The OLS default output is a map showing us how well the model performed, using only the
population variable to explain 911 call volumes. The red areas are under predictions (where the
actual number of calls is higher than the model predicted); the blue areas are over predictions (actual
call volumes are lower than predicted). When a model is performing well, the over/under predictions
reflect random noise… the model is a little high here, but a little low there… you don’t see any
structure at all in the over/under predictions. Do the over and under predictions in the output
feature class appear to be random noise or do you see clustering? When the over (blue) and under
(red) predictions cluster together spatially, you know that your model is missing one or more key
explanatory variables.