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A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships

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  • Antonio Páez

    (Centre for Spatial Analysis/School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario L8S 4K1, Canada)

  • Steven Farber

    (The Department of Geography, University of Utah, Salt Lake City, UT 84112, USA)

  • David Wheeler

    (Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA 20892, USA)

Abstract

Large variability and correlations among the coefficients obtained from the method of geographically weighted regression (GWR) have been identified in previous research. This is an issue that poses a serious challenge for the utility of the method as a tool to investigate multivariate relationships. The objectives of this paper are to assess: (1) the ability of GWR to discriminate between a spatially constant processes and one with spatially varying relationships; and (2) to accurately retrieve spatially varying relationships. Extensive numerical experiments are used to investigate situations where the underlying process is stationary and nonstationary, and to assess the degree to which spurious intercoefficient correlations are introduced. Two different implementations of GWR and cross-validation approaches are assessed. Results suggest that judicious application of GWR can be used to discern whether the underlying process is nonstationary. Furthermore, evidence of spurious correlations indicates that caution must be exercised when drawing conclusions regarding spatial relationships retrieved using this approach, particularly when working with small samples.

Suggested Citation

  • Antonio Páez & Steven Farber & David Wheeler, 2011. "A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships," Environment and Planning A, , vol. 43(12), pages 2992-3010, December.
  • Handle: RePEc:sae:envira:v:43:y:2011:i:12:p:2992-3010
    DOI: 10.1068/a44111
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    References listed on IDEAS

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    1. David Wheeler & Catherine Calder, 2007. "An assessment of coefficient accuracy in linear regression models with spatially varying coefficients," Journal of Geographical Systems, Springer, vol. 9(2), pages 145-166, June.
    2. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, June.
    3. Steven Farber & Antonio Páez, 2007. "A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations," Journal of Geographical Systems, Springer, vol. 9(4), pages 371-396, December.
    4. David Wheeler & Lance Waller, 2009. "Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests," Journal of Geographical Systems, Springer, vol. 11(1), pages 1-22, March.
    5. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
    6. Kamar Ali & Mark D. Partridge & M. Rose Olfert, 2007. "Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?," International Regional Science Review, , vol. 30(3), pages 300-329, July.
    7. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    8. Daniel P. McMillen, 2003. "Spatial Autocorrelation Or Model Misspecification?," International Regional Science Review, , vol. 26(2), pages 208-217, April.
    9. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model," Environment and Planning A, , vol. 32(1), pages 9-32, January.
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