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.

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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.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Geographically Weighted Regression; non-Euclidean distance; Manhattan distance; simulation data; Manhattan distance;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID:	5754
Depositing User:	Martin Charlton
Date Deposited:	02 Feb 2015 16:00
Refereed:	Yes
URI:	Organisation
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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