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Population 24/7: building space-time specific population surface models

Population 24/7: building space-time specific population surface models
Population 24/7: building space-time specific population surface models
Many areas of social science research rely on small area representations of population. Current approaches to spatial population modelling rely almost exclusively on georeferencing of residential locations, drawing heavily on census definitions of ‘resident population’ and therefore essentially presenting an abstract representation of night-time population distribution (Bhaduri, 2008). There are however, good conceptual and practical arguments for modelling population at different times, incorporating population movements from seasonal to diurnal timescales so as to predict, for example, population exposure to a specific hazard or potential customer numbers during a working day. This paper presents early results from an ESRC-funded project to develop space-time specific population surface models of the UK. The project is based on an existing adaptive kernel density approach for building gridded surface population models (Martin, 1996), which is now being extended into a spatio-temporal kernel density estimation method. We begin by briefly reviewing relevant methods, then move on to our conceptual framework, data sources and modelling approach and conclude with some early illustrative results.
space-time, surface, population modelling, grid, spatio-temporal
41-48
University College London
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Leung, Samuel
97eabff8-58eb-45f8-a3c5-cbe085665789
Hakley, M.
Morley, J.
Rahemtulla, H.
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Leung, Samuel
97eabff8-58eb-45f8-a3c5-cbe085665789
Hakley, M.
Morley, J.
Rahemtulla, H.

Cockings, Samantha, Martin, David and Leung, Samuel (2010) Population 24/7: building space-time specific population surface models. Hakley, M., Morley, J. and Rahemtulla, H. (eds.) In Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010. University College London. pp. 41-48 .

Record type: Conference or Workshop Item (Paper)

Abstract

Many areas of social science research rely on small area representations of population. Current approaches to spatial population modelling rely almost exclusively on georeferencing of residential locations, drawing heavily on census definitions of ‘resident population’ and therefore essentially presenting an abstract representation of night-time population distribution (Bhaduri, 2008). There are however, good conceptual and practical arguments for modelling population at different times, incorporating population movements from seasonal to diurnal timescales so as to predict, for example, population exposure to a specific hazard or potential customer numbers during a working day. This paper presents early results from an ESRC-funded project to develop space-time specific population surface models of the UK. The project is based on an existing adaptive kernel density approach for building gridded surface population models (Martin, 1996), which is now being extended into a spatio-temporal kernel density estimation method. We begin by briefly reviewing relevant methods, then move on to our conceptual framework, data sources and modelling approach and conclude with some early illustrative results.

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More information

Published date: 2010
Venue - Dates: conference; gb; 2010-04-14; 2010-04-16, London, United Kingdom, 2010-04-13 - 2010-04-15
Keywords: space-time, surface, population modelling, grid, spatio-temporal

Identifiers

Local EPrints ID: 164199
URI: http://eprints.soton.ac.uk/id/eprint/164199
PURE UUID: f683a943-ae32-41f9-9b34-83ee3b57f0f0
ORCID for Samantha Cockings: ORCID iD orcid.org/0000-0003-3333-4376
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769

Catalogue record

Date deposited: 22 Sep 2010 08:15
Last modified: 09 Jul 2022 01:38

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Contributors

Author: David Martin ORCID iD
Author: Samuel Leung
Editor: M. Hakley
Editor: J. Morley
Editor: H. Rahemtulla

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