Socio-spatial segregation and human mobility: A review of empirical evidence
<|reference_start|>Socio-spatial segregation and human mobility: A review of empirical evidence: Social segregation, the spatial and social separation between individuals from different backgrounds, can affect sustainable urban development and social cohesion. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on income, ethnicity, and education. However, this approach overlooks the complexity of spatial segregation because daily activities often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that day-to-day mobility can impact segregation by reducing or amplifying segregation levels obtained when considering residential aspects alone. This literature review focuses on three critical questions: (a) How do human mobility patterns relate to individuals' segregation experiences? (b) What key factors explain the relationship between one's mobility patterns and segregation experiences? and (c) What are the strengths and limitations of segregation research that incorporates extensive mobility data? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. By incorporating studies from computational social science, urban science, and transportation, our review aims to provide actionable insights for reducing segregation and addressing research gaps in this increasingly interdisciplinary area.<|reference_end|>
@article{liao2024socio-spatial,
    title={Socio-spatial segregation and human mobility: A review of empirical
  evidence},
    author={Yuan Liao, Jorge Gil, Sonia Yeh, Rafael H. M. Pereira, Laura
  Alessandretti},
    journal={arXiv preprint arXiv:2403.06641},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06641},
    primaryClass={cs.SI}
}