Geographically weighted regression and multicollinearity: dispelling the myth |
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Authors: | A Stewart Fotheringham Taylor M Oshan |
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Institution: | 1.School of Geographical Sciences and Urban Planning, Arizona State University Coor Hall,Arizona State University,Tempe,USA |
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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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