "description": "The FLDAS Global model (McNally et al. 2017) is a custom instance of the NASA Land Information System (LIS; http://lis.gsfc.nasa.gov/) that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. Adopting LIS allows FEWS NET to leverage existing land surface models and generate ensembles of soil moisture, ET, and other variables based on multiple meteorological inputs or land surface models. The goal of the FLDAS project is to achieve more effective use of limited available hydroclimatic observations and is designed to be adopted for routine use for FEWS NET decision support.", "description_simplified": "The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) dataset is a custom model incorporating various data and monitoring systems. These data may aid in food insecurity research.", "indicators": "Food Availability", "intended_use": "Path B", "latency": "", "limitations": "", "project": "FEWS NET - FEWS NET Land Data Assimilation System", "source_link": "https://ldas.gsfc.nasa.gov/fldas", "strengths": "Multiple datasets available", "format": "netCDF-3, KMZ, GeoTIFF", "geographic_coverage": "Global", "data_visualization": "visualization available", "spatial_resolution": "Varies", "temporal_extent": "Varies", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 163, "fields": {"dataset": "Global Reservoir and Dam Database, Version 1 (GRanDv1): Reservoirs, Revision 01", "description": "Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The reservoirs were delineated from high spatial resolution satellite imagery and are available as polygon shape files. The only attribute for the reservoirs is the area of the reservoir. The associated dams data set includes multiple attributes such as name of the dam and the impounded river, primary use, nearest city, area, and year of construction (or commissioning). While the main focus was to include all reservoirs with a storage capacity of more than 0.1 cubic kilometers, many smaller reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.", "description_simplified": "The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) dataset contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. This dataset provides the area of each reservoir. These data may aid in water availability research.", "indicators": "Water Availability", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "GRAND - Global Reservoir and Dam Database", "source_link": "https://doi.org/10.7927/H4HH6H08 ", "strengths": "Global coverage and shapefile format for GIS", "format": "Shapefile, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "SEDAC Map widget", "spatial_resolution": "", "temporal_extent": "2011-01-01 to 2011-12-31", "temporal_resolution": ""}}, {"model": "environmental_justice.environmentaljusticerow", "pk": 164, "fields": {"dataset": "Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01", "description": "The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.", "description_simplified": "The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) dataset contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. Information available for each dam include the name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). These data may aid in water availability research.", "indicators": "Water Availability", "intended_use": "Path B", "latency": "", "limitations": "Lacks recent data", "project": "GRAND - Global Reservoir and Dam Database", "source_link": "https://doi.org/10.7927/H4N877QK ", "strengths": "Shapefile format for use in GIS", "format": "Shapefile, PDF, PNG, WMS", "geographic_coverage": "Global", "data_visualization": "", "spatial_resolution": "", "temporal_extent": "2011-01-01 to 2011-12-31", "temporal_resolution": ""}}]