Looking for someone with knowledge of how to write, apply, and iterate on random sampling algorithms in Python and/or to generate maps using adjacency lists scraped from shape files.
The goal is to apply the algorithms to produce 100 districts for house elections in the State of Virginia via random sampling. As my colleagues have previously done, you should theoretically be able to use the algorithms above with the adjacency list you helped me get to enumerate all possible partitions and draw random maps based on that and set criteria. The set criteria that needs to be adhered to comes from here: [login to view URL]
I have attached the csv file for the adjacency list. Please reference these links to get a better understanding visually of how the adjacency list was computed: [login to view URL] | [login to view URL] | [login to view URL]
I do have some algorithms that can potentially be adjusted for this purpose. I can send them to whoever demonstrates the potential background for the task. The random sampling algorithms that were previously used on Minnesota include:
1. One that enumerates all possible partitions of a small map into districts
2. Python-igraph in order to prototype enumerating possible partitioning schemes
Each of the lines correspond to a partitioning of the Minnesota MCD's in the input files, which can then be visualized by editing.
3. If you know R, there is also a package called redist that provides the ability to use MCMC sampling. However, despite talking back and forth with the author of the package, I still can't figure out how to apply it to the adjacency list data that I have.
4. There is a gerrymandering and computational redistricting python package that might also be useful: [login to view URL]
I can also send the shape file initially used in creating the adjacency list if needed. Output should be a shape file or other geo file with 11 districts.