Census-independent population mapping in northern Nigeria

Eric M. Weber, Vincent Y. Seaman, Robert N. Stewart, Tomas J. Bird, Andrew J. Tatem, Jacob J. McKee, Budhendra L. Bhaduri, Jessica J. Moehl, Andrew E. Reith

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

Abstract

Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census counts serving both as observational data for specifying models and as constraints on model outputs. Here we present a framework for estimating populations from the bottom up, entirely independently of national census data, a critical need in areas without recent and reliable census data. To make observations of population density, we replace national census data with a microcensus, in which we enumerate population for a sample of small areas within the states of Kano and Kaduna in northern Nigeria. Using supervised texture-based classifiers with very high resolution satellite imagery, we produce a binary map of human settlement at 8-meter resolution across the two states and then a more refined classification consisting of 7 residential types and 1 non-residential type. Using the residential types and a model linking them to the population density observations, we produce population estimates across the two states in a gridded raster format, at approximately 90-meter resolution. We also demonstrate a simulation framework for capturing uncertainty and presenting estimates as prediction intervals for any region of interest of any size and composition within the study region. Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference data collected during polio vaccination campaigns.

Original languageEnglish
Pages (from-to)786-798
Number of pages13
JournalRemote Sensing of Environment
Volume204
DOIs
StatePublished - Jan 2018

Funding

Copyright notice: This manuscript has been co-authored by one or more employees of UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This material is based upon work supported by the US Department of Energy, Office of Science , under contract number DE-AC05-00OR22725 . Authors at ORNL are supported by the Bill & Melinda Gates Foundation ( 11385 ). A.J.T. and T.J.B. are supported by the Bill & Melinda Gates Foundation ( OPP1106427 , 1032350 , OPP1134076 , OPP1094793 ), the Clinton Health Access Initiative as well as a Wellcome Trust Sustaining Health Grant ( 106866/Z/15/Z ). The authors would like to thank DigitalGlobe for donating high-resolution satellite imagery for the study region. The authors also appreciate the cooperation of Dr. M. Z. Mahmud of Nigeria's National Primary Healthcare Development Agency in ensuring government support for the microcensus work. The authors also thank the numerous colleagues involved in the preparation of the tools and datasets used in this work and are especially grateful to the contributions of Dami Sonoiki, Frank Salet, and Nikhil Patel at eHealth Africa; Dilip Patlolla, Jiangye Yuan, Jeanette Weaver, Brian Moore and Melanie Laverdiere at ORNL; and Victor Alegana at University of Southampton.

FundersFunder number
Clinton Health Access Initiative
Wellcome Trust Sustaining Health106866/Z/15/Z
U.S. Department of Energy
Bill and Melinda Gates Foundation1032350, OPP1106427, OPP1094793, 11385, OPP1134076
Office of ScienceDE-AC05-00OR22725

    Keywords

    • Demographics
    • Nigeria
    • Polio
    • Population
    • Settlement mapping

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