diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 953aa79..34cbb10 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.9.3","generation_timestamp":"2023-10-13T14:12:14","documenter_version":"1.1.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.9.3","generation_timestamp":"2023-10-13T16:10:38","documenter_version":"1.1.1"}} \ No newline at end of file diff --git a/dev/index.html b/dev/index.html index 325f2ea..a53160e 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · OSMToolset
GitHub

OSMToolset.jl

Documentation for OSMToolset.jl

For details please go to the Reference section.

There is also a Visualization example.

Aknowledgments

<sup>This research was funded in whole or in part by [National Science Centre, Poland][2021/41/B/HS4/03349]. </sup>

+Home · OSMToolset
GitHub
diff --git a/dev/reference/index.html b/dev/reference/index.html index 734a3b2..60de4ab 100644 --- a/dev/reference/index.html +++ b/dev/reference/index.html @@ -1,6 +1,6 @@ -Reference · OSMToolset
GitHub

Reference

Scraping points-of-interest (POI)

OSMToolset.find_poi — Function
find_poi(filename::AbstractString; scrape_config::ScrapePOIConfig{T <: MetaPOI}=__builtin_poiconfig)

Generates a DataFrame with points of interests and from a given XML filename. The data frame will also contain the metadata from T for each POI.

The DataFrame can be later used with AttractivenessSpatIndex to build an attractivenss spatial index.

The attractiveness values for the index will be used ones from the scrape_config file. By default __builtin_poiconfig from __builtin_config_path will be used but you can define your own index.

source
OSMToolset.ScrapePOIConfig — Type
Represents the configuration of the data scraping process from OSM XML.

Only those pieces of data will be scraped that are defined here.

The configuration is defined in a DataFrame with the following columns: group, key, values, influence, range. Instead of the DataFrame a paths to a CSV file can be provided.

  • Constructors *
  • ScrapePOIConfig() - default inbuilt configuration for data scraping. Note that the default configuration can change with library updates. This will use AttractivenessMetaPOI as meta data.
  • ScrapePOIConfig{T <: MetaPOI}(filename::AbstractString) - use a CSV file with configuration
  • ScrapePOIConfig{T <: MetaPOI}(df::DataFrame) - use a DataFrame

When the T parameter is not provided AttractivenessMetaPOI will be used. When you do not want to use metadata provide NoneMetaPOI as T

source
OSMToolset.MetaPOI — Type
abstract type MetaPOI end

A base time for representing metadata related to a POI location.

source
OSMToolset.AttractivenessMetaPOI — Type
struct AttractivenessMetaPOI <: MetaPOI

A subtype of MetaPOI that contains metadata for attractiveness (the default configuration of scraping). This assumes that the metadata is stored in a CSV file with the following columns: key, values, group, influence, range.

source

Measuring Attractiveness Spatial Index

OSMToolset.AttractivenessSpatIndex — Type
AttractivenessSpatIndex{T <: MetaPOI, F <: Function}(filename::AbstractString, get_range::Function=get_attractiveness_range, get_group::Function=get_attractiveness_group)
-AttractivenessSpatIndex{T <: MetaPOI, F <: Function}(df::AbstractDataFrame, get_range::Function=get_attractiveness_range, get_group::Function=get_attractiveness_group)

Builds an attractivness spatial index basing on data in some CSV file or a DataFrame

Assuming that T is of typw AttractivenessMetaPOI, the CSV file or DataFrame should have the following columns: - group - data group in attractiveness index, each group name creates attractiveness dimension - key - key in the XML file <tag> - values - values in the <tag> (a star "*" catches all values) - influence - strength of influence - range - maximum influence range in meters

When a DataFrame is provided the additional parameter refLLA can be provided for the reference LLA coordinates in the spatial index. The spatial index works in the ENU coordinate system.

If T is not provided AttractivenessMetaPOI will be used as the default metadata type.

The type F represents the attractiveness group function provided as get_group = (a::T) -> :somegroup.

source
OSMToolset.attractiveness — Function
attractiveness(sindex::AttractivenessSpatIndex{T}, lattitude::Number, longitude::Number; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness,  distance::Function=OpenStreetMapX.distance, explain::Bool=false)  where T <: MetaPOI

Returns the multidimensional attractiveness measure for the given spatial index sindex and lattitude and longitude. The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.

If explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.

source
attractiveness(sindex::AttractivenessSpatIndex{T}, lla::LLA; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI

Returns the multidimensional attractiveness measure for the given spatial index sindex and LLA coordinates. The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.

If explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.

source
attractiveness(sindex::AttractivenessSpatIndex{T}, enu::ENU; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI

Returns the multidimensional attractiveness measure for the given spatial index sindex and enu cooridanates. Note that the enu coordinates must use sindex.refLLA as the reference point. Hence the enu coordinates need to be calculated eg. using ENU(lla,sindex.refLLA). The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.

If explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.

source
OSMToolset.calculate_attractiveness — Function
calculate_attractiveness(a::AttractivenessMetaPOI, poidistance::Number)

Default function used to calculate the attractiveness for AttractivenessMetaPOI on the base of distance. You might want to provide your own implementation and than pass it as a parameter whe using the attractiveness function.

source
OSMToolset.get_attractiveness_group — Function
get_attractiveness_group(a::AttractivenessMetaPOI)

Default group for AttractivenessMetaPOI which is a.group.

source
get_attractiveness_group(a::NoneMetaPOI)

Default group for NoneMetaPOI (NoneMetaPOI).

source
OSMToolset.clean_pois_by_group — Function
clean_pois_by_group(df::DataFrame)

For data imported via AttractivenessMetaPOI the function will return only the most attractive POI for each group. This is useful when you want to remove duplicate entries for the same node.

source

Tiling OSM file

OSMToolset.calc_tiling — Method
calc_tiling(filename::AbstractString, latTileSize::Float64, lonTileSize::Float64)

Calculates recommended bounds, number of rows and columns for a given filename and size of tile latTileSize x lonTileSize.

source
OSMToolset.calc_tiling — Method
calc_tiling(bounds::Bounds, latTileSize::Float64, lonTileSize::Float64)

Calculates recommended bounds, number of rows and columns for a given bounds and size of tile latTileSize x lonTileSize.

source
OSMToolset.tile_osm_file — Method
tile_osm_file(filename::AbstractString, [bounds::Bounds]; nrow::Integer, ncol::Integer, [out_dir::AbstractString]

Provide the tiling functionality for maps. A filename will be open for processing and the tiling will be done around given bounds. If bounds are not given they will be calculated using getbounds function. The tiling will be performed with a matrix having nrow rows and ncol columns. The output will be written to the folder name out_dir. If none out_dir is given than as the output is written to where filename is located.

Returns a Matrix{String} of size nrow x ncol containing the names of the files created.

source
OSMToolset.getbounds — Method
getbounds(filename::AbstractString)::Bounds

Returns Bounds that can be found in the first 10 lines of the OSM file named 'filename'

