In the very early developments of quantitative geography, statistical techniques were invariably applied at a ‘global’ level, where moments or relationships were assumed constant across the study region (Fotheringham and Brunsdon, 1999). However, the world is not an “average” space but full of variations and as such, statistical techniques need to account for different forms of spatial heterogeneity or non-stationarity (Goodchild, 2004). Consequently, a number of local methods were developed, many of which model non- stationarity relationships via some regression adaptation. Examples include: the expansion method (Casetti, 1972), random coefficient modelling (Swamy et al., 1988), multilevel modelling (Duncan and Jones, 2000) and space varying parameter models (Assunção, 2003).

One such localised regression, geographically weighted regression (GWR) (Brunsdon et al., 1996) has become increasingly popular and has been broadly applied in many disciplines outside of its quantitative geography roots. This includes: regional economics, urban and regional analysis, sociology and ecology. There are several toolkits available for applying GWR, such as GWR3.x (Charlton et al., 2007); GWR 4.0 (Nakaya et al., 2009); the GWR toolkit in ArcGIS (ESRI, 2009); the R packages spgwr (Bivand and Yu, 2006) and gwrr
(Wheeler, 2011); and STIS (Arbor, 2010). Most focus on the fundamental functions of GWR or some specific issue - for example, gwrr provides tools to diagnose collinearity.

As a major extension, we report in this paper the development an integrated framework for handling spatially varying structures, via a wide range of geographically weighted (GW) models, not just GWR. All functions are included in an R package named GWmodel, which is also mirrored with a set of GW modelling tools for ESRI’s ArcGIS written in Python.