Version 1
: Received: 21 July 2021 / Approved: 22 July 2021 / Online: 22 July 2021 (09:15:06 CEST)
How to cite:
Thomson, D.R.; Stevens, F.R.; Chen, R.; Yetman, G.; Sorichetta, A.; Gaughan, A.E. Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia. Preprints2021, 2021070510. https://doi.org/10.20944/preprints202107.0510.v1
Thomson, D.R.; Stevens, F.R.; Chen, R.; Yetman, G.; Sorichetta, A.; Gaughan, A.E. Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia. Preprints 2021, 2021070510. https://doi.org/10.20944/preprints202107.0510.v1
Thomson, D.R.; Stevens, F.R.; Chen, R.; Yetman, G.; Sorichetta, A.; Gaughan, A.E. Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia. Preprints2021, 2021070510. https://doi.org/10.20944/preprints202107.0510.v1
APA Style
Thomson, D.R., Stevens, F.R., Chen, R., Yetman, G., Sorichetta, A., & Gaughan, A.E. (2021). Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia. Preprints. https://doi.org/10.20944/preprints202107.0510.v1
Chicago/Turabian Style
Thomson, D.R., Alessandro Sorichetta and Andrea E. Gaughan. 2021 "Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia" Preprints. https://doi.org/10.20944/preprints202107.0510.v1
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" 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, WorldPop-Global (WPG) -Unconstrained and -Constrained “top-down” datasets might be improved in a simulated realistic LMIC urban population by incorporating slum profile population counts into model training. We found that the WPG-Unconstrained model with or without slum training data grossly underestimated population in urban deprived areas while grossly overestimating population in rural areas. SDG 11.1.1, the percent of population living in slums, for example, was estimated to be 20% or less compared to a "true" value of 29.5%. The WPG-Constrained model, which included building 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 training data in “top-down” gridded population models can substantially improve grid cell-level accuracy in both urban and rural areas.
Keywords
LMIC; urban; deprivation; informal settlement; poverty; Global South
Subject
Business, Economics and Management, Accounting and Taxation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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