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GWmodel (version 2.2-8)

Geographically-Weighted Models

Description

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 (Brunsdon et al., 2002), GW principal components analysis (Harris et al., 2011), GW discriminant analysis (Brunsdon et al., 2007) and various forms of GW regression (Brunsdon et al., 1996); some of which are provided in basic and robust (outlier resistant) forms.

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Version

Install

install.packages('GWmodel')

Monthly Downloads

11,847

Version

2.2-8

License

GPL (>= 2)

Maintainer

Binbin Lu

Last Published

October 9th, 2021

Functions in GWmodel (2.2-8)

USelect

Results of the 2004 US presidential election at the county level (SpatialPolygonsDataFrame)
bw.ggwr

Bandwidth selection for generalised geographically weighted regression (GWR)
GWmodel-package

Geographically-Weighted Models
LondonBorough

London boroughs data
DubVoter

Voter turnout data in Greater Dublin(SpatialPolygonsDataFrame)
EWOutline

LondonHP

London house price data set (SpatialPointsDataFrame)
Georgia

Georgia census data set (csv file)
GeorgiaCounties

Georgia counties data (SpatialPolygonsDataFrame)
EWHP

House price data set (DataFrame) in England and Wales
ggwr.cv.contrib

Cross-validation data at each observation location for a generalised GWR model
ggwr.basic

Generalised GWR models with Poisson and Binomial options
gwpca.glyph.plot

Multivariate glyph plots of GWPCA loadings
bw.gwr.lcr

Bandwidth selection for locally compensated ridge GWR (GWR-LCR)
bw.gtwr

Bandwidth selection for GTWR
bw.gwda

Bandwidth selection for GW Discriminant Analysis
gwpca.cv.contrib

Cross-validation data at each observation location for a GWPCA
bw.gwpca

Bandwidth selection for Geographically Weighted Principal Components Analysis (GWPCA)
ggwr.cv

Cross-validation score for a specified bandwidth for generalised GWR
bw.gwr

Bandwidth selection for basic GWR
gwpca.montecarlo.1

Monte Carlo (randomisation) test for significance of GWPCA eigenvalue variability for the first component only - option 1
gwr.lcr.cv

Cross-validation score for a specified bandwidth for GWR-LCR model
gwpca.cv

Cross-validation score for a specified bandwidth for GWPCA
bw.gwss.average

Bandwidth selection for GW summary averages
gtwr

Geographically and Temporally Weighted Regression
gwpca

GWPCA
gwr.mink.approach

Minkovski approach for GWR
gwss

Geographically weighted summary statistics (GWSS)
gwr.collin.diagno

Local collinearity diagnostics for basic GWR
gwr.mink.matrixview

gwss.montecarlo

gwpca.check.components

Interaction tool with the GWPCA glyph map
gwr.bootstrap

Bootstrap GWR
gw.pcplot

Geographically weighted parallel coordinate plot for investigating multivariate data sets
gwr.hetero

Heteroskedastic GWR
gw.dist

Distance matrix calculation
gwr.multiscale

Multiscale GWR
gwr.predict

GWR used as a spatial predictor
gwpca.montecarlo.2

Monte Carlo (randomisation) test for significance of GWPCA eigenvalue variability for the first component only - option 2
gwr.scalable

Scalable GWR
gwr.robust

Robust GWR model
st.dist

Spatio-temporal distance matrix calculation
gwr.lcr

GWR with a locally-compensated ridge term
gwr.lcr.cv.contrib

Cross-validation data at each observation location for the GWR-LCR model
gwr.model.view

gwr.montecarlo

Monte Carlo (randomisation) test for significance of GWR parameter variability
gwr.model.selection

Model selection for GWR with a given set of independent variables
gwr.t.adjust

Adjust p-values for multiple hypothesis tests in basic GWR
gwr.model.sort

gwr.basic

Basic GWR model
gwr.write

Write the GWR results into files
gw.weight

Weight matrix calculation
gwr.cv

Cross-validation score for a specified bandwidth for basic GWR
gwda

GW Discriminant Analysis
gwr.mixed

Mixed GWR
gwr.mink.pval

Select the values of p for the Minkowski approach for GWR
gwr.cv.contrib

Cross-validation data at each observation location for a basic GWR model