Lake, Iain R. ORCID: https://orcid.org/0000-0003-4407-5357, Lovett, Andrew A. ORCID: https://orcid.org/0000-0003-0554-9273, Bateman, Ian J. and Day, Brett (2000) Using GIS and large-scale digital data to implement hedonic pricing studies. International Journal of Geographical Information Science, 14 (6). pp. 521-541. ISSN 1365-8816
Full text not available from this repository.Abstract
This paper describes how a standard GIS package can be used to convert large-scale vector digital data (point, line and annotation features) into polygons using standardised and replicable methods. Building area, garden and land use polygons are all derived from such data (Ordnance Survey LandLine.Plus). These entities are then combined with further sources of digital data to derive more refined information such as property types. Finally, complex DEMs are developed for use in visibility studies. The variables calculated are subsequently employed in a property valuation study where many are found to be significant determinants of property price. The main exception is variables relating to viewsheds, although it is argued that this does not invalidate the techniques used in their deviation but highlights the difficulties involved in modelling a large number of variables in a property price analysis.
Item Type: | Article |
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Faculty \ School: | University of East Anglia Research Groups/Centres > Theme - ClimateUEA Faculty of Science > School of Environmental Sciences |
UEA Research Groups: | Faculty of Science > Research Groups > Environmental Social Sciences University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Centre for Social and Economic Research on the Global Environment (CSERGE) Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation |
Depositing User: | Rosie Cullington |
Date Deposited: | 08 Apr 2011 11:03 |
Last Modified: | 09 Oct 2024 13:32 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/28653 |
DOI: | 10.1080/136588100415729 |
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