Our new R package for Geographically Weighted Modelling, GWmodel, was recently uploaded to CRAN. GWmodel provides range of Geographically Weighted data analysis approaches within a single package, these include descriptive statistics, correlation, regression, general linear models and principal components analysis. The regression models include various for data with Gaussian, logistic and Poisson structures, as well as ridge regression for dealing with correlated predictors. A new feature of this package is the provision of robust versions of each technique – these are resistant to the effects of outliers.
Locations for modelling can be either in a projected coordinate system, or specified using geographical coordinates. Distance metrics include Euclidean, taxicab (Manhattan) and Minkowski, as well as Great Circle distances for locations specified by latitude/longitude coordinates. Various automatic calibration methods are also provided, and there are some helpful model building tools available to help select from alternative predictors.
Example datasets are also provided, and they are used in the accompanying documentation in illustrations of the use of the various techniques.
The documentation, with a description of the various functions, can be accessed at http://cran.r-project.org/web/packages/GWmodel/GWmodel.pdf.
We are currently working on another implementation of these functions as an ArcGIS Toolbox.
Martin Chartlton and Paul Harris
June 24th 2013