Mapping Philippine Poverty using Machine Learning, Satellite Imagery, and Crowd-sourced Geospatial Information
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Updated
Nov 21, 2022 - Jupyter Notebook
Mapping Philippine Poverty using Machine Learning, Satellite Imagery, and Crowd-sourced Geospatial Information
Combing satellite imagery and machine learning methods to cluster ward-level povery in Gauteng, South Africa.
A basic walk-through of building a Fusion Tables map showing poverty levels and health centers in California counties.
Figures from ENGAGER Energy Poverty Encyclopedia entry on sub-regional indicators
Data and code repository from "Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data"
Anyone can be rich... for an instant.
Reexamining the World Bank's approach to mapping poverty with supervised learning
Using sequence and cluster analysis to analyse change over time in fuel poverty estimates.
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