source

Helper functions

OSMToolset.FloatLon — Type
This is an AbstractFloat type representing geographic longitude as the values may wrap around
source
OSMToolset.Node — Type
Node
+Reference · OSMToolset
GitHub
Reference
Scraping points-of-interest (POI)
OSMToolset.find_poi — Function
find_poi(filename::AbstractString; scrape_config::ScrapePOIConfig{T <: MetaPOI}=__builtin_poiconfig)
Generates a DataFrame with points of interests and from a given XML filename. The data frame will also contain the metadata from T for each POI.
The DataFrame can be later used with AttractivenessSpatIndex to build an attractivenss spatial index.
The attractiveness values for the index will be used ones from the scrape_config file. By default __builtin_poiconfig from __builtin_config_path will be used but you can define your own index.
source
OSMToolset.ScrapePOIConfig — Type
Represents the configuration of the data scraping process from OSM XML.
Only those pieces of data will be scraped that are defined here.
The configuration is defined in a DataFrame with the following columns: group, key, values, influence, range. Instead of the DataFrame a paths to a CSV file can be provided.
  • Constructors *
  • ScrapePOIConfig() - default inbuilt configuration for data scraping.   Note that the default configuration can change with library updates.  This will use AttractivenessMetaPOI as meta data.
  • ScrapePOIConfig{T <: MetaPOI}(filename::AbstractString) - use a CSV file with configuration
  • ScrapePOIConfig{T <: MetaPOI}(df::DataFrame) - use a DataFrame
When the T parameter is not provided AttractivenessMetaPOI will be used. When you do not want to use metadata provide NoneMetaPOI as T
source
OSMToolset.MetaPOI — Type
abstract type MetaPOI end
A base time for representing metadata related to a POI location.
source
OSMToolset.AttractivenessMetaPOI — Type
struct AttractivenessMetaPOI <: MetaPOI
A subtype of MetaPOI that contains metadata for attractiveness  (the default configuration of scraping). This assumes that the metadata is stored in a CSV file with the following columns: key, values, group, influence, range.
source
Measuring Attractiveness Spatial Index
OSMToolset.AttractivenessSpatIndex — Type
AttractivenessSpatIndex{T <: MetaPOI, F <: Function}(filename::AbstractString, get_range::Function=get_attractiveness_range, get_group::Function=get_attractiveness_group)
+AttractivenessSpatIndex{T <: MetaPOI, F <: Function}(df::AbstractDataFrame, get_range::Function=get_attractiveness_range, get_group::Function=get_attractiveness_group)
Builds an attractivness spatial index basing on data in some CSV file or a DataFrame 
Assuming that T is of typw AttractivenessMetaPOI,  the CSV file or DataFrame  should have the following columns:     - group - data group in attractiveness index, each group name creates attractiveness dimension     - key - key in the XML file <tag>     - values - values in the <tag> (a star "*" catches all values)     - influence - strength of influence       - range - maximum influence range in meters 
When a DataFrame is provided the additional parameter refLLA can be provided for the reference  LLA coordinates in the spatial index. The spatial index works in the ENU coordinate system.
If T is not provided AttractivenessMetaPOI will be used as the default metadata type.
The type F represents the attractiveness group function provided as  get_group = (a::T) -> :somegroup.
source
OSMToolset.attractiveness — Function
attractiveness(sindex::AttractivenessSpatIndex{T}, lattitude::Number, longitude::Number; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness,  distance::Function=OpenStreetMapX.distance, explain::Bool=false)  where T <: MetaPOI
Returns the multidimensional attractiveness measure for the given spatial index sindex and lattitude and longitude. The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within  the attractiveness range.  The function calculate_attractiveness(a::T, poidistance::Number) will be used  to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to  calculate the distance between point pairs.
If explain is set to true the result will additionally contain details  about POIs used to calculate the attractiveness.
source
attractiveness(sindex::AttractivenessSpatIndex{T}, lla::LLA; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI
Returns the multidimensional attractiveness measure for the given spatial index sindex and LLA coordinates. The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within  the attractiveness range.  The function calculate_attractiveness(a::T, poidistance::Number) will be used  to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to  calculate the distance between point pairs.
If explain is set to true the result will additionally contain details  about POIs used to calculate the attractiveness.
source
attractiveness(sindex::AttractivenessSpatIndex{T}, enu::ENU; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI
Returns the multidimensional attractiveness measure for the given spatial index sindex and enu cooridanates. Note that the enu coordinates must use sindex.refLLA as the reference point.  Hence the enu coordinates need to be calculated eg. using ENU(lla,sindex.refLLA). The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within  the attractiveness range.  The function calculate_attractiveness(a::T, poidistance::Number) will be used  to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to  calculate the distance between point pairs.
If explain is set to true the result will additionally contain details  about POIs used to calculate the attractiveness.
source
OSMToolset.calculate_attractiveness — Function
calculate_attractiveness(a::AttractivenessMetaPOI, poidistance::Number)
Default function used to calculate the attractiveness for AttractivenessMetaPOI on the base of distance. You might want to provide your own implementation and than pass it as a parameter  whe using the attractiveness function.
source
OSMToolset.get_attractiveness_group — Function
get_attractiveness_group(a::AttractivenessMetaPOI)
Default group for AttractivenessMetaPOI which is a.group.
source
get_attractiveness_group(a::NoneMetaPOI)
Default group for NoneMetaPOI (NoneMetaPOI).
source
OSMToolset.clean_pois_by_group — Function
clean_pois_by_group(df::DataFrame)
For data imported via AttractivenessMetaPOI the function will return only the most attractive POI for each group. This is useful when you want to remove duplicate entries for the same node.
source
Tiling OSM file
OSMToolset.calc_tiling — Method
calc_tiling(filename::AbstractString, latTileSize::Float64, lonTileSize::Float64)
Calculates recommended bounds, number of rows and columns for a given filename and size of tile latTileSize x lonTileSize.
source
OSMToolset.calc_tiling — Method
calc_tiling(bounds::Bounds, latTileSize::Float64, lonTileSize::Float64)
Calculates recommended bounds, number of rows and columns for a given bounds and size of tile latTileSize x lonTileSize.
source
OSMToolset.tile_osm_file — Method
tile_osm_file(filename::AbstractString, [bounds::Bounds]; nrow::Integer, ncol::Integer, [out_dir::AbstractString]
Provide the tiling functionality for maps. A filename will be open for processing and the tiling will be done around given bounds. If bounds are not given they will be calculated using getbounds function. The tiling will be performed with a matrix having nrow rows and ncol columns. The output will be written to the folder name out_dir.   If none out_dir is given than as the output is written to where filename is located.
Returns a Matrix{String} of size nrow x ncol containing the names of the files created.
source
OSMToolset.getbounds — Method
getbounds(filename::AbstractString)::Bounds
Returns Bounds that can be found in the first 10 lines of the OSM file named 'filename'
source
Helper functions
OSMToolset.FloatLon — Type
This is an AbstractFloat type representing geographic longitude as the values may wrap around
source
OSMToolset.Node — Type
Node
 
 A node is a point in the map. It has an id, a latitude and a longitude.
-All nodes need to be stored in memory in this format.
source

