GWmodel-package: Geographically-Weighted Models
In GWmodel: Geographically-Weighted Models

GWmodel-package

R Documentation

Geographically-Weighted Models

Description

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.

Details

Package:	GWmodel
Type:	Package
Version:	2.4-1
Date:	2024-09-06
License:	GPL (>=2)
LazyLoad:	yes

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.

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

Author(s)

Binbin Lu, Paul Harris, Martin Charlton, Chris Brunsdon, Tomoki Nakaya, Daisuke Murakami,Isabella Gollini[ctb], Yigong Hu[ctb], Fiona H Evans[ctb]

Maintainer: Binbin Lu <binbinlu@whu.edu.cn>

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, \Sexpr[results=rd]{tools:::Rd_expr_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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10095020.2014.917453")}

Lu, B., Hu, Y., Yang, D., Liu, Y., Ou, G., Harris, P., Brunsdon, C., Comber, A., Dong, G., 2024. Gwmodels: A standalone software to train geographically weighted models. Geo-spatial Information Science, 1-23.

Lu, B., Hu, Y., Murakami, D., Brunsdon, C., Comber, A., Charlton, M., Harris, P., 2022. High-performance solutions of geographically weighted regression in r. Geo-spatial Information Science 25 (4), 536-549.

GWmodel documentation built on Sept. 11, 2024, 9:09 p.m.