Linking UK public geospatial data to build 24/7 space-time specific population surface models
Linking UK public geospatial data to build 24/7 space-time specific population surface models
Until recently any attempt to model population distribution over space has been largely dependent on georeferencing of resident population and therefore presents an abstract representation of night-time population pattern (Bhaduri, 2008). There are however, good arguments for modelling population at different times, incorporating population movements from seasonal to diurnal timescales so as to predict, for example, vulnerable population for rapid disaster relief or potential customer numbers during a working day. This paper presents early results from a publicly-funded project to develop space-time specific population surface models of the UK. The project extends Martin’s (1996) adaptive kernel density approach into a spatio-temporal kernel density estimation for building gridded surface population models. We begin by briefly reviewing relevant methods, then move on to our conceptual modelling and data linkage and conclude with some early illustrative results.
population modelling, gridded population, linked data, geospatial data linkage, census, daytime population, night-time population, spatial-temporal, space-time
Leung, Samuel
97eabff8-58eb-45f8-a3c5-cbe085665789
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
September 2010
Leung, Samuel
97eabff8-58eb-45f8-a3c5-cbe085665789
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Leung, Samuel, Martin, David and Cockings, Samantha
(2010)
Linking UK public geospatial data to build 24/7 space-time specific population surface models.
GIScience 2010: Sixth international conference on Geographic Information Science, Zurich, Switzerland.
13 - 16 Sep 2010.
7 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Until recently any attempt to model population distribution over space has been largely dependent on georeferencing of resident population and therefore presents an abstract representation of night-time population pattern (Bhaduri, 2008). There are however, good arguments for modelling population at different times, incorporating population movements from seasonal to diurnal timescales so as to predict, for example, vulnerable population for rapid disaster relief or potential customer numbers during a working day. This paper presents early results from a publicly-funded project to develop space-time specific population surface models of the UK. The project extends Martin’s (1996) adaptive kernel density approach into a spatio-temporal kernel density estimation for building gridded surface population models. We begin by briefly reviewing relevant methods, then move on to our conceptual modelling and data linkage and conclude with some early illustrative results.
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Published date: September 2010
Venue - Dates:
GIScience 2010: Sixth international conference on Geographic Information Science, Zurich, Switzerland, 2010-09-13 - 2010-09-16
Keywords:
population modelling, gridded population, linked data, geospatial data linkage, census, daytime population, night-time population, spatial-temporal, space-time
Organisations:
Geography & Environment, PHEW – S (Spatial analysis and modelling), Population, Health & Wellbeing (PHeW), Remote Sensing & Spatial Analysis
Identifiers
Local EPrints ID: 164197
URI: http://eprints.soton.ac.uk/id/eprint/164197
PURE UUID: f4e8ea0d-37fc-4f88-8ab5-00462b63e3a7
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Date deposited: 22 Sep 2010 08:07
Last modified: 09 Jul 2022 01:38
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Contributors
Author:
Samuel Leung
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