Linking UK public geospatial data to build 24/7 space-time specific population surface models
Leung, Samuel, Martin, David and Cockings, Samantha (2010) Linking UK public geospatial data to build 24/7 space-time specific population surface models. In, GIScience 2010: Sixth international conference on Geographic Information Science, Zurich, Switzerland, 14 - 17 Sep 2010. University of Zurich, 7pp.
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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.
|Item Type:||Conference or Workshop Item (Paper)|
|Keywords:||population modelling, gridded population, linked data, geospatial data linkage, census, daytime population, night-time population; spatial-temporal; space-time|
|Subjects:||G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
H Social Sciences > HA Statistics
|Divisions :||University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
|Accepted Date and Publication Date:||
|Date Deposited:||22 Sep 2010 08:07|
|Last Modified:||31 Mar 2016 13:29|
Population 24/7: space-time specific population surface modelling
Funded by: ESRC National Centre for Research Methods (RES-062-23-1811)
Led by: David James Martin
1 March 2009 to 30 April 2011
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