Using Contextualised Geographically Weighted Regression to Model the Spatial Heterogeneity of Land Prices in Beijing, China

Geographically Weighted Regression (GWR) is a method of spatial statistical analysis allowing the modelled relationship between a response variable and a set of covariates to vary geographically across a study region. Its use of geographical weighting arises from the expectation that observations close together by distance are likely to share similar characteristics. In practice, however, two points can be geographically close but socially distant because the contexts (or neighbourhoods) within which they are situated are not alike. Drawing on a previous study of geographically and temporally weighted regression, in this paper we develop what we describe as contextualised Geographically Weighted Regression (CGWR), applying it to the field of hedonic house price modelling to examine spatial heterogeneity in the land parcel prices of Beijing, China. Contextual variables are incorporated into the analysis by adjusting the geographical weights matrix to measure proximity not only by distance but also with respect to an attribute space defined by measures of each observation’s neighbourhood. Comparing CGWR with GWR suggests that adding the contextual information improves the model fits.