DeepAI AI Chat
Log In Sign Up

High-Resolution Poverty Maps in Sub-Saharan Africa

by   Kamwoo Lee, et al.

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.


page 7

page 23

page 27

page 35

page 36

page 37

page 38

page 40


High-resolution population estimation using household survey data and building footprints

The national census is an essential data source to support decision-maki...

On the limits of algorithmic prediction across the globe

The impact of predictive algorithms on people's lives and livelihoods ha...

Household poverty classification in data-scarce environments: a machine learning approach

We describe a method to identify poor households in data-scarce countrie...

Micro-Estimates of Wealth for all Low- and Middle-Income Countries

Many critical policy decisions, from strategic investments to the alloca...

Rapid Response Crop Maps in Data Sparse Regions

Spatial information on cropland distribution, often called cropland or c...