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Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South
Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South
Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods. These advances in urban feature extraction and built-area detection can refine the mapping of human population densities, especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data. However, in these contexts it is unclear how best to use built-area data to disaggregate areal, count-based census data. Here we tested two methods using remotely sensed, built-area land cover data to disaggregate population data. These included simple, areal weighting and more complex statistical models with other ancillary information. Outcomes were assessed across eleven countries, representing different world regions varying in population densities, types of built infrastructure, and environmental characteristics. We found that for seven of 11 countries a Random Forest-based, machine learning approach outperforms simple, binary dasymetric disaggregation into remotely-sensed built areas. For these more complex models there was little evidence to support using any single built land cover input over the rest, and in most cases using more than one built-area data product resulted in higher predictive capacity. We discuss these results and implications for future population modeling approaches.
Land cover, built areas, population modeling, remote sensing, settlement mapping
1753-8947
78-100
Forrest, Stephen R.
61e0a6cc-43b1-4a67-bbb9-3af3225531d5
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Nieves, Jeremiah J.
2b5f2f25-afc0-4585-8531-dc2acc4b3511
King, Adam
f8f21b15-59fc-4585-8e30-52639e82fb83
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Forrest, Stephen R.
61e0a6cc-43b1-4a67-bbb9-3af3225531d5
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Nieves, Jeremiah J.
2b5f2f25-afc0-4585-8531-dc2acc4b3511
King, Adam
f8f21b15-59fc-4585-8e30-52639e82fb83
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Forrest, Stephen R., Gaughan, Andrea E., Nieves, Jeremiah J., King, Adam, Sorichetta, Alessandro, Linard, Catherine and Tatem, Andrew (2020) Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South. International Journal of Digital Earth, 13 (1), 78-100. (doi:10.1080/17538947.2019.1633424).

Record type: Article

Abstract

Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods. These advances in urban feature extraction and built-area detection can refine the mapping of human population densities, especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data. However, in these contexts it is unclear how best to use built-area data to disaggregate areal, count-based census data. Here we tested two methods using remotely sensed, built-area land cover data to disaggregate population data. These included simple, areal weighting and more complex statistical models with other ancillary information. Outcomes were assessed across eleven countries, representing different world regions varying in population densities, types of built infrastructure, and environmental characteristics. We found that for seven of 11 countries a Random Forest-based, machine learning approach outperforms simple, binary dasymetric disaggregation into remotely-sensed built areas. For these more complex models there was little evidence to support using any single built land cover input over the rest, and in most cases using more than one built-area data product resulted in higher predictive capacity. We discuss these results and implications for future population modeling approaches.

Full text not available from this repository.

More information

Accepted/In Press date: 13 June 2019
e-pub ahead of print date: 23 September 2019
Published date: January 2020
Keywords: Land cover, built areas, population modeling, remote sensing, settlement mapping

Identifiers

Local EPrints ID: 436838
URI: http://eprints.soton.ac.uk/id/eprint/436838
ISSN: 1753-8947
PURE UUID: 1e3fe93c-3ec7-4a94-8dd9-9a28ff5be6dd
ORCID for Alessandro Sorichetta: ORCID iD orcid.org/0000-0002-3576-5826
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 10 Jan 2020 17:34
Last modified: 08 Oct 2020 16:30

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