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

Author

Listed:
  • Gianluca Boo

    (University of Southampton)

  • Edith Darin

    (University of Southampton)

  • Douglas R. Leasure

    (University of Southampton)

  • Claire A. Dooley

    (University of Southampton)

  • Heather R. Chamberlain

    (University of Southampton)

  • Attila N. Lázár

    (University of Southampton)

  • Kevin Tschirhart

    (Columbia University)

  • Cyrus Sinai

    (University of California at Los Angeles
    University of North Carolina at Chapel Hill)

  • Nicole A. Hoff

    (University of California at Los Angeles)

  • Trevon Fuller

    (University of California at Los Angeles)

  • Kamy Musene

    (University of California at Los Angeles)

  • Arly Batumbo

    (Bureau Central du Recensement, Institut National de la Statistique)

  • Anne W. Rimoin

    (University of California at Los Angeles)

  • Andrew J. Tatem

    (University of Southampton)

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 However, this data may become outdated during the intercensal period, which can stretch up to several decades.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of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.

Suggested Citation

  • Gianluca Boo & Edith Darin & Douglas R. Leasure & Claire A. Dooley & Heather R. Chamberlain & Attila N. Lázár & Kevin Tschirhart & Cyrus Sinai & Nicole A. Hoff & Trevon Fuller & Kamy Musene & Arly Bat, 2022. "High-resolution population estimation using household survey data and building footprints," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29094-x
    DOI: 10.1038/s41467-022-29094-x
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    References listed on IDEAS

    as
    1. Denwood, Matthew J., 2016. "runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i09).
    2. Marivoet, Wim & De Herdt, Tom, 2017. "From figures to facts: making sense of socio-economic surveys in the Democratic Republic of the Congo (DRC)," IOB Analyses & Policy Briefs 23, Universiteit Antwerpen, Institute of Development Policy (IOB).
    3. Douglas R. Leasure & Warren C. Jochem & Eric M. Weber & Vincent Seaman & Andrew J. Tatem, 2020. "National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(39), pages 24173-24179, September.
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    1. Benjamin Herfort & Sven Lautenbach & João Porto de Albuquerque & Jennings Anderson & Alexander Zipf, 2023. "A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Li, Fangyi & Li, Fei & Cai, Bofeng & Lyu, Chen & Xie, Wu, 2024. "Role of Chinese cities in abating aviation carbon emissions based on gridded population data and power law model," Energy, Elsevier, vol. 288(C).

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