https://doi.org/10.1140/epjds/s13688-022-00341-x
Regular Article
Socioeconomic biases in urban mixing patterns of US metropolitan areas
1
Department of Network and Data Science, Central European University, 1100, Vienna, Austria
2
Department of Computer Science, Aalto University School of Science, 00076, Aalto, Finland
3
Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, 04510, Ciudad de México, Mexico
4
Alfréd Rényi Institute of Mathematics, 1053, Budapest, Hungary
Received:
14
September
2021
Accepted:
25
April
2022
Published online:
23
May
2022
Urban areas serve as melting pots of people with diverse socioeconomic backgrounds, who may not only be segregated but have characteristic mobility patterns in the city. While mobility is driven by individual needs and preferences, the specific choice of venues to visit is usually constrained by the socioeconomic status of people. The complex interplay between people and places they visit, given their personal attributes and homophily leaning, is a key mechanism behind the emergence of socioeconomic stratification patterns ultimately leading to urban segregation at large. Here we investigate mixing patterns of mobility in the twenty largest cities of the United States by coupling individual check-in data from the social location platform Foursquare with census information from the American Community Survey. We find strong signs of stratification indicating that people mostly visit places in their own socioeconomic class, occasionally visiting locations from higher classes. The intensity of this ‘upwards bias’ increases with socioeconomic status and correlates with standard measures of racial residential segregation. Our results suggest an even stronger socioeconomic segregation in individual mobility than one would expect from system-level distributions, shedding further light on uneven mobility mixing patterns in cities.
Key words: Segregation in mobility / Urban mixing / Socioeconomic inequalities
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-022-00341-x.
© The Author(s) 2022
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
