Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis
Abstract
Geographically weighted regression and the expansion method are two statistical techniques which can be used to examine the spatial variability of regression results across a region and so inform on the presence of spatial nonstationarity. Rather than accept one set of 'global' regression results, both techniques allow the possibility of producing 'local' regression results from any point within the region so that the output from the analysis is a set of mappable statistics which denote local relationships. Within the paper, the application of each technique to a set of health data from northeast England is compared. Geographically weighted regression is shown to produce more informative results regarding parameter variation over space.Download Info
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Bibliographic Info
Article provided by Pion Ltd, London in its journal Environment and Planning A.
Volume (Year): 30 (1998)
Issue (Month): 11 (November)
Pages: 1905-1927
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Web page: http://www.pion.co.uk
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Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Porter, Michael D. & Brown, Donald E., 2007. "Detecting local regions of change in high-dimensional criminal or terrorist point processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2753-2768, February.
- Dagmar Schröter & Colin Polsky & Anthony Patt, 2005. "Assessing vulnerabilities to the effects of global change: an eight step approach," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 10(4), pages 573-595, October.
- Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
- Paul Longley & Carolina Tobón, 2003. "Spatial dependence and heterogeneity in patterns of urban deprivation," ERSA conference papers ersa03p132, European Regional Science Association.
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