The University of Southampton
University of Southampton Institutional Repository

Assessing the use of global land cover data for guiding large area population distribution modelling

Assessing the use of global land cover data for guiding large area population distribution modelling
Assessing the use of global land cover data for guiding large area population distribution modelling
Gridded population distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate population distribution data. The effectiveness of the population distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving population distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded population datasets across large areas.
population mapping, global land cover data, census data, dasymetric modelling, globcover
0343-2521
525-538
Linard, C.
40dc396f-bbf0-4ae2-8732-7a73447a9100
Gilbert, M.
1783ad6f-c32e-46dd-8de0-eb0e139afce0
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Linard, C.
40dc396f-bbf0-4ae2-8732-7a73447a9100
Gilbert, M.
1783ad6f-c32e-46dd-8de0-eb0e139afce0
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Linard, C., Gilbert, M. and Tatem, A.J. (2010) Assessing the use of global land cover data for guiding large area population distribution modelling. GeoJournal, 76 (5), 525-538. (doi:10.1007/s10708-010-9364-8).

Record type: Article

Abstract

Gridded population distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate population distribution data. The effectiveness of the population distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving population distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded population datasets across large areas.

This record has no associated files available for download.

More information

Published date: 25 May 2010
Keywords: population mapping, global land cover data, census data, dasymetric modelling, globcover
Organisations: Geography & Environment, PHEW – S (Spatial analysis and modelling)

Identifiers

Local EPrints ID: 344432
URI: http://eprints.soton.ac.uk/id/eprint/344432
ISSN: 0343-2521
PURE UUID: 5246c3dd-0e69-4223-a8ca-0b6d92cb148e
ORCID for A.J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 05 Nov 2012 12:18
Last modified: 15 Mar 2024 03:43

Export record

Altmetrics

Contributors

Author: C. Linard
Author: M. Gilbert
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.

×