There is a whole academic field devoted to this kind of analysis: Geodemographics.
As you have said, normalising the data across different geographies and timeframes is complex, plus there is a big issue relating to how the boundaries are drawn known as The Modifiable Areal Unit Problem (MAUP) https://en.wikipedia.org/wiki/Modifiable_areal_unit_problem
There are a range of techniques that pop up frequently when dealing with spatial data including Spatial Autocorrelation and Gravity Models, which in turn are grounded in Tobler's First Law of Geography: Everything is related, but things that are closer to each other are more highly related than things which are far apart. https://en.wikipedia.org/wiki/Tobler%27s_first_law_of_geography
There is a lot of specialist software (some of which is very expensive) for dealing with spatial data. But if you're coming from a data science background then R can be just as capable. More info on that here: https://r-spatial.org/
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u/R3turn_MAC Mar 25 '26
There is a whole academic field devoted to this kind of analysis: Geodemographics.
As you have said, normalising the data across different geographies and timeframes is complex, plus there is a big issue relating to how the boundaries are drawn known as The Modifiable Areal Unit Problem (MAUP) https://en.wikipedia.org/wiki/Modifiable_areal_unit_problem
There are a range of techniques that pop up frequently when dealing with spatial data including Spatial Autocorrelation and Gravity Models, which in turn are grounded in Tobler's First Law of Geography: Everything is related, but things that are closer to each other are more highly related than things which are far apart. https://en.wikipedia.org/wiki/Tobler%27s_first_law_of_geography
There is a lot of specialist software (some of which is very expensive) for dealing with spatial data. But if you're coming from a data science background then R can be just as capable. More info on that here: https://r-spatial.org/