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Statistical tests for spatial nonstationarity based on the geographically weighted regression model

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Author Info

  • Yee Leung
  • Chang-Lin Mei
  • Wen-Xiu Zhang
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    Abstract

    Geographically weighted regression (GWR) is a way of exploring spatial nonstationarity by calibrating a multiple regression model which allows different relationships to exist at different points in space. Nevertheless, formal testing procedures for spatial nonstationarity have not been developed since the inception of the model. In this paper the authors focus mainly on the development of statistical testing methods relating to this model. Some appropriate statistics for testing the goodness of fit of the GWR model and for testing variation of the parameters in the model are proposed and their approximated distributions are investigated. The work makes it possible to test spatial nonstationarity in a conventional statistical manner. To substantiate the theoretical arguments, some simulations are run to examine the power of the statistics for exploring spatial nonstationarity and the results are encouraging. To streamline the model, a stepwise procedure for choosing important independent variables is also formulated. In the last section, a prediction problem based on the GWR model is studied, and a confidence interval for the true value of the dependent variable at a new location is also established. The study paves the path for formal analysis of spatial nonstationarity on the basis of the GWR model.

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    Bibliographic Info

    Article provided by Pion Ltd, London in its journal Environment and Planning A.

    Volume (Year): 32 (2000)
    Issue (Month): 1 (January)
    Pages: 9-32

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    Handle: RePEc:pio:envira:v:32:y:2000:i:1:p:9-32

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

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    Cited by:
    1. Yan Kestens & Marius Thériault & François Des Rosiers, 2006. "Heterogeneity in hedonic modelling of house prices: looking at buyers’ household profiles," Journal of Geographical Systems, Springer, vol. 8(1), pages 61-96, March.
    2. Wei, Chuan-Hua & Qi, Fei, 2012. "On the estimation and testing of mixed geographically weighted regression models," Economic Modelling, Elsevier, vol. 29(6), pages 2615-2620.
    3. Efthymiou, D. & Antoniou, C., 2013. "How do transport infrastructure and policies affect house prices and rents? Evidence from Athens, Greece," Transportation Research Part A: Policy and Practice, Elsevier, vol. 52(C), pages 1-22.
    4. Ciriaci, Daria & Palma, Daniela, 2010. "Geography, environmental efficiency and Italian economic growth: a spatially-adapted Environmental Kuznets Curve," MPRA Paper 22899, University Library of Munich, Germany.
    5. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, . "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Tinbergen Institute Discussion Papers 13-171/VIII, Tinbergen Institute.
    6. Dan-Lin Yu, 2006. "Spatially varying development mechanisms in the Greater Beijing Area: a geographically weighted regression investigation," The Annals of Regional Science, Springer, vol. 40(1), pages 173-190, March.
    7. Katharina Pijnenburg, 2013. "Self-Employment and Economic Performance: A Geographically Weighted Regression Approach for European Regions," Discussion Papers of DIW Berlin 1272, DIW Berlin, German Institute for Economic Research.
    8. 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.
    9. Du, Hongbo & Mulley, Corinne, 2012. "Understanding spatial variations in the impact of accessibility on land value using geographically weighted regression," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 5(2), pages 46-59.

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