The University of Southampton
University of Southampton Institutional Repository

A high resolution spatial population database of Somalia for disease risk mapping

A high resolution spatial population database of Somalia for disease risk mapping
A high resolution spatial population database of Somalia for disease risk mapping
BACKGROUND: Millions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data.

RESULTS: Here land cover information derived from satellite imagery and existing settlement point datasets were used for the spatial reallocation of populations within census units. We used simple and semi-automated methods that can be implemented with free image processing software to produce an easily updatable gridded population dataset at 100 x 100 meters spatial resolution. The 2010 population dataset was matched to administrative population totals projected by the UN. Comparison tests between the new dataset and existing population datasets revealed important differences in population size distributions, and in population at risk of malaria estimates. These differences are particularly important in more densely populated areas and strongly depend on the settlement data used in the modelling approach.

CONCLUSIONS: The results show that it is possible to produce detailed, contemporary and easily updatable settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset produced is freely available as a product of the AfriPop Project and can be downloaded from: http://www.afripop.org.
communicable diseases, databases, factual, delivery of health care, geography, humans, population density, risk assessment methods, somalia epidemiology
1476-072X
1-13
Linard, C.
40dc396f-bbf0-4ae2-8732-7a73447a9100
Alegana, V.A.
6fdaa47e-c08c-48bc-b881-1dc7b89085e4
Noor, A.M.
241236c3-43df-47b0-bcab-ff7c25318cc6
Snow, R.W.
1df934dd-70f4-4bf1-8a98-7feb0207d796
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Linard, C.
40dc396f-bbf0-4ae2-8732-7a73447a9100
Alegana, V.A.
6fdaa47e-c08c-48bc-b881-1dc7b89085e4
Noor, A.M.
241236c3-43df-47b0-bcab-ff7c25318cc6
Snow, R.W.
1df934dd-70f4-4bf1-8a98-7feb0207d796
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Linard, C., Alegana, V.A., Noor, A.M., Snow, R.W. and Tatem, A.J. (2010) A high resolution spatial population database of Somalia for disease risk mapping. International Journal of Health Geographics, 9 (45), 1-13. (doi:10.1186/1476-072X-9-45).

Record type: Article

Abstract

BACKGROUND: Millions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data.

RESULTS: Here land cover information derived from satellite imagery and existing settlement point datasets were used for the spatial reallocation of populations within census units. We used simple and semi-automated methods that can be implemented with free image processing software to produce an easily updatable gridded population dataset at 100 x 100 meters spatial resolution. The 2010 population dataset was matched to administrative population totals projected by the UN. Comparison tests between the new dataset and existing population datasets revealed important differences in population size distributions, and in population at risk of malaria estimates. These differences are particularly important in more densely populated areas and strongly depend on the settlement data used in the modelling approach.

CONCLUSIONS: The results show that it is possible to produce detailed, contemporary and easily updatable settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset produced is freely available as a product of the AfriPop Project and can be downloaded from: http://www.afripop.org.

This record has no associated files available for download.

More information

Published date: 14 September 2010
Keywords: communicable diseases, databases, factual, delivery of health care, geography, humans, population density, risk assessment methods, somalia epidemiology
Organisations: Geography & Environment, PHEW – P (Population Health)

Identifiers

Local EPrints ID: 344430
URI: http://eprints.soton.ac.uk/id/eprint/344430
ISSN: 1476-072X
PURE UUID: aef926ff-087b-428d-b84c-6e9f04e80a8d
ORCID for A.J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 05 Nov 2012 14:35
Last modified: 15 Mar 2024 03:43

Export record

Altmetrics

Contributors

Author: C. Linard
Author: V.A. Alegana
Author: A.M. Noor
Author: R.W. Snow
Author: A.J. Tatem ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×