TY - JOUR
T1 - Robust Geographically Weighted Regression
T2 - A Technique for Quantifying Spatial Relationships Between Freshwater Acidification Critical Loads and Catchment Attributes
AU - Harris, Paul
AU - Fotheringham, A. Stewart
AU - Juggins, Steve
N1 - Funding Information:
Research presented in this article was funded by a Strategic Research Cluster grant (07/SRC/I1168) by the Science Foundation Ireland under the Na- tional Development Plan. The authors gratefully acknowledge this support. Thanks are also due to the first author’s PhD studentship at Newcastle University and to researchers (Martin Kernan and company) in the Department of Geography, University College London, who provided the critical load and the contextual catchment data. Critical load data similar to that used in this study can also be found at http://critloads.ceh.ac.uk/index.htm (last accessed 10 January 2009). We also acknowledge Chris Brunsdon for providing us with the original GWR R codes that we then adapted for this study and the three anonymous referees who provided valuable comments and insights that helped improve the article.
PY - 2010/4
Y1 - 2010/4
N2 - Geographically weighted regression (GWR) is used to investigate spatial relationships between freshwater acidification critical load data and contextual catchment data across Great Britain. Although this analysis is important in developing a greater understanding of the critical load process, the study also examines the application of the GWR technique itself. In particular, and unlike many previous presentations of GWR, the steps taken in choosing a particular GWR model form are presented in detail. A further important advance here is that the calibration results of the chosen GWR model are scrutinized for robustness to outlying observations. With respect to the critical load process itself, the results of this study largely agree with those of earlier research, where relationships between critical load and catchment data can vary across space. The more sophisticated spatial statistical models used here, however, are shown to be more flexible and informative, allowing a clearer picture of process heterogeneities to be revealed.
AB - Geographically weighted regression (GWR) is used to investigate spatial relationships between freshwater acidification critical load data and contextual catchment data across Great Britain. Although this analysis is important in developing a greater understanding of the critical load process, the study also examines the application of the GWR technique itself. In particular, and unlike many previous presentations of GWR, the steps taken in choosing a particular GWR model form are presented in detail. A further important advance here is that the calibration results of the chosen GWR model are scrutinized for robustness to outlying observations. With respect to the critical load process itself, the results of this study largely agree with those of earlier research, where relationships between critical load and catchment data can vary across space. The more sophisticated spatial statistical models used here, however, are shown to be more flexible and informative, allowing a clearer picture of process heterogeneities to be revealed.
KW - Acidified surface waters
KW - Catchment characteristics
KW - Relationship nonstationarity
KW - Robust
KW - Spatial heterogeneity
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U2 - 10.1080/00045600903550378
DO - 10.1080/00045600903550378
M3 - Article
AN - SCOPUS:77951287343
SN - 0004-5608
VL - 100
SP - 286
EP - 306
JO - Annals of the Association of American Geographers
JF - Annals of the Association of American Geographers
IS - 2
ER -