Geographically weighted summary statistics — aframework for localised exploratory data analysis

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GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013, methodology): “Notable GW models include: GW summary statistics (Brunsdon, Fotheringham, and Charlton 2002); GW principal components analysis (GW PCA) (Fotheringham, Brunsdon, and Charlton 2002; Lloyd 2010a; Harris, Brunsdon, and Charlton 2011a); GW regression (Brunsdon, Fotheringham, and Charlton 1996, 1998,…”
The Impact of Surrounding Land Use and Vegetation on Single-Family House Prices (2004, unknown): “…14.6 13.7 13.3 Maximum absolute 53 189 49 066 46 549 residual ($) Mean absolute 12 225 11 578 11 316 residual ($) Standard deviation 10 020 9 527 9 202 residual ($) could still be present (Brunsdon et al, 2002; Fotheringham et al, 2002; Getis and Ord, 1992; Ord and Getis, 1995; Päez et al, 2001).”
Geographically Weighted Colocation Quotients: Specification and Application (2014, background): “The same premise prompted the development of local spatial autocorrelation statistics (Getis and Ord 1992; Anselin 1995) and local descriptive statistics (Brunsdon, Fotheringham, and Charlton 2002).”