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 and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population...

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Bibliographic Details
Published inUrban science Vol. 5; no. 2; p. 48
Main Authors Thomson, Dana R., Gaughan, Andrea E., Stevens, Forrest R., Yetman, Gregory, Elias, Peter, Chen, Robert
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2021
Subjects
Accuracy
Bias
Cities
Datasets
deprivation
Deprived areas
Ghettos
informal settlement
Local government
mapping
Optimism
Population growth
poverty
Rural areas
SDG11
Slums
urban
Urban areas
Urbanization
Kenya
Nigeria
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Summary:Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.
ISSN:2413-8851
2413-8851
DOI:10.3390/urbansci5020048