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Geographically weighted regression and multicollinearity: dispelling the myth

Journal of Geographical Systems volume 18, pages 303–329 (2016)Cite this article

Abstract

Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.

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Acknowledgments

We would like to thank two anonymous reviewers and the editor-in-chief for their helpful comments, which improved the quality of this research.

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Authors and Affiliations

  1. School of Geographical Sciences and Urban Planning, Arizona State University Coor Hall, Arizona State University, Tempe, AZ, 85287, USA

    A. Stewart Fotheringham & Taylor M. Oshan

Authors
  1. A. Stewart Fotheringham
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  2. Taylor M. Oshan
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Correspondence to Taylor M. Oshan.

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Fotheringham, A.S., Oshan, T.M. Geographically weighted regression and multicollinearity: dispelling the myth. J Geogr Syst 18, 303–329 (2016). https://doi.org/10.1007/s10109-016-0239-5

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  • DOI: https://doi.org/10.1007/s10109-016-0239-5

Keywords

  • Geographically weighted regression
  • GWR
  • Collinearity
  • Regression diagnostics

JEL Classification

  • C18 Methodological issues: general
  • C52 Model evaluation, validation, and selection