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.
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: |
|
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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