The Social Integration of American Cities: Network Measures of Connectedness Based on Everyday Mobility Across Neighborhoods
The Social Integration of American Cities: Network Measures of Connectedness Based on Everyday Mobility Across Neighborhoods
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DOI：
10.1177/0049124119852386
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发表时间：
2019-07-17
影响因子：
6.3
通讯作者：
Wang, Ryan Q.
中科院分区：
文献类型：
--
作者：
Phillips, Nolan E.;Levy, Brian L.;Wang, Ryan Q.
一个城市的社会整合取决于来自不同社区的人们有机会相互交流的程度，但大多数先前的工作并没有发展出概念化和衡量这种联系的正式方法。在这篇文章中，我们基于人们在社区之间的日常出行，开发了我们称之为“结构连通性”的基于网络的原始测量方法。我们的主要指数反映了一个城市每个社区的居民以相同的比例前往所有其他社区的程度。我们的二级指数反映了一个城市内的旅行集中在少数几个接收社区的程度。我们根据过去18个月数亿条带有地理标签的推文，说明了美国50个最大城市的指数价值。我们发现了美国主要城市的重要特征，包括它们的连通性在多大程度上依赖于几个社区中心，以及在一些城市中，一些社区之间几乎不存在联系的事实。我们还表明，人口密度更高、世界主义更强、种族隔离更少的城市，其结构连通性水平更高。我们的指数可以应用于任何空间尺度的数据，我们的测量方法为更强大、更精确地分析结构连通性及其对广泛社会现象的影响铺平了道路。
The social integration of a city depends on the extent to which people from different neighborhoods have the opportunity to interact with one another, but most prior work has not developed formal ways of conceptualizing and measuring this kind of connectedness. In this article, we develop original, network-based measures of what we call "structural connectedness" based on the everyday travel of people across neighborhoods. Our principal index captures the extent to which residents in each neighborhood of a city travel to all other neighborhoods in equal proportion. Our secondary index captures the extent to which travels within a city are concentrated in a handful of receiving neighborhoods. We illustrate the value of our indices for the 50 largest American cities based on hundreds of millions of geotagged tweets over 18 months. We uncover important features of major American cities, including the extent to which their connectedness depends on a few neighborhood hubs, and the fact that in several cities, contact between some neighborhoods is all but nonexistent. We also show that cities with greater population densities, more cosmopolitanism, and less racial segregation have higher levels of structural connectedness. Our indices can be applied to data at any spatial scale, and our measures pave the way for more powerful and precise analyses of structural connectedness and its effects across a broad array of social phenomena.