Unified framework for measuring segregation resolves how social and geographical space jointly shape connections

2026-09-14

AI Summary

研究提出统一框架测量地理和社会隔离如何共同影响连接，通过比较网络模型与适当零模型，并利用隐私保护聚合关系数据来推断地区-群体单元对之间的链接概率。

Abstract

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.

Problem

• segregation
• geographical space
• social space
• interaction

Innovation

• unified framework
• geosocial networks
• segregation measurement
• aggregated relational data (ARD)
• intervening-opportunities model