Estimating The True State Of Global Poverty With Machine Learning

A collaboration from UoC Berkeley, Stanford University and Facebook offers a deeper and more granular picture of the actual state of poverty in and across nations, through the use of machine learning. The research, entitled Micro-Estimates of Wealth for all Low-and Middle-Income Countries, is accompanied by a beta website that allows users to interactively explore the absolute and relative economic state of fine-grained areas and pockets of poverty in low and middle-income countries. The framework incorporates data from satellite imagery, topographic maps, mobile phone networks and aggregated anonymized data from Facebook, and is verified against extensive face-to-face surveys, for purposes of reporting relative wealth disparity in a region, rather than absolute estimates of income. A map of global poverty, weighted towards the most affected areas. The system has been adopted by the government of Nigeria as a basis for administering social protection programs, and runs in tandem with the existing framework from the World Bank, the National Social Safety nets Project (NASSP).