一个数据集的美国选区投票分配到人口普查的地理位置与精确度的数据集
1College of Business, Texas A&M University-San Antonio, 1 University Way, San Antonio, Texas, 78224, USA. afekrazad@tamusa.edu.
Scientific data
|May 15, 2025
概括
这项研究将美国投票区数据与人口普查地理联系起来,以便进行详细分析. 区域土地覆盖回归 (RLCR) 方法准确地分配选票,改善选民行为研究.
科学领域:
- 地理空间分析是什么?
- 政治科学是政治学中的一个领域.
- 人口统计学 人口统计学
背景情况:
- 投票区是美国选举结果的最小单位.
- 人口普查地理 (区块组,区域,ZCTA) 提供人口统计和社会经济数据.
- 连接这些不同的空间数据集对于全面分析至关重要.
研究的目的:
- 呈现新的数据集,精确地将区级投票记录与人口普查地理位置联系起来.
- 为了使选举数据与各种行政数据集集的整合.
- 为了促进深入探索选民行为在各种背景.
主要方法:
- 开发了数据集,将美国投票区与人口普查地理联系起来.
- 采用区域土地覆盖回归 (RLCR) 方法对人口和选票的比例分配.
- 根据使用人口普查块和选民级别数据的表面面积和不透度方法验证了RLCR.
主要成果:
- 与替代方法相比,RLCR方法在多个错误指标上表现出卓越的性能.
- 经过验证的数据集准确地将2016年和2020年美国大选结果与人口普查地理联系起来.
- 创建的数据集可以将投票数据与美国社区调查和CDC Places等来源合并.
结论:
- 提出的数据集为整合选举和人口统计数据提供了一个精确和验证的方法.
- 这些资源对于研究选民行为及其决定因素的研究人员来说是有价值的.
- RLCR方法为政治和社会科学研究中的空间数据链接挑战提供了强大的解决方案.
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