Compute Dave's Redistricting (DRA) analytics for an ensemble of redistricting plans.
To clone the repository:
$ git clone https://github.com/alecramsay/rdascore
$ cd rdascore
To install the package:
$ pip install rdascore
As noted next, you probably also want to clone the companion rdabase repository.
There are both code and script examples of how to use this code in the sample
directory.
That directory also contains some sample results from the main scoring function analyze_plan()
.
The samples use data from the companion rdabase
repo.
With four exceptions, analyze_plan()
computes all the analytics that DRA does:
- For a variety of reasons, DRA's production TypeScript package dra-analytics does not calculate a few minor things that show up in the UI. The Python port rdapy does not either. This repo uses the latter, so those few things also aren't in the "scorecard" output.
- To keep the results simple, district-level results are suppressed. The scorecard is a simple flat dictionary of metric key/value pairs.
- To maximize throughput KIWYSI compactness is not calculated. The simple naive approach to performing
compactness calculations is to dissolve precinct shapes into district shapes, but dissolve is very
expensive operation. Analyzing a congressional plan for North Carolina take ~60 seconds. A much
faster approach is to convert precinct shapes into topologies using TopoJSON like DRA does and then
merging precincts into district shapes. That approach takes ~5 seconds, virtually all of the time
being calling TopoJSON
merge()
from Python and marshalling the result back from JavaScript. I could have chosen to implement a Python native version ofmerge()
. Instead, I chose to skip KIWYSI compactness (which requires actual district shapes) and just calculate the two main compactness metrics in DRA: Reock and Polsby-Popper. Together these only depend on district area, perimeter, and diameter, and with some pre-processing once per state (analogous to converting shapes into a topology) these values can be imputed without ever creating the district shapes. The result is that analyzing a congressional plan for North Carolina — calculating all the analytics — takes a small fraction of a second. - Finally, we've already created the precinct contiguity graphs as part of finding root map candidates in our rdaroot GitHub repo, and, by definition, the plans in our ensembles are contiguous. Hence, we don't check that.
$ pytest --disable-warnings