GWmodel: An R package for exploring spatial heterogeneity using geographically weighted models

Isabella Gollini*, Binbin Lu, Martin Charlton, Christopher Brunsdon, Paul Harris

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

173 Citations (Scopus)
960 Downloads (Pure)

Abstract

Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GW model, we present techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localized calibration provides a better description. The approach uses a moving window weighting technique, where localized models are found at target locations. Outputs are mapped to provide a useful exploratory tool into the nature of the data spatial heterogeneity. Currently, GW model includes functions for: GW summary statistics, GW principal components analysis, GW regression, and GW discriminant analysis; some of which are provided in basic and robust forms.

Original languageEnglish
Number of pages50
JournalJournal of Statistical Software
Volume63
Issue number17
Early online date16 Feb 2015
DOIs
Publication statusPublished - Feb 2015

Keywords

  • Geographically weighted principal components analysis
  • Geographically weighted regression
  • R package
  • Robust
  • Spatial prediction

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