Local Linear Estimation of Spatially Varying Coefficient Models: An Improvement on the Geographically Weighted Regression Technique

Abstract

Geographically weighted regression (GWR), as a useful method for exploring spatial non-stationarity of a regression relationship, has been applied to a variety of areas. In this method a spatially varying coefficient model is locally calibrated and the spatial-variation patterns of the locally estimated regression coefficients are taken as the main evidence of spatial nonstationarity for the underlying data-generating processes. Therefore, the validity of the analysis results drawn by GWR is closely dependent on the accuracy between the underlying coefficients and their estimates. Motivated by the local polynomial-modelling technique in statistics, we propose a local linear-based GWR for the spatially varying coefficient models, in which the coefficients are locally expanded as linear functions of the spatial coordinates and then estimated by the weighted least-squares procedure. Some theoretical and numerical comparisons with GWR are conducted and the results demonstrate that the proposed method can significantly improve GWR, not only in goodness-of-fit of the whole regression function but also in reducing bias of the coefficient estimates.