I ardently disagree with the assertion that "Comparing the GWR AICc value to the OLS AICc value is one way to assess the benefits of moving from a global model (OLS) to a local regression model (GWR)". AIC assumes nested model variances in order to compare competing models. Because of non-nested variances, there is a controversy in the literature regarding appropriate use of AIC in spatial models. Regardless, YOU CANNOT COMPARE A GWR AND OLS MODEL USING AIC. AIC is intended for hypothesis testing of competing models and not data mining. A competing model would not be considered OLS vs. GWR, but rather different sub-sets of variables. Given the mathematical differences between GWR and OLS there is not unification between the models, making AIC scores non-comparable.
The asymptotic justification of AIC requires two strong assumptions:
(1) that the true model is contained in the candidate class under consideration,
(2) that the vector of maximum likelihood estimators satisfies the conventional large-sample properties of maximum likelihood.
Because of the above assumptions, AICc was developed as a modification that accounts for small sample sizes. The reason GWR uses AICc is because it is a local regression that iteratively fits small numbers of observations within a specified bandwidth. Unless you have small sample issues, AICc in an OLS is not appropriate. Some exploratory data analysis (Box-whisker and X,Y plots, Moran's-I for global autocorrelation, LISA for non-stationary, etc...) should provide insight to the appropriate modeling approach and verify that your data is suitable for a spatial model like GWR. If there is no autocorrelation (1st or 2nd order) in your data then OLS is quite well specified.