Gridded disaggregated population estimates for Kenya (2021), version 1.0.
Gridded disaggregated population estimates for Kenya (2021), version 1.0.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund (UNICEF) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work, as well as Christopher Lloyd, particularly for the processing of residential/non-residential building footprints. In-country engagements were done by Benard Mitto, Justine Dowden (CIESIN) and Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and sex and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem.
population, disaggregated, Kenya
University of Southampton
Gadiaga, Assane
eada3464-b0a2-4aaa-b594-eff8182c2aee
Abbott, Thomas
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Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Lloyd, Christopher
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Lazar, Attila
d7f835e7-1e3d-4742-b366-af19cf5fc881
Darin, Edith
868fa688-2567-4dbd-aa12-3dcc91f2aa8d
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Gadiaga, Assane
eada3464-b0a2-4aaa-b594-eff8182c2aee
Abbott, Thomas
8b1d4865-a634-4b8a-a39a-ff8842709edd
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Lloyd, Christopher
de6d850d-fba9-4f7e-9340-8ba750bfd9a6
Lazar, Attila
d7f835e7-1e3d-4742-b366-af19cf5fc881
Darin, Edith
868fa688-2567-4dbd-aa12-3dcc91f2aa8d
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Gadiaga, Assane, Abbott, Thomas, Chamberlain, Heather, Lloyd, Christopher, Lazar, Attila, Darin, Edith and Tatem, Andrew
(2022)
Gridded disaggregated population estimates for Kenya (2021), version 1.0.
University of Southampton
doi:10.5258/SOTON/WP00747
[Dataset]
Abstract
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund (UNICEF) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work, as well as Christopher Lloyd, particularly for the processing of residential/non-residential building footprints. In-country engagements were done by Benard Mitto, Justine Dowden (CIESIN) and Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and sex and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem.
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Published date: 22 July 2022
Keywords:
population, disaggregated, Kenya
Identifiers
Local EPrints ID: 472920
URI: http://eprints.soton.ac.uk/id/eprint/472920
PURE UUID: 5deaa515-d783-458e-809b-ede88a509ec0
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Date deposited: 06 Jan 2023 11:59
Last modified: 06 May 2023 02:01
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Contributors
Creator:
Assane Gadiaga
Creator:
Thomas Abbott
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