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- W2273670357 abstract "In the very early developments of quantitative geography, statistical techniques wereinvariably applied at a ‘global’ level, where moments or relationships were assumed constantacross 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 fordifferent forms of spatial heterogeneity or non-stationarity (Goodchild, 2004). Consequently,a number of local methods were developed, many of which model non- stationarityrelationships 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 (Assuncao, 2003).One such localised regression, geographically weighted regression (GWR) (Brunsdon etal., 1996) has become increasingly popular and has been broadly applied in many disciplinesoutside of its quantitative geography roots. This includes: regional economics, urban andregional analysis, sociology and ecology. There are several toolkits available for applyingGWR, such as GWR3.x (Charlton et al., 2007); GWR 4.0 (Nakaya et al., 2009); the GWRtoolkit 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 GWRor 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 forhandling 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, whichis also mirrored with a set of GW modelling tools for ESRI’s ArcGIS written in Python." @default.
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- W2273670357 date "2013-01-01" @default.
- W2273670357 modified "2023-09-27" @default.
- W2273670357 title "Introducing the GWmodel R and python packages for modelling spatial heterogeneity" @default.
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