Mapping Poverty Through Data Integration And AI
In 2017, the Asian Development Bank (ADB) designed a knowledge initiative called Data for Development, which aims to strengthen the capacity of NSOs in the Asia and Pacific region to meet the increasing data demands for effective policymaking and for monitoring development goals and targets.
One component of the initiative focuses on subnational disaggregation of SDG indicators, particularly poverty statistics.
This component draws inspiration from studies that use high resolution satellite imagery, geospatial data, and powerful machine-learning algorithms to complement traditional data sources and conventional survey methods.
This approach can be used to estimate the magnitude of poverty in specific areas in the world, and the resulting data can aid governments and development organizations in distributing funds more efficiently as well as helping policymakers design more effective and targeted poverty reduction strategies.
Statisticians from ADB’s Statistics and Data Innovation Unit within the Economic Research and Regional Cooperation Department worked with the Philippine Statistics Authority, the National Statistical Office of Thailand, and the World Data Lab to examine the feasibility of poverty mapping using satellite imagery and associated geospatial data.
This supplement to Key Indicators for Asia and the Pacific 2020 documents the initial results of the feasibility study, which aimed to explore alternative data collection channels by combining traditional methods with innovative sources that might enhance the granularity, cost effectiveness, and compilation of high-quality poverty statistics.