Geographically Weighted Regression using a non-euclidean distance metric with simulation data

In this study, we investigate the performance of a non-Euclidean distance metric in calibrating a Geographically Weighted Regression (GWR) model with a simulated data set. Random predictor variable and spatially varying coefficients are generated on a square grid of size 20*20. We respectively apply Manhattan and Euclidean distance metrics for the GWR calibrations. The preliminary findings show that Manhattan distance performs significantly better than the traditional choice for GWR - Euclidean distance. In particular, it outperforms in the accuracy of coefficient estimates.