经验丰富的美国种族种族多样性数据集,使用人类流动性数据
Wenfei Xu1, Zhuojun Wang2, Nada Attia3
1Cornell University College of Architecture, Art, and Planning, Ithaca, USA. wenfeixu@cornell.edu.
Scientific data
|June 17, 2024
概括
国家经验种族种族多样性 (NERD) 数据集提供了一种新的方法来衡量超越居住模式的多样性. 它使用手机数据来揭示美国各地潜在的社交互动.
科学领域:
- 社会学 社会学 社会学
- 城市研究 城市研究
- 数据科学数据科学数据科学
背景情况:
- 目前的种族种族隔离和多样性指标严重依赖人口普查数据,主要反映住宅模式.
- 这忽略了日常生活的动态社会背景,包括工作,休和差事.
- 更全面的理解需要衡量家庭之外的多样性.
研究的目的:
- 引入国家经验种族种族多样性 (NERD) 数据集.
- 提供美国各地经历的种族种族多样性的细粒度估计.
- 为了使国家,对社会互动多样性的比较研究.
主要方法:
- 利用匿名和选择的手机位置数据来识别个人的共同位置.
- 估计潜在的社会互动多样性在微小尺度 (38.2m x 19.1m) 和时间框架 (15分钟间隔).
- 聚合数据到人口普查区级别的隐私,创建一个特定年份的代表性数据集.
主要成果:
- 该NERD数据集提供了对经验多样性的新视角,超越了静态的住宅措施.
- 提供了美国各地细粒度层面的潜在社会互动的估计.
- 突出了这个新的研究数据源的特点和局限性.
结论:
- 该NERD数据集代表了在测量经历的种族-民族多样性方面取得的重大进展.
- 它允许对社会背景和群体间接触有更细微的理解.
- 这一数据集为未来研究美国的隔离,多样性和社会动态提供了潜力.
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