National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty

Resource type
Journal Article
Authors/contributors
Title
National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty
Abstract
Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.
Publication
Proceedings of the National Academy of Sciences
Volume
117
Issue
39
Pages
24173-24179
Date
2020/09/29
Journal Abbr
PNAS
Language
en
ISSN
0027-8424, 1091-6490
Short Title
National population mapping from sparse survey data
Accessed
18/03/2021, 19:02
Library Catalogue
Rights
Copyright © 2020 the Author(s). Published by PNAS.. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
Extra
Publisher: National Academy of Sciences Section: Social Sciences PMID: 32929009
Citation
Leasure, D. R., Jochem, W. C., Weber, E. M., Seaman, V., & Tatem, A. J. (2020). National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty. Proceedings of the National Academy of Sciences, 117(39), 24173–24179. https://doi.org/10.1073/pnas.1913050117