Small area population estimates using random forest top-down disaggregation: An R tutorial
Small area population estimates using random forest top-down disaggregation: An R tutorial
This R tutorial demonstrates how to implement random forest top-down disaggregation to estimate populations at finer resolutions using population totals from larger administrative units in combination with higher resolution geospatial covariates.
Human population, sub-national, R tutorial, machine learning, random forest
University of Southampton
Leasure, Douglas
c025de11-3c61-45b0-9b19-68d1d37959cd
Darin, Edith
868fa688-2567-4dbd-aa12-3dcc91f2aa8d
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Leasure, Douglas
c025de11-3c61-45b0-9b19-68d1d37959cd
Darin, Edith
868fa688-2567-4dbd-aa12-3dcc91f2aa8d
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Leasure, Douglas, Darin, Edith and Tatem, Andrew
(2021)
Small area population estimates using random forest top-down disaggregation: An R tutorial.
University of Southampton
doi:10.5258/SOTON/WP00697
[Dataset]
Abstract
This R tutorial demonstrates how to implement random forest top-down disaggregation to estimate populations at finer resolutions using population totals from larger administrative units in combination with higher resolution geospatial covariates.
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Published date: 20 January 2021
Keywords:
Human population, sub-national, R tutorial, machine learning, random forest
Identifiers
Local EPrints ID: 446189
URI: http://eprints.soton.ac.uk/id/eprint/446189
PURE UUID: d99650cf-ccff-40d7-816c-e5005b4a8b3c
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Date deposited: 27 Jan 2021 17:30
Last modified: 06 May 2023 02:01
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Creator:
Douglas Leasure
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