Nilforoshan, H., Looi, W., Pierson, E. et al. Human mobility networks reveal increased segregation in large cities. Nature 624, 586–592 (2023)

Objective:

• A nationwide study of socioeconomic mixing and urbanization has not been undertaken because of difficulties in ascertaining individual-level socioeconomic status (SES), determining when dyadic exposure occur, and amassing the data needed to compare across cities or counties

Case:

• US

Methodology:

• Exposure segregation: $ES = Corr(SES, \overline{SES_es}) = \frac{cov(SES, \overline{SES})}{\sigma_{SES}\sigma_{\overline{SES}}}$

Data Source

• Mobile phone and POIs: SafeGraph
• Home: CoreLogit real estate database
• Rent: Zillow
• Census: American Community Survey
• Transport: Tiger

Findings:

• Exposure segregation is higher in large MSAs
• Exposure segregation is lower because when people venture outside their home tracts, they experience more diversity
• Segregation can be mitigated when frequently visited POIs

• Larger MSAs offer residents a greater number of leisure choices

Coding Reference:

• Python