Geographically Weighted Regression

By Roger S. Bivand, Danlin Yu. 2015

1,229 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d6c6aeb5441fc804ac1f27a75caa8b52304579fa

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Citations in the corpus

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).”
Estimation and inference in spatially varying coefficient models (2018, methodology): “In addition, under the independence error condition, which is not uncommon in the GWR literature (Brunsdon et al., 1996, 1998; Shen et al., 2011; Huang et al., 2017; Su et al., 2017, for example), we show the proposed coefficient estimators converge to the true coefficient functions. An important st”
A statistical method based on remote sensing for the estimation of air temperature in China (2015, methodology): “35: 2131–2143 (2015) data; and (3) the residuals derived using GWR were spatially independent, making it unnecessary to adjust them using kriging.”
Mapping the mean annual precipitation of China using local interpolation techniques (2014, methodology): “Note this approach is different from Kumar et al. (2012), who kriged the residuals using a global variogram.”
Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014, methodology): “GWR provides an intuitive and technically accessible tool to explore spatially varying relationships (Páez and Wheeler 2009).”
Spatial interpolation methods applied in the environmental sciences: A review (2014, methodology): “The application of this method to spatial interpolation would be worthwhile (Wheeler and Páez, 2010); but its applications to environmental sciences are still rare (Harris et al.”
A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2.5 (2020, methodology): “The GWR model is mathematically expressed as follows (Fotheringham et al., 2002):”