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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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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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