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Progress report: 24-hour gridded population models

Progress report: 24-hour gridded population models
Progress report: 24-hour gridded population models
In a paper presented at the 2009 EFGS Conference in The Hague (Martin et al., 2009) we set out the early stages of a UK research project focused on the production of time-specific gridded population models. The present paper reviews overall progress with this work and develops in more detail some aspects of the modelling principles and available data sources which make this possible. The paper seeks to demonstrate how readily accessible data are beginning to provide many of the key inputs required to make time-specific representations of population. Time-specific models have the potential to overcome many key weaknesses of current representations which are generally based on dated population statistics about population numbers at place of residence and which fail to reflect massive cyclical spatial redistributions of population. In many application areas, current analyses persist in using population maps with major deficiencies due to the absence of plausible alternative models. The remainder of this paper is structured as follows: the following section provides a brief review of the case for more sophisticated time-space population distributions. The thirdsection reviews issues arising from the challenge of data source acquisition and integration and the final section indicates the progress made in the project to date with regard to addressing these challenges
Martin, David
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Cockings, Samantha
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Leung, Samuel
97eabff8-58eb-45f8-a3c5-cbe085665789
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Leung, Samuel
97eabff8-58eb-45f8-a3c5-cbe085665789

Martin, David, Cockings, Samantha and Leung, Samuel (2010) Progress report: 24-hour gridded population models. European Forum for Geostatistics Conference, Tallinn, Estonia. 05 - 07 Oct 2010.

Record type: Conference or Workshop Item (Other)

Abstract

In a paper presented at the 2009 EFGS Conference in The Hague (Martin et al., 2009) we set out the early stages of a UK research project focused on the production of time-specific gridded population models. The present paper reviews overall progress with this work and develops in more detail some aspects of the modelling principles and available data sources which make this possible. The paper seeks to demonstrate how readily accessible data are beginning to provide many of the key inputs required to make time-specific representations of population. Time-specific models have the potential to overcome many key weaknesses of current representations which are generally based on dated population statistics about population numbers at place of residence and which fail to reflect massive cyclical spatial redistributions of population. In many application areas, current analyses persist in using population maps with major deficiencies due to the absence of plausible alternative models. The remainder of this paper is structured as follows: the following section provides a brief review of the case for more sophisticated time-space population distributions. The thirdsection reviews issues arising from the challenge of data source acquisition and integration and the final section indicates the progress made in the project to date with regard to addressing these challenges

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More information

e-pub ahead of print date: 2010
Venue - Dates: European Forum for Geostatistics Conference, Tallinn, Estonia, 2010-10-05 - 2010-10-07
Related URLs:
Organisations: Geography, Geography & Environment, PHEW – S (Spatial analysis and modelling), Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 175321
URI: http://eprints.soton.ac.uk/id/eprint/175321
PURE UUID: 5aa71ab7-3ebc-4c2c-91de-0289c99aeb0a
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769
ORCID for Samantha Cockings: ORCID iD orcid.org/0000-0003-3333-4376

Catalogue record

Date deposited: 23 Feb 2011 09:51
Last modified: 15 Mar 2024 02:45

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Contributors

Author: David Martin ORCID iD
Author: Samuel Leung

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