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

National population mapping from sparse survey data: a hierarchical Bayesian modeling framework to account for uncertainty
National population mapping from sparse survey data: a hierarchical Bayesian modeling framework to account for uncertainty
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.
0027-8424
Leasure, Douglas
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Jochem, Warren
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Weber, Eric M.
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Seaman, Vincent Y.
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Tatem, Andrew
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Leasure, Douglas
c025de11-3c61-45b0-9b19-68d1d37959cd
Jochem, Warren
ef65df67-4364-4438-92e9-f93ceedb8da1
Weber, Eric M.
d4317485-6fe7-440d-baa5-0bd1bc23b982
Seaman, Vincent Y.
563edd26-911f-472e-81a4-0b94f475e7d7
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Leasure, Douglas, Jochem, Warren, Weber, Eric M., Seaman, Vincent Y. and Tatem, Andrew (2020) National population mapping from sparse survey data: a hierarchical Bayesian modeling framework to account for uncertainty. Proceedings of the National Academy of Sciences of the United States of America. (doi:10.1073/pnas.1913050117).

Record type: Article

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.

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Accepted/In Press date: 23 July 2020
Published date: 14 September 2020

Identifiers

Local EPrints ID: 444069
URI: http://eprints.soton.ac.uk/id/eprint/444069
ISSN: 0027-8424
PURE UUID: 895f09df-0a62-4622-84d3-0bd1f9b0f605
ORCID for Warren Jochem: ORCID iD orcid.org/0000-0003-2192-5988
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 24 Sep 2020 16:31
Last modified: 18 Feb 2021 17:26

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Contributors

Author: Douglas Leasure
Author: Warren Jochem ORCID iD
Author: Eric M. Weber
Author: Vincent Y. Seaman
Author: Andrew Tatem ORCID iD

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