Journal of Geographical Systems

, Volume 18, Issue 4, pp 303–329 | Cite as

Geographically weighted regression and multicollinearity: dispelling the myth

  • A. Stewart Fotheringham
  • Taylor M. OshanEmail author
Original Article


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.


Geographically weighted regression GWR Collinearity Regression diagnostics 

JEL Classification

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



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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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Geographical Sciences and Urban Planning, Arizona State University Coor HallArizona State UniversityTempeUSA

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