Population 24/7: building space-time specific population surface models
Cockings, Samantha, Martin, David and Leung, Samuel (2010) Population 24/7: building space-time specific population surface models. In, Hakley, M., Morley, J. and Rahemtulla, H. (eds.) Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010. London, University College London, 41-48.
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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.
|Item Type:||Book Section|
|Keywords:||space-time, surface, population modelling, grid, spatio-temporal|
|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
|Date Deposited:||22 Sep 2010 08:15|
|Last Modified:||02 Mar 2012 13:37|
|Publisher:||University College London|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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