Segregated interactions in urban and online space

TL;DR

This paper investigates behavioral segregation in urban and online spaces by analyzing credit card and Twitter data across three cities, revealing amplified and asymmetric socio-economic interaction patterns.

Contribution

It introduces a novel analysis of social and economic interaction segregation using digital transaction and communication data, extending beyond traditional geographic studies.

Findings

Segregation in interactions is stronger than geographic segregation.

Interaction from poorer to wealthier areas is more frequent than vice versa.

Segregation increases with socio-economic differences.

Abstract

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications.