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Spatial nonstationarity and autoregressive models

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  • C Brunsdon
  • A S Fotheringham
  • M Charlton

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.

Suggested Citation

  • C Brunsdon & A S Fotheringham & M Charlton, 1998. "Spatial nonstationarity and autoregressive models," Environment and Planning A, Pion Ltd, London, vol. 30(6), pages 957-973, June.
  • Handle: RePEc:pio:envira:v:30:y:1998:i:6:p:957-973
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    Cited by:

    1. Lebreton, Marie, 2005. "The NCSTAR model as an alternative to the GWR model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 77-84.
    2. Yuanshuo Xu & Mildred E Warner, 2016. "Does devolution crowd out development? A spatial analysis of US local government fiscal effort," Environment and Planning A, , vol. 48(5), pages 871-890, May.
    3. 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.
    4. Eckey, Hans-Friedrich & Kosfeld, Reinhold & Türck, Matthias, 2005. "Regional convergence in Germany: A geographically weighted regression approach," Volkswirtschaftliche Diskussionsbeiträge 76, University of Kassel, Faculty of Economics and Management.
    5. 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.
    6. Jesús Mur & Fernando López & Ana Angulo, 2009. "Testing the hypothesis of stability in spatial econometric models," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 409-444, June.
    7. Eckey, Hans-Friedrich & Türck, Matthias, 2005. "Convergence of EU-regions: A literature report," Volkswirtschaftliche Diskussionsbeiträge 80, University of Kassel, Faculty of Economics and Management.
    8. 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.
    9. Declan Curran, 2011. "British Regional Growth and Sectoral Trends - Global and Local Spatial Econometric Approaches," Post-Print hal-00687807, HAL.
    10. Chunzhu Wei & Mark Padgham & Pablo Cabrera Barona & Thomas Blaschke, 2017. "Scale-Free Relationships between Social and Landscape Factors in Urban Systems," Sustainability, MDPI, Open Access Journal, vol. 9(1), pages 1-19, January.

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