+All nodes need to be stored in memory in this format.
source
diff --git a/dev/search_index.js b/dev/search_index.js index 42cdf35..8a5bf5f 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"visualize/#Visualization","page":"Visualization","title":"Visualization","text":"","category":"section"},{"location":"visualize/#How-to-visualize-the-data","page":"Visualization","title":"How to visualize the data","text":"","category":"section"},{"location":"visualize/","page":"Visualization","title":"Visualization","text":"Below is a sample piece of code that can be used to generate a visualization of POIs on a map.","category":"page"},{"location":"visualize/","page":"Visualization","title":"Visualization","text":"using PyCall\nusing Colors\nusing OSMToolset\n\nfile = sample_osm_file()\ndf = find_poi(file) \nix = AttractivenessSpatIndex(df);\n\nflm = pyimport(\"folium\");\n\ncolrs = distinguishable_colors(length(ix.measures), [RGB(0.1,0.2,0.4)])\nclass2col = Dict(ix.measures .=> colrs);\n\nm = flm.Map(tiles = \"Stamen Toner\")\nline = 0\n\nfor row in eachrow(df) \n line += 1\n info = \"$(row.group):$(row.key)=$(row.value)\"\n \n k = findfirst(==(Symbol(row.group)), ix.measures)\n flm.Circle((row.lat, row.lon), color=\"#$(hex(colrs[k]))\",radius=row.influence,\n fill_color=\"#$(hex(colrs[k]))\", fill_opacity=0.06, tooltip=info).add_to(m)\n \nend\nbb = getbounds(file)\nbounds = [(bb.minlat, Float64(bb.minlon)), (bb.maxlat, Float64(bb.maxlon))]\nm.fit_bounds(bounds)\nflm.Rectangle(bounds, color=\"blue\",weight=2).add_to(m)\n\nm","category":"page"},{"location":"visualize/","page":"Visualization","title":"Visualization","text":"The obtained result should be similar to the picture below.","category":"page"},{"location":"visualize/","page":"Visualization","title":"Visualization","text":"(Image: POI Visualization)","category":"page"},{"location":"reference/#Reference","page":"Reference","title":"Reference","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"CurrentModule = OSMToolset\nDocTestSetup = quote\n using OSMToolset\nend","category":"page"},{"location":"reference/#Scraping-points-of-interest-(POI)","page":"Reference","title":"Scraping points-of-interest (POI)","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"find_poi\nScrapePOIConfig\nMetaPOI\nNoneMetaPOI\nAttractivenessMetaPOI","category":"page"},{"location":"reference/#OSMToolset.find_poi","page":"Reference","title":"OSMToolset.find_poi","text":"find_poi(filename::AbstractString; scrape_config::ScrapePOIConfig{T <: MetaPOI}=__builtin_poiconfig)\n\nGenerates a DataFrame with points of interests and from a given XML filename. The data frame will also contain the metadata from T for each POI.\n\nThe DataFrame can be later used with AttractivenessSpatIndex to build an attractivenss spatial index.\n\nThe attractiveness values for the index will be used ones from the scrape_config file. By default __builtin_poiconfig from __builtin_config_path will be used but you can define your own index.\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.ScrapePOIConfig","page":"Reference","title":"OSMToolset.ScrapePOIConfig","text":"Represents the configuration of the data scraping process from OSM XML.\n\nOnly those pieces of data will be scraped that are defined here.\n\nThe configuration is defined in a DataFrame with the following columns: group, key, values, influence, range. Instead of the DataFrame a paths to a CSV file can be provided.\n\nConstructors *\nScrapePOIConfig() - default inbuilt configuration for data scraping. Note that the default configuration can change with library updates. This will use AttractivenessMetaPOI as meta data.\nScrapePOIConfig{T <: MetaPOI}(filename::AbstractString) - use a CSV file with configuration\nScrapePOIConfig{T <: MetaPOI}(df::DataFrame) - use a DataFrame\n\nWhen the T parameter is not provided AttractivenessMetaPOI will be used. When you do not want to use metadata provide NoneMetaPOI as T\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.MetaPOI","page":"Reference","title":"OSMToolset.MetaPOI","text":"abstract type MetaPOI end\n\nA base time for representing metadata related to a POI location.\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.NoneMetaPOI","page":"Reference","title":"OSMToolset.NoneMetaPOI","text":"struct NoneMetaPOI <: MetaPOI; end\n\nA subtype of MetaPOI that does not contain any metadata.\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.AttractivenessMetaPOI","page":"Reference","title":"OSMToolset.AttractivenessMetaPOI","text":"struct AttractivenessMetaPOI <: MetaPOI\n\nA subtype of MetaPOI that contains metadata for attractiveness (the default configuration of scraping). This assumes that the metadata is stored in a CSV file with the following columns: key, values, group, influence, range.