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Some notes on parametric significance tests for geographically weighted regression

Lookup NU author(s): Dr Christopher Brunsdon, Professor Alexander Fotheringham, M Charlton


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The technique of geographically weighted regression (GWR) is used to model spatial 'drift' in linear model coefficients. In this paper we extend the ideas of GWR in a number of ways. First, We introduce a set of analytically derived significance tests allowing a null hypothesis of no spatial parameter drift to be investigated. Second, we discuss 'mixed' GWR models where some parameters are fixed globally but others vary geographically. Again, models of this type maybe assessed using significance tests. Finally, we consider a means of deciding the degree of parameter smoothing used in GWR based on the Mallows C-p statistic. To complete the paper, we analyze an example data set based on house prices in Kent in the U.K. using the techniques introduced.

Publication metadata

Author(s): Brunsdon CF, Fotheringham AS, Charlton ME

Publication type: Article

Publication status: Published

Journal: Journal of Regional Science

Year: 1999

Volume: 39

Issue: 3

Pages: 497-524

ISSN (print): 0022-4146

ISSN (electronic): 1467-9787


DOI: 10.1111/0022-4146.00146


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