Mobility patterns are associated with experienced income segregation in large US cities

Esteban Moro,Xiaowen Dong,Alex Pentland,Dan Calacci

doi:10.1038/s41467-021-24899-8

Abstract

Traditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

Highlights

Traditional understanding of urban income segregation is largely based on static coarsegrained residential patterns
These results suggest that experienced income segregation in the social fabric of cities might be partly encoded in universal behavior and mathematical models that explain the urban mobility patterns of people
Our main data source is from Cuebiq, who supplied 6-month long records of anonymized and high-resolution mobile location pings for 4.5 million devices across 11 U.S census core-based statistical areas (CBSAs)

Summary

Introduction

Traditional understanding of urban income segregation is largely based on static coarsegrained residential patterns These do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Research that uses call detail records or GPS locations at the city- and country-scale to measure highresolution human movement has shown that individual mobility patterns are highly predictable[20,21,22], explainable by urban mobility models[20,23,24,25], and can be grouped into collective mobility behaviors[26] These results suggest that experienced income segregation in the social fabric of cities might be partly encoded in universal behavior and mathematical models that explain the urban mobility patterns of people

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Results

Conclusion