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.
Data and code repository from "Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data"
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
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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