High resolution population maps for low income nations: combining land cover and census in East Africa
High resolution population maps for low income nations: combining land cover and census in East Africa
BACKGROUND: Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas. METHODOLOGY/PRINCIPAL FINDINGS: We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps. CONCLUSIONS: We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km(2). The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available.
africa, eastern, demography, developing countries, humans
e1298-[8pp]
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Noor, Abdisalan M.
06d32991-29fe-47a5-a62b-fe584c753414
von Hagen, Craig
7e830b9d-294c-4245-8989-b64dcac5743c
Di Gregorio, Antonio
a95c9222-9038-4d25-99df-861e9e2aca2e
Hay, Simon I.
471d3ae4-a3c1-4d29-93e3-a90d44471b00
December 2007
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Noor, Abdisalan M.
06d32991-29fe-47a5-a62b-fe584c753414
von Hagen, Craig
7e830b9d-294c-4245-8989-b64dcac5743c
Di Gregorio, Antonio
a95c9222-9038-4d25-99df-861e9e2aca2e
Hay, Simon I.
471d3ae4-a3c1-4d29-93e3-a90d44471b00
Tatem, Andrew J., Noor, Abdisalan M., von Hagen, Craig, Di Gregorio, Antonio and Hay, Simon I.
(2007)
High resolution population maps for low income nations: combining land cover and census in East Africa.
PLoS ONE, 2 (12), .
(doi:10.1371/journal.pone.0001298).
(PMID:18074022)
Abstract
BACKGROUND: Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas. METHODOLOGY/PRINCIPAL FINDINGS: We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps. CONCLUSIONS: We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km(2). The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available.
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Published date: December 2007
Keywords:
africa, eastern, demography, developing countries, humans
Organisations:
Geography & Environment, PHEW – S (Spatial analysis and modelling), Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 344477
URI: http://eprints.soton.ac.uk/id/eprint/344477
ISSN: 1932-6203
PURE UUID: a9e0d625-d6ae-4bb8-a318-d21aab8b530f
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Date deposited: 25 Oct 2012 08:39
Last modified: 15 Mar 2024 03:43
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Contributors
Author:
Abdisalan M. Noor
Author:
Craig von Hagen
Author:
Antonio Di Gregorio
Author:
Simon I. Hay
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