社区有边界吗?社区有边界吗? 一个新的经验测试对一个历史问题
1Department of Sociology, University of Wisconsin-Madison, Madison, WI, United States of America.
PloS one
|December 2, 2024
概括
邻里边界是理解隔离的关键. 流动性数据揭示了城市内的独特集群,突出了种族,教育和年龄的差异,为衡量社会分裂提供了一种新方法.
科学领域:
- 城市研究 城市研究
- 社会学 社会学 社会学
- 数据科学数据科学数据科学
背景情况:
- 关于社区的定义和运营存在学术辩论.
- 最近的研究倾向于去强调邻里边界.
- 了解邻里边界对于分析隔离至关重要.
研究的目的:
- 通过移动数据调查社区边界的存在和意义.
- 展示移动模式如何划分不同的城市集群.
- 分析这些集群的社会人口统计和公共卫生特征.
主要方法:
- 利用来自4500万个全国代表设备的日常移动模式数据.
- 采用了一种新的聚类程序来识别人口普查区组的不同组.
- 分析了基于流动性的划分与人口统计 (种族,教育,年龄,职业) 和公共卫生 (COVID-19发病率,犯罪时间) 数据的对齐.
主要成果:
- 人口普查区组之间的流动性模式划分与种族,教育程度,职业和年龄的差异有关.
- 一种新的集群程序成功地将人口普查区组分为基于移动差异的有意义的集群.
- 这些集群在种族,教育程度和年龄方面表现出独特的隔离,可能低估了与其他空间聚合物的真正隔离.
- 群体之间COVID-19病例发生率和犯罪时间的显著差异表明存在不同的社会过程.
结论:
- 移动数据可以识别有意义的城市集群,具有明显的边界,即使它们不代表客观的社区.
- 这些以流动性定义的集群为社会分析提供了有价值的地理单元,包括测量隔离和研究网络弹性.
- 这些发现强调了考虑流动模式在理解城市社会结构和隔离方面的重要性.
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