Lab 12 – Geographically Weighted Regression

Spatial regression can be used in GIS to model a phenomenon of interest.  In non-spatial regression analysis, spatial auto-correlation is generally undesirable.  Spatial regression attempts to quantify auto-correlation and use it as an explanatory variable.  Geographic Weighted Regression (GWR) is a specific spatial regression used to account for multicolinearity.  In the lab this week we compared GWR to OLS regression.  The model output from GWR regression was better (i.e. had a lower AICc) than the OLS model.  Further the z-score was lower, indicating that there was spatial dependence in the phenomenon of interest.