Characteristics of parameter mixed geographically weighted regression model : global (a-group) and local (b-group)

Abstract

Mixed Geographically Weighted Regression (MGWR) is a combination of a linear regression model and a Geographically Weighted Regression (GWR) model that considers several variables to be constant and some to vary spatially. The MGWR model also considers location characteristics as indicated by global and local variables. The MGWR model is more flexible than the GWR model because its characteristics are the same for each location. These characteristics are shown in the variables contained in the MGWR model, namely global and local variables. Global variables produce global a-group parameters that have the same effect for all observation locations and local variables produce local b-group parameters that have different effects for each observation location. This article reviews the characteristics of global (a-group) and local (b-group) parameters in the MGWR model. The research shows that the two parameters can be predicted by different methods, estimating global parameters in the regression model by the least squared (LS) method and estimating local parameters by the weighted least squared method (WLS). The difference in this parameter estimation method is due to local parameters influenced by the presence of observational characteristics so that the assumption does not occur heteroscedasticity cannot be obtained.