https://github.com/princetonuniversity/gerrymandertests

Lots of metrics for quantifying gerrymandering.
https://github.com/princetonuniversity/gerrymandertests

gerrymandering politics redistricting

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Lots of metrics for quantifying gerrymandering.

• Host: GitHub
• URL: https://github.com/princetonuniversity/gerrymandertests
• Owner: PrincetonUniversity
• License: gpl-3.0
• Created: 2018-02-06T19:23:15.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2023-02-20T14:54:03.000Z (almost 2 years ago)
• Last Synced: 2024-10-08T03:44:23.598Z (3 months ago)
• Topics: gerrymandering, politics, redistricting
• Language: Python
• Homepage:
• Size: 1.18 MB
• Stars: 25
• Watchers: 15
• Forks: 9
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Metrics for quantifying gerrymandering

[![PyPI version](https://badge.fury.io/py/gerrymetrics.svg)](https://badge.fury.io/py/gerrymetrics)

This repository contains:

1. [Python code](metrics.py) for implementing a number of metrics for quantifying gerrymandering9:

    - Mean-median difference and variant:

       - Mean-median difference1,2

       - Equal vote weight2

    - Lopsided margins (two-sample _t_-test on win margins)1

    - Bootstrap (Monte Carlo) simulation1

    - Mann-Whitney _U_ test

    - Declination variants3

       - Declination

       - Declination (buffered)

       - Declination variant

       - Declination variant (buffered)

    - Efficiency gap variants

       - Efficiency gap4

       - Difference gap5,6,7

       - Loss gap7

       - Surplus gap8

       - Vote-centric gap6,7

       - Vote-centric gap 26,7

       - Tau gap3

    - Partisan bias6,7

2. Historical election results:

    - Congressional elections, 1948–2016 ([CSV](election_data/congressional_election_results_post1948.csv))

    - State legislative elections (lower house), 1971–2017 ([CSV](election_data/state_legislative/state_legislative_election_results_post1971.csv), [full repository](https://github.com/PrincetonUniversity/historic_state_legislative_election_results))

3. [Jupyter notebook](run_gerrymandering_metrics.ipynb) demonstrating how to run the tests on all elections, as well as reporting the percentile ranking for all tests of any particular election.

## Installation

If using pip, do `pip install gerrymetrics`

## References

1. Samuel S.-H. Wang. (2016). [Three Tests for Practical Evaluation of Partisan Gerrymandering.](https://www.stanfordlawreview.org/print/article/three-tests-for-practical-evaluation-of-partisan-gerrymandering/) _Stanford Law Review_.

2. Michael D. McDonald and Robin E. Best. (2015). [Unfair Partisan Gerrymanders in Politics and Law: A Diagnostic Applied to Six Cases.](https://www.liebertpub.com/doi/abs/10.1089/elj.2015.0358) _Election Law Journal_.

3. Gregory S. Warrington. (2018). [Quantifying Gerrymandering Using the Vote Distribution.](https://www.liebertpub.com/doi/abs/10.1089/elj.2017.0447) _Election Law Journal_.

4. Eric McGhee. (2014). [Measuring Partisan Bias in Single‐Member District Electoral Systems.](https://onlinelibrary.wiley.com/doi/abs/10.1111/lsq.12033) _Legislative Studies Quarterly_.

5. _Whitford v. Gill_, No. 15-cv-421, F. Supp. 3d. (2016). [Griesbach, dissenting, 128.](https://www.leagle.com/decision/infdco20161122f51)

6. Benjamin P. Cover. (2018). [Quantifying Partisan Gerrymandering: An Evaluation of the Efficiency Gap Proposal](https://www.stanfordlawreview.org/print/article/quantifying-partisan-gerrymandering/). _Stanford Law Review_.

7. John F. Nagle. (2017). [How Competitive Should a Fair Single Member Districting Plan Be?](https://www.liebertpub.com/doi/full/10.1089/elj.2016.0386) _Election Law Journal_.

8. Wendy K. Tam Cho. (2018). [Measuring Partisan Fairness: How Well Does the Efficiency Gap Guard Against Sophisticated as well as Simple-Minded Modes of Partisan Discrimination?](https://scholarship.law.upenn.edu/penn_law_review_online/vol166/iss1/2/) _University of Pennsylvania Law

Review_.

9. Gregory S. Warrington. (2018). [A Comparison of Gerrymandering Metrics.](https://arxiv.org/abs/1805.12572) _arXiv_.