Geospatial micro-estimates of slum populations in 129 Global South countries using machine learning and public data

Reliable estimation of populations living in slums or slum-like conditions is crucial for urban planning, humanitarian resource allocation, and human well-being improvement. We generate the micro-estimate of slum population at a neighborhood level (~3.63 arc-minutes, preserving the privacy of vulnerable people) for 129 Global South countries in 2018. The estimates are built based on the Sustainable Development Goals 11.1 indicator framework and machine learning algorithms to heterogeneous data from household-based surveys and satellite images, as well as grided population data. Our integrated regional models show strong predictive capabilities for cluster-level slums proxy, explaining 82% to 96% of the variation in ground-truth surveys conducted in Global South countries, with root mean squared error ranging from 4.85% to 10.47%. The models perform match or surpass benchmarks established by previous studies. Cross-comparison with independent data sources at multi-scales suggest that our approach can yield reliable and consistent slum population estimates.