Suppose I'm trying to figure out the relationship between home-ownership and wine drinking.
My information on where home-owners live comes from the census, and it divides a given geographical area up into zones like this:
This (made up) data is organized like this:
Census Zone | Home-ownership rate ____________|______________________ 1 | 45% 2 | 49% 3 | 63% etc | etc
(it also tells me things like total population and population density)
I get my data on wine consumption from The International Alkie Confederation, and their data looks like this:
Wine Zone | Wine drunk per capita per year in litres ____________|____________________________________________ A | 8 B | 10 C | 11 D | 0 E | 3 etc | etc
So what's the best way of combining these two datasets? For example, can I look at how each wine-zone intersects with each census-zone, and take a weighted average based on how much area they overlap? (Let's assume I have all the shapefiles for each subdivision so I can do this sort of procedure)
e.g. Wine-zone C might be something like 60% census-zone 2, and 20% each of CZ-1 and CZ-3, resulting in a weighted average of 51% home-ownership for that wine-zone.
Resulting in a table like this:
Wine Zone | Wine drunk | Home-ownership (estimate) ____________|_______________|_____________________________________ A | 8 | 39% B | 10 | 49% C | 11 | 51% D | 0 | 43% E | 3 | 52% etc | etc | etc
Obviously this wouldn't be completely accurate, because it would assume that each census area has a homogeneous population distribution (e.g. the small slice of CZ-1 might not be a representative sample of all of CZ-1).
But still, if I did this across the entire dataset, would all of these errors "wash out" and give me a meaningful comparison? Also, how can I estimate the error in doing this? Is there a name for this kind of analysis where I can google search for more information?