Data Science Asked by bashity on January 3, 2021
I have a relatively straightforward question that I know poses some difficult challenges.
Let’s say I have a state-level rate of X. I would like to disaggregate the state-level rate to the county-level. I realize this is can be dangerous (ecological fallacy), but I have seen some studies use the technique with a set of assumptions.
For example, if I know that each county is a certain proportion of the entire state population, I could take that proportion and multiply it by the state-level rate of X to get an (incredibly) naive county-level rate of X.
I’m trying to find more information on ways to make this approach ‘less’ naive, but I can’t seem to get any momentum. I’ve tried using the terms ‘disaggregating’ and ‘weights’, but I can’t seem to tap into the right body of literature.
Does anyone know of any methods/body of work that have attempted to handle this problem?
From a statistical point of view this is impossible if one doesn't have any data at the fine-grained level. Any statistical inference must be based on a sample from which specific patterns can be observed.
If there is no data at the fine-grained level, any calculation is based on assumptions. For example one may assume that a variable is proportional to population (linear relation). But why not assume that the variable is a polynomial function of the temperature? Or that it is related to the prevalence of a particular gene? The main issue is that without any data there's no way to test any of these assumptions, so no there can be not reliable conclusion.
Correct answer by Erwan on January 3, 2021
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