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Intercensal updating using structure‐preserving methods and satellite imagery

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  • Till Koebe
  • Alejandra Arias‐Salazar
  • Natalia Rojas‐Perilla
  • Timo Schmid

Abstract

Censuses are fundamental building blocks of most modern‐day societies, yet collected every 10 years at best. We propose an extension of the widely popular census updating technique structure‐preserving estimation by incorporating auxiliary information in order to take ongoing subnational population shifts into account. We apply our method by incorporating satellite imagery as additional source to derive annual small‐area updates of multidimensional poverty indicators from 2013 to 2020 for a population at risk: female‐headed households in Senegal. We evaluate the performance of our proposal using data from two different census periods.

Suggested Citation

  • Till Koebe & Alejandra Arias‐Salazar & Natalia Rojas‐Perilla & Timo Schmid, 2022. "Intercensal updating using structure‐preserving methods and satellite imagery," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 170-196, December.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:s2:p:s170-s196
    DOI: 10.1111/rssa.12802
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    References listed on IDEAS

    as
    1. Angela Luna & Li-Chun Zhang & Alison Whitworth & Kirsten Piller, 2015. "Small Area Estimates Of The Population Distribution By Ethnic Group In England: A Proposal Using Structure Preserving Estimators," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 585-602, December.
    2. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
    3. repec:bla:jorssa:v:180:y:2017:i:4:p:1163-1190 is not listed on IDEAS
    4. Till Koebe, 2020. "Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-28, November.
    5. Maxim Pinkovskiy & Xavier Sala-i-Martin, 2016. "Lights, Camera … Income! Illuminating the National Accounts-Household Surveys Debate," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 579-631.
    6. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    7. 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.
    8. Alison Whitworth & Kirsten Piller & Angela Luna & Li-Chun Zhang, 2015. "Small area estimates of the population distribution by ethnic group in England: a proposal using structure preserving estimators," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 585-602, December.
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