High-performance solutions of geographically weighted regression in R

C.5 Geographically Weighted Regression

Geographically weighted regression (GWR) was introduced to the geography literature by Brunsdon et al. (1996) to study the potential for relationships in a regression model to vary in geographical space, or what is termed parametric nonstationarity. GWR is based on the non-parametric technique of locally weighted regression developed in statistics for curve-fitting and smoothing applications, w...

Geographically Weighted Regression White Paper

Modeling church services supply and performance, using geographically weighted regression

The objective of this study is to develop a multiple linear regression model that measures the relationship between the church services supply and the attendance to the services in the Uppsala diocese, Church of Sweden. By reviewing previous models and examining the nature of data available, two research questions were introduced, namely, the problem of omitted variables and the problem of spat...

Mapping the Results of Geographically Weighted Regression

Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. Previous approaches to mapping the results of GWR have primarily employed an equal step classification and sequential no-hue colour scheme for choropleth mapping of parameter estimates. This cartographic approach may hinder the exploration of spatial nonstationarity by inadeq...

A Family of Geographically Weighted Regression Models

A Bayesian treatment of locally linear regression methods introduced in McMillen (1996) and labeled geographically weighted regressions (GWR) in Brunsdon, Fotheringham and Charlton (1996) is set forth in this paper. GWR uses distance-decay-weighted sub-samples of the data to produce locally linear estimates for every point in space. While the use of locally linear regression represents a true c...