Recent Workshop: Investigating Spatial Heterogeneity with Geographically Weighted Models

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.