A Family of Geographically Weighted Regression Models

Abstract

A Bayesian treatment of locally linear regression methods intro-duced in McMillen (1996) and labeled geographically weighted regres-sions (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 contribution in the area of spatial econometrics, it also presents problems. It is ar-gued that a Bayesian treatment can resolve these problems and has a great many advantages over ordinary least-squares estimation used by the GWR method.