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Worldpop - Fusion of earth and big data for intraurban population mapping

Worldpop - Fusion of earth and big data for intraurban population mapping
Worldpop - Fusion of earth and big data for intraurban population mapping

High resolution estimates of human population distributions are very useful for large-scale or national scale analyses in many fields including epidemiology, healthcare, resource distribution, and development. Population densities have long been estimated using remote sensing data, particularly at large spatial scales. However, the accuracy of population density predictions can be very poor in cities, and this is particularly relevant in urban areas in sub-Saharan Africa. Here we map intra-urban population densities for select African cities by disaggregating census data using random forest techniques with remotely-sensed and geospatial data, including bespoke time-series intra-urban built-up data. We produce maps with up to 83% explained variance and find including built-up density layers in urban population models allows for clear improvements in prediction.

Africa, Built-up, Census, Machine learning, Population density, Urban areas
2070-2071
Institute of Electrical and Electronics Engineers Inc.
Steele, Jessica E.
5cbba8c8-f3fd-41ee-82c8-0aa13c04c04d
Nieves, Jeremiah
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Forget, Yann
442df24a-c8c8-43a2-9adc-25c5d35306be
Shimoni, Michal
69a2fb37-d296-43a7-8009-767745f4976b
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Steele, Jessica E.
5cbba8c8-f3fd-41ee-82c8-0aa13c04c04d
Nieves, Jeremiah
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Forget, Yann
442df24a-c8c8-43a2-9adc-25c5d35306be
Shimoni, Michal
69a2fb37-d296-43a7-8009-767745f4976b
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856

Steele, Jessica E., Nieves, Jeremiah, Tatem, Andrew J., Forget, Yann, Shimoni, Michal and Linard, Catherine (2018) Worldpop - Fusion of earth and big data for intraurban population mapping. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. vol. 2018-July, Institute of Electrical and Electronics Engineers Inc. pp. 2070-2071 . (doi:10.1109/IGARSS.2018.8518181).

Record type: Conference or Workshop Item (Paper)

Abstract

High resolution estimates of human population distributions are very useful for large-scale or national scale analyses in many fields including epidemiology, healthcare, resource distribution, and development. Population densities have long been estimated using remote sensing data, particularly at large spatial scales. However, the accuracy of population density predictions can be very poor in cities, and this is particularly relevant in urban areas in sub-Saharan Africa. Here we map intra-urban population densities for select African cities by disaggregating census data using random forest techniques with remotely-sensed and geospatial data, including bespoke time-series intra-urban built-up data. We produce maps with up to 83% explained variance and find including built-up density layers in urban population models allows for clear improvements in prediction.

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More information

Published date: 31 October 2018
Venue - Dates: 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 2018-07-22 - 2018-07-27
Keywords: Africa, Built-up, Census, Machine learning, Population density, Urban areas

Identifiers

Local EPrints ID: 429917
URI: https://eprints.soton.ac.uk/id/eprint/429917
PURE UUID: 84a3b4fa-9be3-48c5-8d1d-dc663aebb739
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 09 Apr 2019 16:30
Last modified: 20 Jul 2019 00:41

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Contributors

Author: Jeremiah Nieves
Author: Andrew J. Tatem ORCID iD
Author: Yann Forget
Author: Michal Shimoni
Author: Catherine Linard

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