High-resolution population estimation using household survey data and building footprints.

Autor: Boo G; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK. gianluca.boo@gmail.com., Darin E; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Leasure DR; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Dooley CA; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Chamberlain HR; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Lázár AN; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Tschirhart K; Center for International Earth Science Information Network (CIESIN), Columbia University, New York, NY, USA., Sinai C; UCLA Fielding School of Public Health, University of California at Los Angeles, Los Angeles, CA, USA.; Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Hoff NA; UCLA Fielding School of Public Health, University of California at Los Angeles, Los Angeles, CA, USA., Fuller T; UCLA Fielding School of Public Health, University of California at Los Angeles, Los Angeles, CA, USA., Musene K; UCLA Fielding School of Public Health, University of California at Los Angeles, Los Angeles, CA, USA., Batumbo A; Bureau Central du Recensement, Institut National de la Statistique, Kinshasa, Democratic Republic of the Congo., Rimoin AW; UCLA Fielding School of Public Health, University of California at Los Angeles, Los Angeles, CA, USA., Tatem AJ; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Mar 14; Vol. 13 (1), pp. 1330. Date of Electronic Publication: 2022 Mar 14.
DOI: 10.1038/s41467-022-29094-x
Abstrakt: The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R 2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.
(© 2022. The Author(s).)
Databáze: MEDLINE
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