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Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets
Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets
Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.
Human population, sub-national, global, spatial dataset, multi-temporal
108-139
Lloyd, Christopher T.
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Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Kerr, David
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Yetman, Greg
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Pistolesi, Linda
d283e435-98eb-41e4-957a-560fb78eadad
Stevens, Forrest R.
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Gaughan, Andrew E.
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Nieves, Jeremiah, Joseph
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Hornby, Graeme
52fc0227-a0b1-46eb-a08f-ec689c460bf8
MacManus, Kytt
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Sinha, Parmanand
b975ee23-d2a2-46c9-bbaf-c0becfd6640d
Bondarenko, Maksym
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Sorichetta, Alessandro
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Tatem, Andrew
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Lloyd, Christopher T.
de6d850d-fba9-4f7e-9340-8ba750bfd9a6
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Kerr, David
0cb7f738-0bf6-463f-b1f1-b380c6b568b8
Yetman, Greg
149630df-4250-4fc4-a4bf-72a7e60da962
Pistolesi, Linda
d283e435-98eb-41e4-957a-560fb78eadad
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Gaughan, Andrew E.
accb5fdc-8780-4f5b-b7cb-62ffb977c63d
Nieves, Jeremiah, Joseph
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Hornby, Graeme
52fc0227-a0b1-46eb-a08f-ec689c460bf8
MacManus, Kytt
e7fcafad-9e54-4dc4-a8a4-009cb48cfbf6
Sinha, Parmanand
b975ee23-d2a2-46c9-bbaf-c0becfd6640d
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Lloyd, Christopher T., Chamberlain, Heather, Kerr, David, Yetman, Greg, Pistolesi, Linda, Stevens, Forrest R., Gaughan, Andrew E., Nieves, Jeremiah, Joseph, Hornby, Graeme, MacManus, Kytt, Sinha, Parmanand, Bondarenko, Maksym, Sorichetta, Alessandro and Tatem, Andrew (2019) Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data, 3 (2), 108-139. (doi:10.1080/20964471.2019.1625151).

Record type: Article

Abstract

Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.

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Submitted date: 15 May 2019
Accepted/In Press date: 25 May 2019
Published date: 18 June 2019
Keywords: Human population, sub-national, global, spatial dataset, multi-temporal

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Local EPrints ID: 433013
URI: http://eprints.soton.ac.uk/id/eprint/433013
PURE UUID: 36542485-947f-488c-98fe-fb2c64579d85
ORCID for Christopher T. Lloyd: ORCID iD orcid.org/0000-0001-7435-8230
ORCID for Graeme Hornby: ORCID iD orcid.org/0000-0002-2833-8711
ORCID for Alessandro Sorichetta: ORCID iD orcid.org/0000-0002-3576-5826
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 06 Aug 2019 16:30
Last modified: 14 May 2020 00:47

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Contributors

Author: David Kerr
Author: Greg Yetman
Author: Linda Pistolesi
Author: Forrest R. Stevens
Author: Andrew E. Gaughan
Author: Graeme Hornby ORCID iD
Author: Kytt MacManus
Author: Parmanand Sinha
Author: Andrew Tatem ORCID iD

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