\n\n\n\n\n\n","category":"type"},{"location":"reference/#Measuring-Attractiveness-Spatial-Index","page":"Reference","title":"Measuring Attractiveness Spatial Index","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"AttractivenessSpatIndex\nattractiveness\ncalculate_attractiveness\nget_attractiveness_group\nclean_pois_by_group","category":"page"},{"location":"reference/#OSMToolset.AttractivenessSpatIndex","page":"Reference","title":"OSMToolset.AttractivenessSpatIndex","text":"AttractivenessSpatIndex{T <: MetaPOI, F <: Function}(filename::AbstractString, get_range::Function=get_attractiveness_range, get_group::Function=get_attractiveness_group)\nAttractivenessSpatIndex{T <: MetaPOI, F <: Function}(df::AbstractDataFrame, get_range::Function=get_attractiveness_range, get_group::Function=get_attractiveness_group)\n\nBuilds an attractivness spatial index basing on data in some CSV file or a DataFrame \n\nAssuming that T is of typw AttractivenessMetaPOI, the CSV file or DataFrame should have the following columns: - group - data group in attractiveness index, each group name creates attractiveness dimension - key - key in the XML file - values - values in the (a star \"*\" catches all values) - influence - strength of influence - range - maximum influence range in meters \n\nWhen a DataFrame is provided the additional parameter refLLA can be provided for the reference LLA coordinates in the spatial index. The spatial index works in the ENU coordinate system.\n\nIf T is not provided AttractivenessMetaPOI will be used as the default metadata type.\n\nThe type F represents the attractiveness group function provided as get_group = (a::T) -> :somegroup.\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.attractiveness","page":"Reference","title":"OSMToolset.attractiveness","text":"attractiveness(sindex::AttractivenessSpatIndex{T}, lattitude::Number, longitude::Number; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI\n\nReturns the multidimensional attractiveness measure for the given spatial index sindex and lattitude and longitude. The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.\n\nIf explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.\n\n\n\n\n\nattractiveness(sindex::AttractivenessSpatIndex{T}, lla::LLA; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI\n\nReturns the multidimensional attractiveness measure for the given spatial index sindex and LLA coordinates. The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.\n\nIf explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.\n\n\n\n\n\nattractiveness(sindex::AttractivenessSpatIndex{T}, enu::ENU; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI\n\nReturns the multidimensional attractiveness measure for the given spatial index sindex and enu cooridanates. Note that the enu coordinates must use sindex.refLLA as the reference point. Hence the enu coordinates need to be calculated eg. using ENU(lla,sindex.refLLA). The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.\n\nIf explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.calculate_attractiveness","page":"Reference","title":"OSMToolset.calculate_attractiveness","text":"calculate_attractiveness(a::AttractivenessMetaPOI, poidistance::Number)\n\nDefault function used to calculate the attractiveness for AttractivenessMetaPOI on the base of distance. You might want to provide your own implementation and than pass it as a parameter whe using the attractiveness function.\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.get_attractiveness_group","page":"Reference","title":"OSMToolset.get_attractiveness_group","text":"get_attractiveness_group(a::AttractivenessMetaPOI)\n\nDefault group for AttractivenessMetaPOI which is a.group.\n\n\n\n\n\nget_attractiveness_group(a::NoneMetaPOI)\n\nDefault group for NoneMetaPOI (NoneMetaPOI).\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.clean_pois_by_group","page":"Reference","title":"OSMToolset.clean_pois_by_group","text":"clean_pois_by_group(df::DataFrame)\n\nFor data imported via AttractivenessMetaPOI the function will return only the most attractive POI for each group. This is useful when you want to remove duplicate entries for the same node.\n\n\n\n\n\n","category":"function"},{"location":"reference/#Tiling-OSM-file","page":"Reference","title":"Tiling OSM file","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"calc_tiling(::AbstractString, ::Float64, ::Float64) \ncalc_tiling(::OSMToolset.Bounds, ::Float64, ::Float64)\ntile_osm_file(::AbstractString, ::Bounds; ::Integer, ::Integer, ::AbstractString)\nBounds\ngetbounds(::AbstractString)","category":"page"},{"location":"reference/#OSMToolset.calc_tiling-Tuple{AbstractString, Float64, Float64}","page":"Reference","title":"OSMToolset.calc_tiling","text":"calc_tiling(filename::AbstractString, latTileSize::Float64, lonTileSize::Float64)\n\nCalculates recommended bounds, number of rows and columns for a given filename and size of tile latTileSize x lonTileSize.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OSMToolset.calc_tiling-Tuple{Bounds, Float64, Float64}","page":"Reference","title":"OSMToolset.calc_tiling","text":"calc_tiling(bounds::Bounds, latTileSize::Float64, lonTileSize::Float64)\n\nCalculates recommended bounds, number of rows and columns for a given bounds and size of tile latTileSize x lonTileSize.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OSMToolset.tile_osm_file-Tuple{AbstractString, Bounds}","page":"Reference","title":"OSMToolset.tile_osm_file","text":"tile_osm_file(filename::AbstractString, [bounds::Bounds]; nrow::Integer, ncol::Integer, [out_dir::AbstractString]\n\nProvide the tiling functionality for maps. A filename will be open for processing and the tiling will be done around given bounds. If bounds are not given they will be calculated using getbounds function. The tiling will be performed with a matrix having nrow rows and ncol columns. The output will be written to the folder name out_dir. If none out_dir is given than as the output is written to where filename is located.