美国选区划分和全州党派选举结果。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Brian Amos, Steven Gerontakis, Michael McDonald
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引用次数: 0

摘要

我们介绍了美国 2016 年、2018 年和 2020 年 11 月大选中使用的所有选区边界数据库的创建和验证情况,以及所有党派全州办公室的选举结果。美国选举官员以称为选区的最小地理报告方式报告选举结果。学者和从业人员发现,这些选举结果对许多用例都很有价值。但是,如果没有选区界限,这些数据就无法与其他地理数据(如美国人口普查数据)一起使用。在此,我们将介绍从州和地方选举官员处收集选区边界数据的情况,这些数据有时以 GIS 格式、图像、文本描述的形式提供,在极少数情况下也会以口头形式提供。我们还介绍了如何利用其他选举数据(如地理编码选民登记文件)验证选区边界。我们的开源数据曾出现在美国最高法院审理的选区重划诉讼中,并被州和地方选区重划机构、媒体组织、权益团体、学者以及充满活力的制图爱好者社区所使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
United States Precinct Boundaries and Statewide Partisan Election Results.

We describe the creation and verification of databases of all precinct boundaries used in the United States 2016, 2018, and 2020 November general elections, enhanced with election results for all partisan statewide offices. United States election officials report election results in the smallest geographic reporting known as the precinct. Scholars and practitioners find these election results valuable for numerous use cases. However, these data cannot be augmented with other geographically-bound data, such as U.S. Census data, without precinct boundaries. Here we describe the collection of precinct boundary data from state and local election officials, sometimes provided in GIS formats, images, text descriptions, and - in rare cases - verbally. We describe how we verify boundaries with other election data, such as geocoded voter registration files. Our open-source data has appeared in redistricting litigation argued before the United States Supreme Court; and has been used by state and local redistricting authorities, media organizations, advocacy groups, scholars, and a vibrant community of mapping enthusiasts.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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