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

被引:0
|
作者
Gollini, Isabella [1 ]
Lu, Binbin [2 ]
Charlton, Martin [3 ]
Brunsdon, Christopher [3 ]
Harris, Paul
机构
[1] Univ Bristol, Dept Civil Engn, Bristol BS8 1TR, Avon, England
[2] Wuhan Univ, Wuhan, Peoples R China
[3] NUI Maynooth, Maynooth, Kildare, Ireland
来源
JOURNAL OF STATISTICAL SOFTWARE | 2015年 / 63卷 / 17期
基金
爱尔兰科学基金会;
关键词
geographically weighted regression; geographically weighted principal components analysis; spatial prediction; robust; R package; VARYING COEFFICIENT MODELS; REGRESSION; SELECTION; PRICES; TESTS;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:1 / 50
页数:50
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