\n\nReturns a Matrix{String} of size nrow x ncol containing the names of the files created.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OSMToolset.Bounds","page":"Reference","title":"OSMToolset.Bounds","text":"A range of geographic coordinates for a map\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.getbounds-Tuple{AbstractString}","page":"Reference","title":"OSMToolset.getbounds","text":"getbounds(filename::AbstractString)::Bounds\n\nReturns Bounds that can be found in the first 10 lines of the OSM file named 'filename'\n\n\n\n\n\n","category":"method"},{"location":"reference/","page":"Reference","title":"Reference","text":"Helper functions","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"sample_osm_file\nFloatLon\nOSMToolset.Node","category":"page"},{"location":"reference/#OSMToolset.sample_osm_file","page":"Reference","title":"OSMToolset.sample_osm_file","text":"Provides location of sample OSM file for tests\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.FloatLon","page":"Reference","title":"OSMToolset.FloatLon","text":"This is an AbstractFloat type representing geographic longitude as the values may wrap around\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.Node","page":"Reference","title":"OSMToolset.Node","text":"Node\n\nA node is a point in the map. It has an id, a latitude and a longitude.\nAll nodes need to be stored in memory in this format.\n\n\n\n\n\n","category":"type"},{"location":"","page":"Home","title":"Home","text":"CurrentModule = OSMToolset","category":"page"},{"location":"#OSMToolset.jl","page":"Home","title":"OSMToolset.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Documentation for OSMToolset.jl","category":"page"},{"location":"","page":"Home","title":"Home","text":"For details please go to the Reference section.","category":"page"},{"location":"","page":"Home","title":"Home","text":"There is also a Visualization example.","category":"page"},{"location":"#Aknowledgments","page":"Home","title":"Aknowledgments","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"This research was funded in whole or in part by [National Science Centre, Poland][2021/41/B/HS4/03349]. ","category":"page"}] +[{"location":"visualize/#Visualization","page":"Visualization","title":"Visualization","text":"","category":"section"},{"location":"visualize/#How-to-visualize-the-data","page":"Visualization","title":"How to visualize the data","text":"","category":"section"},{"location":"visualize/","page":"Visualization","title":"Visualization","text":"Below is a sample piece of code that can be used to generate a visualization of POIs on a map.","category":"page"},{"location":"visualize/","page":"Visualization","title":"Visualization","text":"using PyCall\nusing Colors\nusing OSMToolset\n\nfile = sample_osm_file()\ndf = find_poi(file) \nix = AttractivenessSpatIndex(df);\n\nflm = pyimport(\"folium\");\n\ncolrs = distinguishable_colors(length(ix.measures), [RGB(0.1,0.2,0.4)])\nclass2col = Dict(ix.measures .=> colrs);\n\nm = flm.Map(tiles = \"Stamen Toner\")\nline = 0\n\nfor row in eachrow(df) \n line += 1\n info = \"$(row.group):$(row.key)=$(row.value)\"\n \n k = findfirst(==(Symbol(row.group)), ix.measures)\n flm.Circle((row.lat, row.lon), color=\"#$(hex(colrs[k]))\",radius=row.influence,\n fill_color=\"#$(hex(colrs[k]))\", fill_opacity=0.06, tooltip=info).add_to(m)\n \nend\nbb = getbounds(file)\nbounds = [(bb.minlat, Float64(bb.minlon)), (bb.maxlat, Float64(bb.maxlon))]\nm.fit_bounds(bounds)\nflm.Rectangle(bounds, color=\"blue\",weight=2).add_to(m)\n\nm","category":"page"},{"location":"visualize/","page":"Visualization","title":"Visualization","text":"The obtained result should be similar to the picture below.","category":"page"},{"location":"visualize/","page":"Visualization","title":"Visualization","text":"(Image: POI Visualization)","category":"page"},{"location":"reference/#Reference","page":"Reference","title":"Reference","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"CurrentModule = OSMToolset\nDocTestSetup = quote\n using OSMToolset\nend","category":"page"},{"location":"reference/#Scraping-points-of-interest-(POI)","page":"Reference","title":"Scraping points-of-interest (POI)","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"find_poi\nScrapePOIConfig\nMetaPOI\nNoneMetaPOI\nAttractivenessMetaPOI","category":"page"},{"location":"reference/#OSMToolset.find_poi","page":"Reference","title":"OSMToolset.find_poi","text":"find_poi(filename::AbstractString; scrape_config::ScrapePOIConfig{T <: MetaPOI}=__builtin_poiconfig)\n\nGenerates a DataFrame with points of interests and from a given XML filename. The data frame will also contain the metadata from T for each POI.\n\nThe DataFrame can be later used with AttractivenessSpatIndex to build an attractivenss spatial index.\n\nThe attractiveness values for the index will be used ones from the scrape_config file. By default __builtin_poiconfig from __builtin_config_path will be used but you can define your own index.\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.ScrapePOIConfig","page":"Reference","title":"OSMToolset.ScrapePOIConfig","text":"Represents the configuration of the data scraping process from OSM XML.\n\nOnly those pieces of data will be scraped that are defined here.