High-Resolution Poverty Maps in Sub-Saharan Africa

Abstract

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

arXiv commentChanged an author's affiliation, updated the narrowing method for DHS clusters leading to slight changes to all validation results

References in corpus (1)

• Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico

Cited by in corpus (4)

• Interpreting wealth distribution via poverty map inference using multimodal data
• Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks
• Graph-based Village Level Poverty Identification
• Mapping poverty at multiple geographical scales