Robust Geographically Weighted Regression

A Technique for Quantifying Spatial Relationships Between Freshwater Acidification Critical Loads and Catchment Attributes

Paul Harris, Stewart Fotheringham, Steve Juggins

Research output: Contribution to journalArticle

35 Citations (Scopus)

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 (US)
Pages (from-to)286-306
Number of pages21
JournalAnnals of the Association of American Geographers
Volume100
Issue number2
DOIs
StatePublished - Apr 2010
Externally publishedYes

Fingerprint

critical load
acidification
catchment
regression
calibration
attribute

Keywords

  • Acidified surface waters
  • Catchment characteristics
  • Relationship nonstationarity
  • Robust
  • Spatial heterogeneity

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

Cite this

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