The Centre for Multilevel Modelling at the University of Bristol are hosting our forthcoming Geographically Weighted Modelling Workshop. The two day workshop will run on 23rd and 24th April 2014, and will be lead by Professor Chris Brunsdon, Dr Paul Harris and Martin Charlton. The workshop will be based around our GWmodel R package, and will cover geographically weighted versions of summary statistics, principal components analysis, and regression. Workshop attendees will need some familiarity with basic statistical concepts and techniques. While familiarity with the R statistical and visualisation software will be useful, it is not essential.
Places are limited; registration, which is essential, will close on 1st April 2014. Further details are to be found at the workshop page.
We have recently completed a couple of useful vignettes which should answer many of the questions we have had concerning the use of the GWmodel functions. Have a look at:
Gollini I, Lu B, Charlton M, Brunsdon C, Harris P (2013) GWmodel: an R Package for exploring Spatial Heterogeneity using Geographically Weighted Models, http://arxiv.org/abs/1306.0413
Lu B, Harris P, Charlton M, Brunsdon C (2013) The GWmodel R Package: further topics for exploring Spatial Heterogeneity using Geographically Weighted Models, http://arxiv-web3.library.cornell.edu/abs/1312.2753
to see if your question is covered.
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
This workshop encompasses all forms of Geographically Weighted (GW) modelling, providing hands-on exercises such that participants can apply the GW techniques themselves. The workshop will also introduce the new GWmodel R and python packages. The workshop and associated software development is part of the StratAG research programme, which is funded by Science Foundation Ireland.
For more information regarding workshop objectives, registration, and contacts please download the GWmodel_flyer_April_v2.pdf below.
GWR RC1 Software Download
GWR4_64.exe File size: 31.52MB
Both 32 and 64 bit versions of GWR4 are now available.
GWR4 Recommended System Requirements
Windows Vista or higher
Pentium 4 processor at 1.8GHz or higher
2MB available in the hard disk
The GWR4 user manual is available for download GWR4_Manual.pdf.
For user testing a sample data-set is also available for download in zip file format.
Runtime Scalability Matrices:
This information is provided as a guide to the user when setting up and running large GWR4 models. The Runtimes were collected using GWR4 RC1.