Extreme coefficients in Geographically Weighted Regression and their effects on mapping
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This study deals with the issue of extreme coefficients in geographically
weighted regression (GWR) and their effects on mapping coefficients using three datasets
with different spatial resolutions. We found that although GWR yields extreme coefficients
regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to
generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on
polygon data representing aggregated areal units are more sensitive to extreme coefficients,
and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are
more vulnerable to the extreme coefficients than adaptive calibration.