I have a number of geographically dispersed stores where I would like to understand what factors predict revenue. Each store has certain properties (sq ft, age, employees, etc.). I also have data for the area surrounding the store by zip code (population, industry spend, competition, etc.).
I am struggling with the right way to incorporate the geographic data into my model. One potential would be to draw radii and create aggregation variables based on distance from the store. For example:
- Number of households 25km from a store
- Number of competitors 50km from a store
and then include these in a regression model. My question is:
- Is this the best technique? Are there other models better suited to do this analysis?
- Is there a way to statistically determine the distance I should use for each variable?