\n\nThe configuration is defined in a DataFrame with the following columns: group, key, values, influence, range. Instead of the DataFrame a paths to a CSV file can be provided.\n\nConstructors *\nScrapePOIConfig() - default inbuilt configuration for data scraping. Note that the default configuration can change with library updates. This will use AttractivenessMetaPOI as meta data.\nScrapePOIConfig{T <: MetaPOI}(filename::AbstractString) - use a CSV file with configuration\nScrapePOIConfig{T <: MetaPOI}(df::DataFrame) - use a DataFrame\n\nWhen the T parameter is not provided AttractivenessMetaPOI will be used. When you do not want to use metadata provide NoneMetaPOI as T\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.MetaPOI","page":"Reference","title":"OSMToolset.MetaPOI","text":"abstract type MetaPOI end\n\nA base time for representing metadata related to a POI location.\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.NoneMetaPOI","page":"Reference","title":"OSMToolset.NoneMetaPOI","text":"struct NoneMetaPOI <: MetaPOI; end\n\nA subtype of MetaPOI that does not contain any metadata.\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.AttractivenessMetaPOI","page":"Reference","title":"OSMToolset.AttractivenessMetaPOI","text":"struct AttractivenessMetaPOI <: MetaPOI\n\nA subtype of MetaPOI that contains metadata for attractiveness (the default configuration of scraping). This assumes that the metadata is stored in a CSV file with the following columns: key, values, group, influence, range.\n\n\n\n\n\n","category":"type"},{"location":"reference/#Measuring-Attractiveness-Spatial-Index","page":"Reference","title":"Measuring Attractiveness Spatial Index","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"AttractivenessSpatIndex\nattractiveness\ncalculate_attractiveness\nget_attractiveness_group\nclean_pois_by_group","category":"page"},{"location":"reference/#OSMToolset.AttractivenessSpatIndex","page":"Reference","title":"OSMToolset.AttractivenessSpatIndex","text":"AttractivenessSpatIndex{T <: MetaPOI, F <: Function}(filename::AbstractString, get_range::Function=get_attractiveness_range, get_group::Function=get_attractiveness_group)\nAttractivenessSpatIndex{T <: MetaPOI, F <: Function}(df::AbstractDataFrame, get_range::Function=get_attractiveness_range, get_group::Function=get_attractiveness_group)\n\nBuilds an attractivness spatial index basing on data in some CSV file or a DataFrame \n\nAssuming that T is of typw AttractivenessMetaPOI, the CSV file or DataFrame should have the following columns: - group - data group in attractiveness index, each group name creates attractiveness dimension - key - key in the XML file - values - values in the (a star \"*\" catches all values) - influence - strength of influence - range - maximum influence range in meters \n\nWhen a DataFrame is provided the additional parameter refLLA can be provided for the reference LLA coordinates in the spatial index. The spatial index works in the ENU coordinate system.\n\nIf T is not provided AttractivenessMetaPOI will be used as the default metadata type.\n\nThe type F represents the attractiveness group function provided as get_group = (a::T) -> :somegroup.\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.attractiveness","page":"Reference","title":"OSMToolset.attractiveness","text":"attractiveness(sindex::AttractivenessSpatIndex{T}, lattitude::Number, longitude::Number; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI\n\nReturns the multidimensional attractiveness measure for the given spatial index sindex and lattitude and longitude. The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.\n\nIf explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.\n\n\n\n\n\nattractiveness(sindex::AttractivenessSpatIndex{T}, lla::LLA; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI\n\nReturns the multidimensional attractiveness measure for the given spatial index sindex and LLA coordinates. The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.\n\nIf explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.\n\n\n\n\n\nattractiveness(sindex::AttractivenessSpatIndex{T}, enu::ENU; aggregator::Function=sum, calculate_attractiveness::Function=calculate_attractiveness, distance::Function=OpenStreetMapX.distance, explain::Bool=false) where T <: MetaPOI\n\nReturns the multidimensional attractiveness measure for the given spatial index sindex and enu cooridanates. Note that the enu coordinates must use sindex.refLLA as the reference point. Hence the enu coordinates need to be calculated eg. using ENU(lla,sindex.refLLA). The aggregator function will be used to aggregate the attractiveness values. The aggreagation is required as more than one point of interest can be found within the attractiveness range. The function calculate_attractiveness(a::T, poidistance::Number) will be used to calculate the attractiveness on the base of metadata and distance. The distance function distance(a::ENU, b::ENU) is used to calculate the distance between point pairs.\n\nIf explain is set to true the result will additionally contain details about POIs used to calculate the attractiveness.\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.calculate_attractiveness","page":"Reference","title":"OSMToolset.calculate_attractiveness","text":"calculate_attractiveness(a::AttractivenessMetaPOI, poidistance::Number)\n\nDefault function used to calculate the attractiveness for AttractivenessMetaPOI on the base of distance. You might want to provide your own implementation and than pass it as a parameter whe using the attractiveness function.