Apportionment and districting by sum of ranking differences

Sum of Ranking Differences is an innovative statistical method that ranks competing solutions based on a reference point. The latter might arise naturally, or can be aggregated from the data. We provide two case studies to feature both possibilities. Apportionment and districting are two critical issues that emerge in relation to democratic elections. Theoreticians invented clever heuristics to measure malapportionment and the compactness of the shape of the constituencies, yet, there is no unique best method in either cases. Using data from Norway and the US we rank the standard methods both for the apportionment and for the districting problem. In case of apportionment, we find that all the classical methods perform reasonably well, with subtle but significant differences. By a small margin the Leximin method emerges as a winner, but - somewhat unexpectedly - the nonregular Imperiali method ties for first place. In districting, the Lee-Sallee index and a novel parametric method the so-called Mo ent Invariant performs the best, although the latter is sensitive to the function's chosen parameter.