Aggregating community maps
Aggregating community maps
复制标题
DOI：
10.1145/3557915.3560961
复制
发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
E. Chambers;M. Duchin;Ranthony A. C. Edmonds;Parker B. Edwards;JN Matthews;Anthony E. Pizzimenti;Chanel Richardson;Parker Rule;Ari Stern
中科院分区：
文献类型：
--
作者：
E. Chambers;M. Duchin;Ranthony A. C. Edmonds;Parker B. Edwards;JN Matthews;Anthony E. Pizzimenti;Chanel Richardson;Parker Rule;Ari Stern
本文的动机是一个实际问题：作为选区重新划分过程的一部分，美国许多州都举行了关于“利益社区”的公开听证会，但目前还没有一个州采用空间化和聚合社区地图的具体方法，以便在绘制选区新边界时将其考虑在内。下面，我们描述了一个为期一年的项目，该项目通过与基层组织和/或政府办公室的合作收集并合成了数千张社区地图。然后通过地理聚类和修改后的豪斯多夫距离对提交的内容进行汇总；然后，使用语义标签对叙述性提交的文本进行分类，以便可以使用马尔可夫链的短期运行来形成语义子簇。生成的数据集是公开的，包括提交的社区地图的原始数据以及经过后处理的社区集群以及用于衡量分区计划尊重集群程度的评分系统。我们讨论了这种方法的优点和缺点，并提出了未来工作的建议方向。
This paper is motivated by a practical problem: many U.S. states have public hearings on "communities of interest" as part of their redistricting process, but no state has as yet adopted a concrete method of spatializing and aggregating community maps in order to take them into account in the drawing of new boundaries for electoral districts. Below, we describe a year-long project that collected and synthesized thousands of community maps through partnerships with grassroots organizations and/or government offices. The submissions were then aggregated by geographical clustering with a modified Hausdorff distance; then, the text from the narrative submissions was classified with semantic labels so that short runs of a Markov chain could be used to form semantic sub-clusters. The resulting dataset is publicly available, including the raw data of submitted community maps as well as post-processed community clusters and a scoring system for measuring how well districting plans respect the clusters. We provide a discussion of the strengths and weaknesses of this methodology and conclude with proposed directions for future work.