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Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data

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  • Forrest R Stevens
  • Andrea E Gaughan
  • Catherine Linard
  • Andrew J Tatem

Abstract

High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.

Suggested Citation

  • Forrest R Stevens & Andrea E Gaughan & Catherine Linard & Andrew J Tatem, 2015. "Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-22, February.
  • Handle: RePEc:plo:pone00:0107042
    DOI: 10.1371/journal.pone.0107042
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    References listed on IDEAS

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    1. Catherine Linard & Marius Gilbert & Robert W Snow & Abdisalan M Noor & Andrew J Tatem, 2012. "Population Distribution, Settlement Patterns and Accessibility across Africa in 2010," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
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    Cited by:

    1. Gaëlle Nicolas & Timothy P Robinson & G R William Wint & Giulia Conchedda & Giuseppina Cinardi & Marius Gilbert, 2016. "Using Random Forest to Improve the Downscaling of Global Livestock Census Data," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-16, March.
    2. Olivia D'Aoust & Julius Gunneman & Karishma V. Patel & Caroline Tassot, 2022. "Cash in the City," World Bank Publications - Reports 37443, The World Bank Group.

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