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I have a data set of 200k observations of about 75 variables representing people in about 60 distinct jurisdictions. I am trying to figure out which jurisdictions are most similar to each other on the basis of say 4-10 different demographic variables (all continuous). Essentially, what I am trying to do is pick two jurisdictions that are as similar as possible in one but very different with regard to a specific variable (per-child funding in a specific grant). Once I figure out which districts are most similar, I can just check the pairs to see which are disparate in funding.

I checked this post, but it doesn't seem to apply.

How can I quantitatively determine just how similar two groups are? Bonus if there's a scikit-learn library that handles it...

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  • $\begingroup$ Any single similarity score would be some sort of aggregation/comparison between the observation pairs. scikit-learn has several Pairwise metrics. $\endgroup$
    – rickhg12hs
    Nov 9, 2022 at 3:55
  • $\begingroup$ Thanks @rickhg. Would pairwise options work if the districts do not contain the same number of observations? $\endgroup$
    – pubb
    Nov 9, 2022 at 23:33
  • $\begingroup$ I believe most, if not all, of the scikit-learn pairwise metrics require equal number of observations. $\endgroup$
    – rickhg12hs
    Nov 10, 2022 at 0:26

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