TY - JOUR
T1 - GWmodel
T2 - An R package for exploring spatial heterogeneity using geographically weighted models
AU - Gollini, Isabella
AU - Lu, Binbin
AU - Charlton, Martin
AU - Brunsdon, Christopher
AU - Harris, Paul
PY - 2015/2
Y1 - 2015/2
N2 - 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 GW model, 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, GW model 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.
AB - 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 GW model, 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, GW model 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.
KW - Geographically weighted principal components analysis
KW - Geographically weighted regression
KW - R package
KW - Robust
KW - Spatial prediction
UR - http://www.scopus.com/inward/record.url?scp=84922560278&partnerID=8YFLogxK
U2 - 10.18637/jss.v063.i17
DO - 10.18637/jss.v063.i17
M3 - Article (Academic Journal)
AN - SCOPUS:84922560278
SN - 1548-7660
VL - 63
JO - Journal of Statistical Software
JF - Journal of Statistical Software
IS - 17
ER -