Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data
Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data
High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America
e0107042
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Amaral, Luis A. Nunes
3fadff33-2b75-4e43-b1e8-d8673e6ad61a
17 February 2015
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Amaral, Luis A. Nunes
3fadff33-2b75-4e43-b1e8-d8673e6ad61a
Stevens, Forrest R., Gaughan, Andrea E., Linard, Catherine and Tatem, Andrew J.
,
Amaral, Luis A. Nunes
(ed.)
(2015)
Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data.
PLoS ONE, 10 (2), .
(doi:10.1371/journal.pone.0107042).
(PMID:25689585)
Abstract
High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America
Other
fetchObject.action_uri=info_doi%2F10.1371%2Fjournal.pone.0107042&representation=PDF
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More information
Accepted/In Press date: 11 August 2014
Published date: 17 February 2015
Organisations:
Global Env Change & Earth Observation, WorldPop, Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 374549
URI: http://eprints.soton.ac.uk/id/eprint/374549
ISSN: 1932-6203
PURE UUID: 66e7df84-2597-4655-aff8-50c554aded62
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Date deposited: 20 Feb 2015 11:52
Last modified: 15 Mar 2024 03:43
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Contributors
Author:
Forrest R. Stevens
Author:
Andrea E. Gaughan
Author:
Catherine Linard
Editor:
Luis A. Nunes Amaral
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