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, where local regression parameters are estimated using subsets of data proximate to a model estimation point in variable space. The innovation with GWR is using a subset of data proximate to the model calibration location in geographical space instead of variable space. While the emphasis in traditional locally weighted regression in statistics has been on curve-fitting, that is estimating or predic ting the response variable, GWR has been presented as a method to conduct inference on spatially varying relationships, in an attempt to extend the original emphasis on prediction to confirmatory analysis (Páez and Wheeler 2009).
CITATION STYLE
Wheeler, D. C., & Páez, A. (2010). Geographically Weighted Regression. In Handbook of Applied Spatial Analysis (pp. 461–486). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-03647-7_22
Mendeley helps you to discover research relevant for your work.