References
- Alvarez, E., M. Duchin, E. Meike, and M. Mueller. 2018. Clustering propensity: A mathematical framework for measuring segregation [unpublished]. https://mggg.org/Capy.pdf
- Bangia, S., C. Graves, G. Herschlag, H. Kang, J. Mattingly, and R. Ravier. 2018. Quantifying gerrymandering in North Carolina. arXiv Preprint arXiv:1801.03783.
- Bhatia, R., and C. Davis. 2000. A better bound on the variance. The American Mathematical Monthly 107 (4):353–57. doi:10.1080/00029890.2000.12005203.
- Carter, D., G. Herschlag, Z. Hunter, and J. Mattingly. 2019. A merge-split proposal for reversible Monte Carlo Markov chain sampling of redistricting plans. arXiv Preprint arXiv: 1911.01503.
- Chen, J., A. Manne, R. Mendum, P. Sahoo, and A. Yang. 2019. Minority voter distributions and partisan gerrymandering. 2019. arXiv Preprint arXiv: 1911.09792v1.
- DeFord, D.,. M. Duchin, and J. Solomon. 2019. Recombination: A family of Markov chains for redistricting. arXiv Preprint arXiv: 1911.05725.
- Duchin, M. 2018. Gerrymandering metrics: How to measure? What’s the baseline? arXiv Preprint arXiv:1801.02064.
- Duchin, M. 2018. Outlier analysis for Pennsylvania congressional redistricting. https://www.governor.pa.gov/wp-content/uploads/2018/02/md-report.pdf
- Herschlag, G., J. Mattingly, and R. Ravier. 2017. Evaluating partisan gerrymandering in Wisconsin. arXiv Preprint arXiv:1709.01596.
- Tapp, K. 2019. Measuring political Gerrymandering. American Mathematical Monthly 126 (7):579–92.
- Veomett, E. 2018. The efficiency gap, voter turnout, and the efficiency principle. Election Law Journal 17 (4):249–63. doi:10.1089/elj.2018.0488.