Working on specifying my GWR model and doing my OLS work found, not surprisingly, that "income", and other variables like "population density", and "call_rate" needed transformation. So I used a normal log transformation and computed new field values for each and created "log_income" and "log_pop_density" and "log_call_rate". Before, running the OLS, everything seemed fine. But now, when running GWR, the analysis craps out with the ole...

[INDENT]ERROR 040038: Results cannot be computed because of severe model design problems.

Failed to execute (GeographicallyWeightedRegression).

[/INDENT]

So I narrowed it down to the "log_income" and "log_pop_density" variables - whenever the "log_income" is included, either as independent or dependent variable, the model completely tanks. For the "log_pop_den" variable only when it's a dependent variable does the model tank. Looking at the distribution graphically (attached) for income it seems OK. Perhaps this isn't the correct (best?) transformation though?

Methodologically I'm working on an city wide grid, measuring about 2000 x 2000 polygons high and wide, each measuring 250' x 250', where almost every one has data within it. I've set bandwidths to extremes, used minimum # of neighbors, AICc, etc. - every iteration possible - and can't seem to get it to fly. Clearly my "transformation" is the problem but not sure what that problem is.

Much thanks for anyone's eyes on this.

[INDENT]ERROR 040038: Results cannot be computed because of severe model design problems.

Failed to execute (GeographicallyWeightedRegression).

[/INDENT]

So I narrowed it down to the "log_income" and "log_pop_density" variables - whenever the "log_income" is included, either as independent or dependent variable, the model completely tanks. For the "log_pop_den" variable only when it's a dependent variable does the model tank. Looking at the distribution graphically (attached) for income it seems OK. Perhaps this isn't the correct (best?) transformation though?

Methodologically I'm working on an city wide grid, measuring about 2000 x 2000 polygons high and wide, each measuring 250' x 250', where almost every one has data within it. I've set bandwidths to extremes, used minimum # of neighbors, AICc, etc. - every iteration possible - and can't seem to get it to fly. Clearly my "transformation" is the problem but not sure what that problem is.

Much thanks for anyone's eyes on this.

I'm sorry you're having trouble getting GWR to run for your analysis. Severe model design errors often indicate a problem with global or local multicollinearity. To determine where the problem is, run the model using OLS and examine the VIF value for each explanatory variable. If some of the VIF values are large (above 7.5, for example), global multicollinearity is preventing GWR from solving. More likely, however, local multicollinearity is the problem. Try creating a thematic map for each explanatory variable. If the map reveals spatial clustering of identical values, consider combining those variables with other explanatory variables to increase value variation. If, for example, you are modeling home values and have variables for both bedrooms and bathrooms, you may want to combine these to increase value variation or represent them as bathroom/bedroom square footage.

Another option is to try transforming it (although not in the traditional sense of logs or powers): create a new field, then calculate the values to be the value (in this case the log) minus the mean for all values in that field. This doesn�??t actually change anything (the impact on results), but for some reason we've found that GWR likes variables in that form�?� and this transformation will often �??fix�?� the problem.

Also, just a reminder to make sure that you find a properly specified OLS model before moving on to GWR. There is some great documentation about this, including this recent ArcUser article on Finding a Meaningful Model and the training seminar on Regression Analysis Basics.