Calibrating a Geographically Weighted Regression Model with Parameter-Specific Distance Metrics


Lu, Binbin and Harris, Paul and Charlton, Martin and Brunsdon, Chris (2015) Calibrating a Geographically Weighted Regression Model with Parameter-Specific Distance Metrics. Procedia Environmental Sciences, 26. pp. 110-115. ISSN 1878-0296

[img]
Preview
Download (574kB) | Preview


Share your research

Twitter Facebook LinkedIn GooglePlus Email more...



Add this article to your Mendeley library


Abstract

Geographically Weighted Regression (GWR) is a local technique that models spatially varying relationships, where Euclidean distance is traditionally used as default in its calibration. However, empirical work has shown that the use of non-Euclidean distance metrics in GWR can improve model performance, at least in terms of predictive fit. Furthermore, the relationships between the dependent and each independent variable may have their own distinctive response to the weighting computation, which is reflected by the choice of distance metric. Thus, we propose a back-fitting approach to calibrate a GWR model with parameter-specific distance metrics. To objectively evaluate this new approach, a simple simulation experiment is carried out that not only enables an assessment of prediction accuracy, but also parameter accuracy. The results show that the approach can provide both more accurate predictions and parameter estimates, than that found with standard GWR. Accurate localised parameter estimation is crucial to GWR’s main use as a method to detect and assess relationship non-stationarity.

Item Type: Article
Keywords: Non-stationarity; GWR; Parameter-Specific Distance Metrics; Simulation Experiment;
Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID: 7846
Identification Number: 10.1016/j.proenv.2015.05.011
Depositing User: Martin Charlton
Date Deposited: 01 Feb 2017 15:57
Journal or Publication Title: Procedia Environmental Sciences
Publisher: Elsevier
Refereed: Yes
URI:

Repository Staff Only(login required)

View Item Item control page

Document Downloads

More statistics for this item...