Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression

By David C. Wheeler. 2007

272 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0f3283710c84e6d13363f8e81d774feeaff519ae

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Citations in the corpus

Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014, background): “Methods to address local collinearity issues that are inherent in local regression modelling have also been developed (Wheeler 2007, 2009).”
A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships (2011, methodology): “A better alternative is to use the tools of variance decomposition proportions and condition indexes of the GWR design matrix detailed in Wheeler (2007) to diagnose collinearity in the GWR model.”
mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale (2018, background): “The VDP’s indicate that some areas may be subject to the effects of collinearity; however, none of the CC’s, VIF’s nor CN’s indicate that collinearity is problematic for any of the calibration locations.”
Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR) (2008, background): “Second, the more parsimonious coefficients obtained by incorporating map pattern-attribute variable interaction effects into a linear regression analysis exhibit considerably less multicollinearity (for an alternative approach, see Wheeler, 2007).”
Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor (2011, background): “For instances where collinearity problems cannot be adequately addressed with basic GWR, the ridge or lasso GWR models of Wheeler (2007, 2009) would be a more formal way of dealing with them.”
Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso (2009, methodology): “As an additional example,Wheeler (2007) applies collinearity diagnostic tools in a Columbus, Ohio, crime dataset to clearly link local collinearity to strong GWR coefficient correlation and increased coefficient variability for two covariates at numerous data locations with counterintuitive…”
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “For example, 72 Wheeler (2007) proposes a ridge regularization-based GWR that replaces Eq.”
Modelling hedonic residential rents for land use and transport simulation while considering spatial effects (2010, methodology): “…statistical tools. erefore, several authors have compared the GWR models with alternatives in this regard, particularly with methodological improvements of the GWR approach (Griffith 2008; Wheeler 2007, 2009) and Bayesian spatially varying coefficient process models (Wheeler and Waller 2009).”
Living with Collinearity in Local Regression Models (2012, methodology): “These remedies build on the geographically weighted models proposed by Wheeler (2007; 2009), following the initial highlighting of this issue presented in Wheeler and Tiefelsdorf (2005).”
A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore (2018, background): “In addition, different approaches to address local collinearity issues have been studied (Wheeler 2007, 2009).”
Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data (2010, background): “The approach we suggest does little to address the problem of spurious spatial patterns introduced by the mechanics of GWR (e.g. Wheeler and Teifelsdorf, 2005; Wheeler, 2007), but it does attend to residual spatial dependence remaining in or generated by the GWR procedure.”
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013, background): “Coefficients can be estimated, but with inflated standard errors (see Wheeler 2007, for spatial regression 115 examples).”
GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013, methodology): “…models Collinearity is a problem in any form of regression modelling and the importance of assessing and taking action has been raised by the many commentaries on local collinearity in GW regression (Wheeler and Tiefelsdorf 2005; Wheeler 2007, 2009, 2010, 2013a; Griffith 2008; Páez et al. 2011).”