The GWmodel R package:further topics for exploring spatial heterogeneity using geographically weighted models  

作　　者：Binbin LU Paul HARRIS Martin CHARLTON Chris BRUNSDON 

机构地区：[1]School of Remote Sensing and Information Engineering,Wuhan University,129 Luoyu Road,Wuhan 430079,China [2]Rothamsted Research,North Wyke,Okehampton,Devon,UK [3]National Centre for Geocomputation,National University of Ireland Maynooth,Maynooth,Co.Kildare,Ireland

出　　处：《Geo-Spatial Information Science》2014年第2期85-101,共17页地球空间信息科学学报（英文）

基　　金：presented in this paper was funded by a Strategic Research Cluster grant(07/SRC/I1168)by Science Foundation Ireland under the National Development Plan.

摘　　要：In this study,we present a collection of local models,termed geographically weighted(GW)models,which can be found within the GWmodel R package.A GW model suits situations when spatial data are poorly described by the global form,and for some regions the localized fit provides a better description.The approach uses a moving window weighting technique,where a collection of local models are estimated at target locations.Commonly,model parameters or outputs are mapped so that the nature of spatial heterogeneity can be explored and assessed.In particular,we present case studies using:(i)GW summary statistics and a GW principal components analysis;(ii)advanced GW regression fits and diagnostics;(iii)associated Monte Carlo significance tests for non-stationarity;(iv)a GW discriminant analysis;and(v)enhanced kernel bandwidth selection procedures.General Election data-sets from the Republic of Ireland and US are used for demonstration.This study is designed to complement a companion GWmodel study,which focuses on basic and robust GW models.

关 键 词：principal components analysis semi-parametric GW regression discriminant analysis Monte Carlo tests election data 

分 类 号：O17[理学—数学]