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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Urban Science |
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| Online Access: | https://www.mdpi.com/2413-8851/5/2/48 |
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| author | Dana R. Thomson Andrea E. Gaughan Forrest R. Stevens Gregory Yetman Peter Elias Robert Chen |
| author_facet | Dana R. Thomson Andrea E. Gaughan Forrest R. Stevens Gregory Yetman Peter Elias Robert Chen |
| author_sort | Dana R. Thomson |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:14:42Z |
| format | Article |
| id | doaj.art-71b3e6acdfa34409890bbc9fc4c860f6 |
| institution | Directory Open Access Journal |
| issn | 2413-8851 |
| language | English |
| last_indexed | 2024-03-10T10:14:42Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Urban Science |
| spelling | doaj.art-71b3e6acdfa34409890bbc9fc4c860f62023-11-22T00:54:07ZengMDPI AGUrban Science2413-88512021-06-01524810.3390/urbansci5020048Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and KenyaDana R. Thomson0Andrea E. Gaughan1Forrest R. Stevens2Gregory Yetman3Peter Elias4Robert Chen5Faculty of Geo-Information Science & Earth Observation, University of Twente, 7514 AE Enschede, The NetherlandsDepartment of Geography & Geosciences, University of Louisville, Louisville, KY 40208, USADepartment of Geography & Geosciences, University of Louisville, Louisville, KY 40208, USACenter for International Earth Science Information Network (CIESIN), Columbia University, New York, NY 10964, USADepartment of Geography, University of Lagos, Lagos 101017, NigeriaCenter for International Earth Science Information Network (CIESIN), Columbia University, New York, NY 10964, USALow- 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.https://www.mdpi.com/2413-8851/5/2/48SDG11urbandeprivationinformal settlementpovertymapping |
| spellingShingle | Dana R. Thomson Andrea E. Gaughan Forrest R. Stevens Gregory Yetman Peter Elias Robert Chen Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya Urban Science SDG11 urban deprivation informal settlement poverty mapping |
| title | Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya |
| title_full | Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya |
| title_fullStr | Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya |
| title_full_unstemmed | Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya |
| title_short | Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya |
| title_sort | evaluating the accuracy of gridded population estimates in slums a case study in nigeria and kenya |
| topic | SDG11 urban deprivation informal settlement poverty mapping |
| url | https://www.mdpi.com/2413-8851/5/2/48 |
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