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

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Faculty of Geo-Information Science & Earth Observation, University of Twente, 7514 AE Enschede, The Netherlands
2
Department of Social Statistics & Demography, University of Southampton, Southampton SO17 1BJ, UK
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Department of Geography & Geosciences, University of Louisville, Louisville, KY 40208, USA
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Center for International Earth Science Information Network (CIESIN), Columbia University, New York, NY 10964, USA
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Department of Geography, University of Lagos, Lagos 101017, Nigeria
*
Author to whom correspondence should be addressed.
Academic Editor: David Plane
Received: 16 May 2021 / Revised: 16 June 2021 / Accepted: 17 June 2021 / Published: 20 June 2021
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. View Full-Text
Keywords: SDG11; urban; deprivation; informal settlement; poverty; mapping SDG11; urban; deprivation; informal settlement; poverty; mapping
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MDPI and ACS Style

Thomson, D.R.; Gaughan, A.E.; Stevens, F.R.; Yetman, G.; Elias, P.; Chen, R. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. Urban Sci. 2021, 5, 48. https://0-doi-org.brum.beds.ac.uk/10.3390/urbansci5020048

AMA Style

Thomson DR, Gaughan AE, Stevens FR, Yetman G, Elias P, Chen R. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. Urban Science. 2021; 5(2):48. https://0-doi-org.brum.beds.ac.uk/10.3390/urbansci5020048

Chicago/Turabian Style

Thomson, Dana R., Andrea E. Gaughan, Forrest R. Stevens, Gregory Yetman, Peter Elias, and Robert Chen. 2021. "Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya" Urban Science 5, no. 2: 48. https://0-doi-org.brum.beds.ac.uk/10.3390/urbansci5020048

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