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GWmodel3: Modelling Spatial Data with Geographically Weighted Models

Source: R/GWmodel3-package.R
GWmodel3-package.Rd

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. 'GWmodel3' includes: GW summary statistics (Brunsdon et al., 2002)doi:10.1016/s0198-9715(01)00009-6 , GW principal components analysis (Harris et al., 2011)doi:10.1080/13658816.2011.554838 , GW discriminant analysis (Brunsdon et al., 2007)doi:10.1111/j.1538-4632.2007.00709.x and various forms of GW regression (Brunsdon et al., 1996)doi:10.1111/j.1538-4632.1996.tb00936.x ; some of which are provided in basic and robust (outlier resistant) forms.

Details

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. In particular, the high-performence computing technologies, including multi-thread and CUDA techniques are started to be adopted for efficient calibrations.

Note

Acknowledgements: We gratefully acknowledge support from National Natural Science Foundation of China (42071368); Science Foundation Ireland under the National Development Plan through the award of a Strategic Research Centre grant 07-SRC-I1168.

For latest tutorials on using GWmodel please go to: https://rpubs.com/gwmodel

References

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, doi:10.18637/jss.v063.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, doi:10.1080/10095020.2014.917453

See also

Useful links:

Author

Maintainer: Binbin Lu binbinlu@whu.edu.cn (ORCID)

Authors:

Other contributors:

  • Haotian Zhang [contributor]

  • Guangyu Ou [contributor]

  • Paul Harris [contributor]

  • Martin Charlton [contributor]

  • Chris Brunsdon [contributor]

  • Tomoki Nakaya [contributor]

  • Daisuke Murakami [contributor]

  • Isabella Gollini [contributor]

  • Fiona H Evans [contributor]