Lu, Binbin and Charlton, Martin and Fotheringham, Stewart
(2012)
Geographically Weighted Regression using a non-euclidean distance metric with simulation data.
In: Agro-Geoinformatics (Agro-Geoinformatics), 2012 First International Conference on, 2-4 August, 2012, Shangai, China.
Abstract
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.
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