在美国大都会地区基于流动性的隔离
1Department of Sociology and Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA.
Demography
|August 19, 2025
概括
这项研究引入了一种使用流动性数据测量隔离的新方法,揭示了日常流动如何在活动空间中产生明显的种族,种族和收入隔离. 它强调了自己群体的显著孤立和社区内部的多样化暴露.
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:
- 传统的隔离措施通常依赖于住宅附近,可能会忽视日常活动和互动的细微差别.
- 要了解隔离,不仅要检查人们居住的地方,还要检查他们去哪里,以及他们在日常活动中遇到谁.
研究的目的:
- 开发和验证一个新的,基于流动性的种族,种族和收入隔离的措施,在个人的活动空间.
- 区分当地的住宅环境和由日常旅行模式塑造的连接社区.
- 通过将措施分解为社区内部和社区间的组件来分析隔离的来源.
主要方法:
- 利用大型全球定位系统 (GPS) 在美国大都市地区从移动设备获取每日移动数据.
- 开发了一种基于移动性的群体暴露概念,从传统的基于距离的指标转移.
- 整合日常流动数据与美国人口普查数据的社区特征,以衡量流动连接和隔离.
主要成果:
- 基于移动性的隔离措施与传统基于距离的方法相比,捕捉了不同的维度.
- 个人在他们的活动空间中表现出明显的自己群体隔离,这是日常运动模式所证明的.
- 即使在人口结构相似的社区内,当地流动性暴露的实质性异质性也存在,特别是在少数群体中.
结论:
- 基于流动性的隔离措施为在活动空间中经历过的隔离提供了更全面的理解.
- 大规模的人类移动数据与新的隔离指标相结合,为复杂的社会不平等提供了有价值的见解.
- 调查结果强调了在研究种族,种族和收入隔离方面考虑日常流动的重要性.
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