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Spatial nonstationarity and autoregressive models

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


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Until relatively recently, the emphasis of spatial analysis was on the investigation of global models and global processes. Recent research, however, has tended to explore exceptions to general processes, and techniques have been developed which have as their focus the investigation of spatial variations in local relationships. One of these techniques, known as geographically weighted regression (GWR), developed by the authors is used here to investigate spatial variations in spatial association. The particular framework in which spatial association is examined here is the spatial autoregressive model of Ord, although the technique can easily be applied to any form of spatial autocorrelation measurement. The conceptual and theoretical foundations of GWR applied to the Ord model are followed by an empirical example which uses data on owner-occupation in the housing market of Tyne and Wear in northeast England where the problems of relying on global models of spatial association are demonstrated. This empirical investigation of spatial variations in spatial autocorrelation prompts a further discussion of several issues concerning the statistical technique.

Publication metadata

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

Publication type: Article

Publication status: Published

Journal: Environment and Planning A

Year: 1998

Volume: 30

Issue: 6

Pages: 957-973

Print publication date: 01/06/1998

ISSN (print): 0308-518X

ISSN (electronic): 1472-3409


DOI: 10.1068/a300957


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