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Lu, Binbin, 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 |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/5754 |
| 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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