\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.get_attractiveness_group","page":"Reference","title":"OSMToolset.get_attractiveness_group","text":"get_attractiveness_group(a::AttractivenessMetaPOI)\n\nDefault group for AttractivenessMetaPOI which is a.group.\n\n\n\n\n\nget_attractiveness_group(a::NoneMetaPOI)\n\nDefault group for NoneMetaPOI (NoneMetaPOI).\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.clean_pois_by_group","page":"Reference","title":"OSMToolset.clean_pois_by_group","text":"clean_pois_by_group(df::DataFrame)\n\nFor data imported via AttractivenessMetaPOI the function will return only the most attractive POI for each group. This is useful when you want to remove duplicate entries for the same node.\n\n\n\n\n\n","category":"function"},{"location":"reference/#Tiling-OSM-file","page":"Reference","title":"Tiling OSM file","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"calc_tiling(::AbstractString, ::Float64, ::Float64) \ncalc_tiling(::OSMToolset.Bounds, ::Float64, ::Float64)\ntile_osm_file(::AbstractString, ::Bounds; ::Integer, ::Integer, ::AbstractString)\nBounds\ngetbounds(::AbstractString)","category":"page"},{"location":"reference/#OSMToolset.calc_tiling-Tuple{AbstractString, Float64, Float64}","page":"Reference","title":"OSMToolset.calc_tiling","text":"calc_tiling(filename::AbstractString, latTileSize::Float64, lonTileSize::Float64)\n\nCalculates recommended bounds, number of rows and columns for a given filename and size of tile latTileSize x lonTileSize.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OSMToolset.calc_tiling-Tuple{Bounds, Float64, Float64}","page":"Reference","title":"OSMToolset.calc_tiling","text":"calc_tiling(bounds::Bounds, latTileSize::Float64, lonTileSize::Float64)\n\nCalculates recommended bounds, number of rows and columns for a given bounds and size of tile latTileSize x lonTileSize.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OSMToolset.tile_osm_file-Tuple{AbstractString, Bounds}","page":"Reference","title":"OSMToolset.tile_osm_file","text":"tile_osm_file(filename::AbstractString, [bounds::Bounds]; nrow::Integer, ncol::Integer, [out_dir::AbstractString]\n\nProvide the tiling functionality for maps. A filename will be open for processing and the tiling will be done around given bounds. If bounds are not given they will be calculated using getbounds function. The tiling will be performed with a matrix having nrow rows and ncol columns. The output will be written to the folder name out_dir. If none out_dir is given than as the output is written to where filename is located.\n\nReturns a Matrix{String} of size nrow x ncol containing the names of the files created.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OSMToolset.Bounds","page":"Reference","title":"OSMToolset.Bounds","text":"A range of geographic coordinates for a map\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.getbounds-Tuple{AbstractString}","page":"Reference","title":"OSMToolset.getbounds","text":"getbounds(filename::AbstractString)::Bounds\n\nReturns Bounds that can be found in the first 10 lines of the OSM file named 'filename'\n\n\n\n\n\n","category":"method"},{"location":"reference/","page":"Reference","title":"Reference","text":"Helper functions","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"sample_osm_file\nFloatLon\nOSMToolset.Node","category":"page"},{"location":"reference/#OSMToolset.sample_osm_file","page":"Reference","title":"OSMToolset.sample_osm_file","text":"Provides location of sample OSM file for tests\n\n\n\n\n\n","category":"function"},{"location":"reference/#OSMToolset.FloatLon","page":"Reference","title":"OSMToolset.FloatLon","text":"This is an AbstractFloat type representing geographic longitude as the values may wrap around\n\n\n\n\n\n","category":"type"},{"location":"reference/#OSMToolset.Node","page":"Reference","title":"OSMToolset.Node","text":"Node\n\nA node is a point in the map. It has an id, a latitude and a longitude.\nAll nodes need to be stored in memory in this format.\n\n\n\n\n\n","category":"type"},{"location":"","page":"Home","title":"Home","text":"CurrentModule = OSMToolset","category":"page"},{"location":"#OSMToolset.jl","page":"Home","title":"OSMToolset.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Documentation for OSMToolset.jl","category":"page"},{"location":"","page":"Home","title":"Home","text":"For details please go to the Reference section.","category":"page"},{"location":"","page":"Home","title":"Home","text":"There is also a Visualization example.","category":"page"},{"location":"#Aknowledgments","page":"Home","title":"Aknowledgments","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"This research was funded by National Science Centre, Poland grant number 2021/41/B/HS4/03349. ","category":"page"}] } diff --git a/dev/visualize/index.html b/dev/visualize/index.html index 2d718a9..2e0d52d 100644 --- a/dev/visualize/index.html +++ b/dev/visualize/index.html @@ -29,4 +29,4 @@ m.fit_bounds(bounds) flm.Rectangle(bounds, color="blue",weight=2).add_to(m) -m

The obtained result should be similar to the picture below.

POI Visualization

+m

The obtained result should be similar to the picture below.

POI Visualization