Harris, Paul and Fotheringham, Stewart and Crespo, R. and Charlton, Martin
(2010)
The Use of Geographically Weighted Regression
for Spatial Prediction: An Evaluation of Models Using
Simulated Data Sets.
Mathematical Geosciences, 42 (6).
pp. 657-680.
ISSN 1874-8961
Abstract
Increasingly, the geographically weighted regression (GWR) model is be-
ing used for spatial prediction rather than for inference. Our study compares GWR as
a predictor to (a) its global counterpart of multiple linear regression (MLR); (b) tra-
ditional geostatistical models such as ordinary kriging (OK) and universal kriging
(UK), with MLR as a mean component; and (c) hybrids, where kriging models are
specified with GWR as a mean component. For this purpose, we test the performance
of each model on data simulated with differing levels of spatial heterogeneity (with
respect to data relationships in the mean process) and spatial autocorrelation (in the
residual process). Our results demonstrate that kriging (in a UK form) should be the
preferred predictor, reflecting its optimal statistical properties. However the GWR-
kriging hybrids perform with merit and, as such, a predictor of this form may pro-
vide a worthy alternative to UK for particular (non-stationary relationship) situations
when UK models cannot be reliably calibrated. GWR predictors tend to perform more
poorly than their more complex GWR-kriging counterparts, but both GWR-based
models are useful in that they provide extra information on the spatial processes gen-
erating the data that are being predicted.
| Item Type: |
Article
|
| Additional Information: |
Erratum included: Vol. 43 (3) 399, 2010. |
| Keywords: |
Relationship nonstationarity; Relationship heterogeneity;
GWR; Kriging; Spatial interpolation; |
| Academic Unit: |
Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG |
| Item ID: |
5764 |
| Identification Number: |
https://doi.org/10.1007/s11004-010-9284-7 |
| Depositing User: |
Martin Charlton
|
| Date Deposited: |
03 Feb 2015 15:33 |
| Journal or Publication Title: |
Mathematical Geosciences |
| Publisher: |
Springer Verlag |
| 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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