Bandwidth Selection in Geographically Weighted Regression Models via Information Complexity Criteria

Abstract

The geographically weighted regression (GWR) model is a local spatial regression technique used to determine and map spatial variations in the relationships between variables. In the GWR model, the bandwidth is very important as it can change the parameter estimates and affect the model performance. In this study, we applied the information complexity (ICOMP) type criteria in the selection of fixed bandwidth for the first time in the literature. The ICOMP-type criteria use a complexity measure that measures how parameters in the model relate to each other. A real dataset example and a simulation study have been conducted. Results of the simulation demonstrate that GWR models created with the bandwidth selection by ICOMP-type criteria show superior performance. In addition, when the bandwidth is selected according to the ICOMP-type criteria and the GWR model is created for the actual total fertility rate data, it is seen that the spatial distribution of the total fertility rate estimates is quite compatible with the distribution of the actual total fertility rate. According to the results, ICOMP-type criteria can be used effectively instead of the classical criteria in the literature in the selection of bandwidth in the GWR model.