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Robust Geographically Weighted Regression: A Technique for Quantifying Spatial Relationships Between Freshwater Acidification Critical Loads and Catchment Attributes

Lookup NU author(s): Professor Alexander Fotheringham, Emeritus Professor Steve Juggins


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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.

Publication metadata

Author(s): Harris P, Fotheringham AS, Juggins S

Publication type: Article

Publication status: Published

Journal: Annals of The Association of American Geographers

Year: 2010

Volume: 100

Issue: 2

Pages: 286-306

Print publication date: 08/03/2010

ISSN (print): 0004-5608

ISSN (electronic): 1467-8306

Publisher: Routledge


DOI: 10.1080/00045600903550378


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