Paul Harris and Chris Brunsdon ran a short workshop at the 7th Channel Network Conference on July 10th 2019 which was hosted at Rothamsted.
Spatial statistics is an ever-expanding discipline providing analytical techniques for a wide range of disciplines in the natural and social/economic sciences. In this workshop, we’ll outline 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 localised calibration provides a better description. The approach is moving window based, where localised models are found at target locations calibrated with weighted data subsets. Outputs are mapped and spatially-interrogated to provide insight into the nature of the data’s spatial heterogeneity. Core GW techniques include: GW summary statistics, GW principal components analysis, GW regression, GW generalised linear models and GW discriminant analysis (Gollini et al. 2015; JSS 63(17):1:50). This workshop will focus on GW regression, illustrated with applications in agriculture.
At the beginning of December we uploaded an updated version of GWmodel to CRAN. This is version 2.0-1. While there have been some minor cosmetic changes the major improvement is that some of the more computationally intensive functions have been recoded in C++. This means that many GWmodel functions will now run noticeably faster than they did in previous versions. This is now the version that is installed by the install.packages() and update.packages() functions.
There have been two GW modelling workshops in the first 6 months of 2016.
The first took place at the University of Sheffield on March 22nd and 23rd. This Advanced Spatial Analysis workshop was organised by the Applied Quantitative Methods Network, and entitled “Modelling Spatial Heterogeneity with Geographically Weighted Models using R”. The course was run by Chris Brunsdon, Paul Harris and Martin Charlton. We covered geographically weighted summary statistics, geographically weighted regression, geographically weighted principal components analysis, and further issues in spatial models, including dealing collinear data using locally compensated models.
The second was hosted by the International Institute for Applied Systems Analysis (IIASA) at the Schloß Laxenburg, |Austria, on May 24th and 25th. We covered some fairly advanced material, including geographically weighted principal component analysis, REML models, GW discriminant analysis, and GW contingency tables, and some issues in spatial indexing to deal with big data. The workshop was run by Paul Harris, Chris Brunsdon and Martin Charlton, with enthusiastic participation from researchers at IIASA. Each of us probably ate our own body weight in Wiener Schnitzeln… We stayed in the Hotel Prinz Eugen, which is a short walk to the bus station at Wien Hbf, for the 30 minutes ride on the No 200 bus to Laxenburg.