How remotely sensed built areas and their realizations inform and constrain gridded population models
How remotely sensed built areas and their realizations inform and constrain gridded population models
Many disciplines require consistent, comparable areal information about where people are. Gridded population estimates fill this need typically by disaggregating census data or predicting population counts from ancillary information within identical pixel sizes across variable extents. Remotely-sensed built areas are an extremely important form of such ancillary data, and how they inform and constrain these techniques has yet to be fully explored. This study assesses the effectiveness of three remotely-sensed, built area data sets (High Resolution Settlement Layer, Global Human Settlement Layer and the World Settlement Footprint) in producing gridded population estimates. Three mapping techniques are applied with those three data sets in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. The modeling techniques include binary dasymetric redistribution, random forest with a dasymetric component, and a hybrid of the previous two. Results show that in five of the six countries the random forest and hybrid models were comparable, although there may be case-specific instances in which statistically complex models are not as robust as simply redistributing population counts into "built" grid cells. Relative contributions from different built area data to informing population modelling was discussed in relation to the spatial resolution, modeling techniques, and underlying built area vs. built-up definitions. Further investigation is also discussed regarding the inclusion of additional built datasets, ancillary inputs, and reference data.
built areas, Dasymetric modeling, Gridded Population, Random Forest, remote sensing, spatial downscaling
6364-6367
Stevens, Forrest R.
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Reed, Fennis
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Gaughan, Andrea E.
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Sinha, Parmanand
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Sorichetta, Alessandro
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Yetman, Gregory
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Tatem, Andrew J.
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28 July 2019
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Reed, Fennis
2730f5da-555e-4b1d-ae00-ae0695ae7148
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Sinha, Parmanand
b975ee23-d2a2-46c9-bbaf-c0becfd6640d
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Yetman, Gregory
502f5c7d-0576-4c88-99a4-e8c771844257
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Stevens, Forrest R., Reed, Fennis, Gaughan, Andrea E., Sinha, Parmanand, Sorichetta, Alessandro, Yetman, Gregory and Tatem, Andrew J.
(2019)
How remotely sensed built areas and their realizations inform and constrain gridded population models.
In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019.
IEEE.
.
(doi:10.1109/IGARSS.2019.8899339).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Many disciplines require consistent, comparable areal information about where people are. Gridded population estimates fill this need typically by disaggregating census data or predicting population counts from ancillary information within identical pixel sizes across variable extents. Remotely-sensed built areas are an extremely important form of such ancillary data, and how they inform and constrain these techniques has yet to be fully explored. This study assesses the effectiveness of three remotely-sensed, built area data sets (High Resolution Settlement Layer, Global Human Settlement Layer and the World Settlement Footprint) in producing gridded population estimates. Three mapping techniques are applied with those three data sets in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. The modeling techniques include binary dasymetric redistribution, random forest with a dasymetric component, and a hybrid of the previous two. Results show that in five of the six countries the random forest and hybrid models were comparable, although there may be case-specific instances in which statistically complex models are not as robust as simply redistributing population counts into "built" grid cells. Relative contributions from different built area data to informing population modelling was discussed in relation to the spatial resolution, modeling techniques, and underlying built area vs. built-up definitions. Further investigation is also discussed regarding the inclusion of additional built datasets, ancillary inputs, and reference data.
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Published date: 28 July 2019
Additional Information:
Publisher Copyright:
© 2019 IEEE.
Published version available from Currans Associates : ISBN 9781538691557
Venue - Dates:
39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, , Yokohama, Japan, 2019-07-28 - 2019-08-02
Keywords:
built areas, Dasymetric modeling, Gridded Population, Random Forest, remote sensing, spatial downscaling
Identifiers
Local EPrints ID: 456335
URI: http://eprints.soton.ac.uk/id/eprint/456335
PURE UUID: c8045cf3-8523-41c4-8821-0afac62f9d9b
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Date deposited: 27 Apr 2022 02:19
Last modified: 06 Jun 2024 01:50
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Contributors
Author:
Forrest R. Stevens
Author:
Fennis Reed
Author:
Andrea E. Gaughan
Author:
Parmanand Sinha
Author:
Gregory Yetman
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