The University of Southampton
University of Southampton Institutional Repository

Census-independent population mapping in northern Nigeria

Census-independent population mapping in northern Nigeria
Census-independent population mapping in northern Nigeria

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.

Demographics, Nigeria, Polio, Population, Settlement mapping
0034-4257
786-798
Weber, Eric M.
d4317485-6fe7-440d-baa5-0bd1bc23b982
Seaman, Vincent Y.
563edd26-911f-472e-81a4-0b94f475e7d7
Stewart, Robert N.
8f288833-e1ff-4571-8148-be1d04c462c0
Bird, Tomas J.
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
McKee, Jacob J.
c015e6a3-5312-4e41-adbe-d82d18186585
Bhaduri, Budhendra L.
4319dc26-c61c-4a48-b796-255100a73bb4
Moehl, Jessica J.
c7fd6239-3a9d-4a49-8d3c-124bb2a9189b
Reith, Andrew E.
7eebcd81-f9a4-4947-ae9a-a94d9e830308
Weber, Eric M.
d4317485-6fe7-440d-baa5-0bd1bc23b982
Seaman, Vincent Y.
563edd26-911f-472e-81a4-0b94f475e7d7
Stewart, Robert N.
8f288833-e1ff-4571-8148-be1d04c462c0
Bird, Tomas J.
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
McKee, Jacob J.
c015e6a3-5312-4e41-adbe-d82d18186585
Bhaduri, Budhendra L.
4319dc26-c61c-4a48-b796-255100a73bb4
Moehl, Jessica J.
c7fd6239-3a9d-4a49-8d3c-124bb2a9189b
Reith, Andrew E.
7eebcd81-f9a4-4947-ae9a-a94d9e830308

Weber, Eric M., Seaman, Vincent Y., Stewart, Robert N., Bird, Tomas J., Tatem, Andrew J., McKee, Jacob J., Bhaduri, Budhendra L., Moehl, Jessica J. and Reith, Andrew E. (2018) Census-independent population mapping in northern Nigeria. Remote Sensing of Environment, 204, 786-798. (doi:10.1016/j.rse.2017.09.024).

Record type: Article

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.

Text
1-s2.0-S0034425717304364-main - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)

More information

Accepted/In Press date: 16 September 2017
e-pub ahead of print date: 21 October 2017
Published date: 1 January 2018
Keywords: Demographics, Nigeria, Polio, Population, Settlement mapping

Identifiers

Local EPrints ID: 417414
URI: https://eprints.soton.ac.uk/id/eprint/417414
ISSN: 0034-4257
PURE UUID: 141c8c3f-22a4-4827-8916-0cbebce62841
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 31 Jan 2018 17:30
Last modified: 14 Mar 2019 01:35

Export record

Altmetrics

Contributors

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

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×