Redistricting: Drawing the Line
Redistricting: Drawing the Line
复制标题
重新划分选区：划清界限
DOI：
10.48550/arxiv.2206.00579
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Robert J. Ravier
中科院分区：
文献类型：
--
作者：
Sachet Bangia;Christy V. Graves;G. Herschlag;H. Kang;Justin Luo;Jonathan C. Mattingly;Robert J. Ravier
我们开发了评估政治地区是否准确代表人民意志的方法。为了探索和展示我们的想法，我们专注于美国众议院的国会区，并自2010年人口普查以来使用北卡罗来纳州及其重新分区。使用蒙特卡洛算法，我们随机生成了24,000多个重新划分，这些重新划分是无党派的，并符合拟议立法的标准。将历史投票数据应用于这些随机重新划分时，我们发现民主党和共和党代表的数量因绘制地区的绘制方式而异。一些结果更为普遍，我们获得了明确的预期选举结果。使用我们生成的重新划分的统计数据，我们批评了2012年和2016年NC选举中使用的特定国会区以及两党重新分配委员会提出的区域。我们发现，2012年和2016年的地区是高度非典型的，不代表人民的意志。另一方面，我们的结果表明，两党退休法官面板制定的计划是高度典型和代表性的。由于我们的分析是基于北卡罗来纳州合理重新划分的一体组合的，因此它们为给定选举提供了基准，该选举结合了该州人口分布的几何形状。
We develop methods to evaluate whether a political districting accurately represents the will of the people. To explore and showcase our ideas, we concentrate on the congressional districts for the U.S. House of representatives and use the state of North Carolina and its redistrictings since the 2010 census. Using a Monte Carlo algorithm, we randomly generate over 24,000 redistrictings that are non-partisan and adhere to criteria from proposed legislation. Applying historical voting data to these random redistrictings, we find that the number of democratic and republican representatives elected varies drastically depending on how districts are drawn. Some results are more common, and we gain a clear range of expected election outcomes. Using the statistics of our generated redistrictings, we critique the particular congressional districtings used in the 2012 and 2016 NC elections as well as a districting proposed by a bipartisan redistricting commission. We find that the 2012 and 2016 districtings are highly atypical and not representative of the will of the people. On the other hand, our results indicate that a plan produced by a bipartisan panel of retired judges is highly typical and representative. Since our analyses are based on an ensemble of reasonable redistrictings of North Carolina, they provide a baseline for a given election which incorporates the geometry of the state's population distribution.
DOI：
10.1073/pnas.1617540114
发表时间：
2017-03-14
影响因子：
11.1
作者：
Chikina, Maria;Frieze, Alan;Pegden, Wesley
通讯作者：
Pegden, Wesley