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