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Spatial nonstationarity and autoregressive models

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  • C Brunsdon
  • A S Fotheringham
  • M Charlton
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    Abstract

    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.

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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): 6 (June)
    Pages: 957-973

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    Handle: RePEc:pio:envira:v:30:y:1998:i:6:p:957-973

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    Web page: http://www.pion.co.uk

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    Cited by:
    1. Jesús Mur & Fernando López & Ana Angulo, 2010. "Instability in spatial error models: an application to the hypothesis of convergence in the European case," Journal of Geographical Systems, Springer, vol. 12(3), pages 259-280, September.
    2. Sunak, Yasin & Madlener, Reinhard, 2012. "The Impact of Wind Farms on Property Values: A Geographically Weighted Hedonic Pricing Model," FCN Working Papers 3/2012, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Mar 2013.
    3. Stephen Matthews & Tse-Chuan Yang, 2012. "Mapping the results of local statistics," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(6), pages 151-166, March.

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