In GWmodel, we introduce 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 localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics, GW principal components analysis, GW discriminant analysis and various forms of GW regression; some of which are provided in basic and robust (outlier resistant) forms.
Acknowledgements: We gratefully acknowledge support from Science Foundation Ireland under the National Development Plan through the award of a Strategic Research Centre grant 07-SRC-I1168.
Beta versions can always be found at https://github.com/lbb220/GWmodel, which includes all the newly developed functions for GW models.
For latest tutorials on using GWmodel please go to: https://rpubs.com/gwmodel
Binbin Lu, Paul Harris, Martin Charlton, Chris Brunsdon, Tomoki Nakaya, Isabella Gollini
Maintainer: Binbin Lu <[email protected]>
Gollini I, Lu B, Charlton M, Brunsdon C, Harris P (2015) GWmodel: an R Package for exploring Spatial Heterogeneity using Geographically Weighted Models. Journal of Statistical Software, 63(17):1-50, http://www.jstatsoft.org/v63/i17/
Lu B, Harris P, Charlton M, Brunsdon C (2014) The GWmodel R Package: further topics for exploring Spatial Heterogeneity using Geographically Weighted Models. Geo-spatial Information Science 17(2): 85-101, http://www.tandfonline.com/doi/abs/10.1080/10095020.2014.917453
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