I provide statistical support for a public health department. As you might imagine, we put together a lot of maps on a regular basis. For me, maps are just another kind of data visualization - useful for getting a feel for the data, for generating and checking hypotheses, etc. But we don't often follow through on actual modeling and hypothesis testing.
How do you/your organization go about this? What does a workflow that includes inference look like? Who's involved? What tools do you use? What would it ideally look like, if you had your way?
To be clear, I'm curious about different strategies for going from spatial data to formal, statistical tests of hypotheses about what's going on in the world. For example, let's say I'm trying to target an educational campaign to increase tuberculosis testing. I (personally) would map out the cases of TB against covariates of interest (say, median income or percent foreign-born residents) and try to see if there were any patterns.
I might or might not find any; but I would ultimately build a model to estimate the association between those covariates and the number of demographics. This is a critical step because of how good humans are at finding patterns where none exist, or finding uninteresting ones. I know how to do this on my own, but I'm curious about how different organizations institutionalize it (if at all).