Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya

Low- and middle-income country cities face unprecedented urbanization growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic spatial are a promising source of estimates, but limitations slums due to the dynamic nature this as well modelling assumptions. In study, we compared field-referenced boundaries counts Slum Dwellers International Lagos (Nigeria), Port Harcourt Nairobi (Kenya) with nine gridded datasets assess their statistical accuracy We found that all estimates vastly underestimated (RMSE: 4958 14,422, Bias: ?2853 ?7638), most accurate dataset (HRSL) estimating just 39 per cent slum residents. Using modelled map compare terms SDG 11.1.1 (percent living deprived areas), estimated indicator at 1–3 56 using UN-Habitat’s approach. outline steps might improve each urban areas. While not yet sufficiently estimate 11.1.1, optimistic some could be used future following updates approaches.