I am looking to divide the 50 US states into
n regions. The requirements in dividing are:
- Each state will be assigned a value
- The state values in each region should sum to make even group totals (as closely as possible). Which seems to make this a bin packing problem variation.
- The states in each region need to be geographically clustered, For example, CA+OR+WA should be clustered even though CA+GA+RI produces a smaller standard deviation of regional value totals.
This post asks a similar question. K-Means clustering seems overkill as states just need to be neighbors, however I am pretty green to stats.
As a side-note, I am ultimately looking to implement this in Ruby (which has a R library plugin).
The motivation behind the clustering is for ease of travel, therefore cluster compactness is more important than state adjacency (i.e. long, narrow, string-shaped clusters should be avoided).