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
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.
Repository Staff Only(login required)
 |
Item control page |
Downloads per month over past year
Origin of downloads
Altmetric
Altmetric