Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

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Semiparametric geographically weighted generalisedlinear modelling in GWR 4.0 (2009, background): “To date, numerous theoretical and applied studies of GWR have been reported after the first seminal papers of GWR appeared (Fotheringham et al., 1996; Brunsdon et al, 1996).”
Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data (2010, methodology): “The GWR approach has been increasingly applied in spatial econometric studies to understand and control for spatial heterogeneity (McMillen, 1996; Brunsdon et al., 1996, 1999; Fotheringham et al., 1998, 2002; Leung et al., 2000a, b; Huang and Leung, 2002; Páez et al., 2002a, b; Yu and Wu, 2004;…”
Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China (2014, methodology): “Both AICs and CV will attempt to identify the best fixed distance or the best appropriate number of adjacent points of the regression province using the adaptive kernel technique (Brunsdon et al., 1996).”
Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014, methodology): “For example, different kernel functions have been suggested (e.g. Brunsdon et al. 1996, Fotheringham et al. 1998, Yrigoyen et al. 2007) and different rules to select an optimum bandwidth have also been proposed (e.g. Fotheringham et al. 2002, Páez et al. 2002, Farber and Páez 2007).”