GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models

By Isabella Gollini, Binbin Lu, Martin Charlton, Chris Brunsdon, Paul Harris. 2013

582 citations recorded by the source corpus.

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 GWmodel 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, GWmodel 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.

Subfields: Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2146994c91cb9383e1466ff2ee61d2722cc46d58

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In Economics and Econometrics

  1. Y. Yamagata
  2. Daisuke Murakami
  3. Takahiro Yoshida
  4. Hajime Seya
  5. Jakob A. Dambon
  6. Daniel A. Griffith
  7. Reinhard Furrer
  8. Fabio Sigrist

In Global and Planetary Change

  1. Jakob A. Dambon
  2. Takahiro Yoshida
  3. Daniel A. Griffith
  4. Hajime Seya
  5. Y. Yamagata
  6. Daisuke Murakami
  7. Reinhard Furrer
  8. Fabio Sigrist

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