I have some data gathered from a survey conducted within my city. All responses include an approximate geo location of where they were gathered (accurate to probably a couple of hundred yards which is relatively small), and things like the respondents age, sex, income range, number of dependents, etc. There are approx. 4000 responses.
What I would like to is to be able to generate what I guess you would call a model, so that given a geo point (or box) I could characterize the typical respondent from there (it doesn't really have to be really rigorous, although some kind of formal confidence measurement would be nice).
So, is the right thing to do to simply treat all the gathered attributes separately and say "Well the age of your typical respondent in that area is m with stdev s, and their income range is ..., etc."
Or is there some better way to analyse the data together to get a better profile of the respondents.
Some key phrases to google would even help at this stage, because I'm a bit lost. I thought this might be "data fusion" but I don't think it is.