发表机构
Imperial College London; Massachusetts Institute of Technology; Harvard T.H. Chan School of Public Health; Indian Institute of Technology Delhi（帝国理工学院; 麻省理工学院; 哈佛大学陈曾熙公共卫生学院; 印度德里理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出统一框架测量地理社交网络中的隔离，发现社会隔离主导地理隔离，且地理距离会放大社会同质性，从而间接影响跨群体联系。
AI 中文摘要
我们对地理隔离与社会隔离如何相互作用的理解仍然有限，因为相对较少的研究对它们进行联合考察，且现有方法往往缺乏区分地理隔离、社会隔离和总体隔离的框架。此外，大规模、个体分辨的地理社交网络数据很少公开可用。我们解决了这两个问题。在概念上，我们开发了一个统一框架，通过将网络模型与适当的零模型进行比较来测量地理社交网络中的隔离，并将泰尔指数、相异指数和网络模块度作为特例恢复。在实证上，我们转向隐私保护的聚合关系数据（ARD）：我们将美国Facebook社会连通性指数与美国人口普查和皮尤数据相结合，并引入一个兼容ARD的联合地理社交介入机会模型，以推断区域-群体单元对之间的链接概率。应用我们的隔离框架，我们发现社会隔离主导地理隔离，在两种隔离类型中，白人与黑人、大学学历与无大学学历、高收入与中低收入群体之间存在显著分离。我们发现社会同质性随地理距离增加而增强，以及群体特定的地理连通性模式，这表明地理隔离可能不仅直接影响跨群体连通性，还可能通过放大社会隔离来间接影响。
英文摘要
Our understanding of how geographical and social segregation interact remains limited, as relatively few studies investigate them jointly, and existing approaches often lack a framework distinguishing geographical, social, and total segregation. Additionally, large-scale individually resolved geo-social network data are rarely publicly available. We address both. Conceptually, we develop a unified framework that measures segregation in geosocial networks by comparing network models to appropriate null models and recovers the Theil index, dissimilarity index, and network modularity as special cases. Empirically, we turn to privacy-preserving aggregated relational data (ARD): we combine the Facebook Social Connectedness Index for the US with US Census and Pew data, and introduce an ARD-compatible joint geosocial intervening-opportunities model to infer link probabilities between region--group cell pairs. Applying our segregation framework, we find that social segregation predominates over geographical segregation, with notable separation for White--Black, college-degree--no-degree, and high-income--low/middle-income across both segregation types. We find increasing social homophily with geographical distance and group-specific geographical connectivity patterns, suggesting that geographical segregation may affect cross-group connectivity not only directly but also by amplifying social segregation.
CommentsSupplementary Information included for the segregation framework; further supplementary material for the intervening opportunities model and the empirical analysis will be added in a subsequent version
