Robust geographically weighted regression: a technique for quantifying spatial relationships between freshwater acidification critical loads and catchment attributes

Paul Harris, A. Stewart Fotheringham, Steve Juggins

    Research output: Contribution to journalArticlepeer-review

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)286-306
    Number of pages21
    JournalAnnals of the Association of American Geographers
    Volume100
    Issue number2
    Early online date8 Mar 2010
    DOIs
    Publication statusPublished - 2010

    Keywords

    • acidified surface waters
    • catchment characteristics
    • relationship nonstationarity
    • robust
    • spatial heterogeneity

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