Boo G, Darin E, Leasure DR, Dooley CA, Chamberlain HR, Lázár AN, Tschirhart K, Sinai C, Hoff NA, Fuller T, Musene K, Batumbo A, Rimoin AW, Tatem AJ. High-resolution population estimation using household survey data and building footprints.
Nat Commun 2022;
13:1330. [PMID:
35288578 PMCID:
PMC8921279 DOI:
10.1038/s41467-022-29094-x]
[Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 02/23/2022] [Indexed: 11/19/2022] Open
Abstract
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 However, this data may become outdated during the intercensal period, which can stretch up to several decades.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 R2 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of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.
A lack of up-to-date population figures may hamper effective decision-making. Here, the authors develop a Bayesian model to estimate population data at high resolution in five provinces of the Democratic Republic of the Congo.
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