Unified framework for measuring segregation resolves how social and geographical space jointly shape connections
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.
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BibTeX
@misc{happenhofer2026unified,
title = {{Unified framework for measuring segregation resolves how social and geographical space jointly shape connections}},
author = {Johannes Happenhofer and Sahil Loomba and Till Hoffmann and Sumeet Agarwal and Nick S. Jones},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.16469},
eprint = {2609.16469},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16469},
}
RIS
TY - UNPB
TI - Unified framework for measuring segregation resolves how social and geographical space jointly shape connections
AU - Johannes Happenhofer
AU - Sahil Loomba
AU - Till Hoffmann
AU - Sumeet Agarwal
AU - Nick S. Jones
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2609.16469
UR - https://arxiv.org/abs/2609.16469
AB - 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.
ER -