Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR)

Daniel A. Griffith

266 citations0/0 files storedEnvironment and Planning A Economy and Space

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Spatial and Panel Data Analysis Regional Economic and Spatial Analysis demographic modeling and climate adaptation

Abstract

Interaction terms are constructed with georeferenced attribute variables and spatial filter eigenvectors, and then used to compute geographically varying regression coefficients. These coefficients, which are analogous to geographically weighted regression (GWR) coefficients, display preferable properties, and this specification is used to critique selected features of GWR. Comparisons are illustrated with the Georgia data appearing in the standard GWR tutorial.

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References

1995Local Indicators of Spatial Association—LISA 2002Geographically Weighted Regression: The Analysis of Spatially Varying Relationships 2002All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices 2005Multicollinearity and correlation among local regression coefficients in geographically weighted regression 2006SPATIAL MODELING IN ECOLOGY: THE FLEXIBILITY OF EIGENFUNCTION SPATIAL ANALYSES 2003Spatial Autocorrelation and Spatial Filtering 2012Spatial Autocorrelation and Spatial Filtering: Gaining Understanding Through Theory and Scientific Visualization 2007Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach

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2009Geographically Weighted Regression 2011A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships 2021Replication across space and time must be weak in the social and environmental sciences 2016Geographically weighted regression with parameter-specific distance metrics 2008Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

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