Improving the accuracy of gridded population estimates in cities and slums to monitor SDG 11: Evidence from a simulation study in Namibia

Abstract

People living in slums and other deprived areas in low- and middle-income country (LMIC) cities are under-represented in censuses, and subsequently in "top-down" census-derived gridded population estimates. Modelled gridded population data are a unique source of disaggregated population information to calculate local development indicators such as the Sustainable Development Goals (SDGs). This study evaluates if, and how, “top-down” WorldPop Global (WPG) Unconstrained and Constrained datasets might be improved in a simulated LMIC urban population by incorporating slum population counts into model training. We found that the WPG-Unconstrained model, with or without slum training data, underestimated population in urban deprived areas while overestimating population in rural areas. The percent of population living in slums (SDG 11.1.1), for example, was estimated to be 20% or less compared to a "true" value of 29.5%. The WPG-Constrained model, which included building footprint auxiliary datasets, far more accurately estimated the population in all grid cells (including rural areas), and the inclusion of slum training data further improved estimates such that SDG 11.1.1 was estimated at 27.1% and 27.0%, respectively. Inclusion of building metrics and slum population training data in “top-down” gridded population models can substantially improve grid cell-level accuracy in both urban and rural areas.