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

Esteban Moro, Dan Calacci, Xiaowen Dong, Alex Pentland
Author Information
  1. Esteban Moro: Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. esteban.moroegido@gmail.com. ORCID
  2. Dan Calacci: Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. ORCID
  3. Xiaowen Dong: Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
  4. Alex Pentland: Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. ORCID

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.

References

  1. Proc Natl Acad Sci U S A. 2008 Mar 18;105(11):4109-14 [PMID: 18334650]
  2. RSF. 2017 Feb;3(2):210-231 [PMID: 29034322]
  3. Proc Natl Acad Sci U S A. 2013 Aug 20;110(34):13774-9 [PMID: 23918373]
  4. Nat Commun. 2018 Aug 20;9(1):3330 [PMID: 30127416]
  5. Nat Commun. 2015 Sep 08;6:8166 [PMID: 26349016]
  6. Soc Sci Res. 2017 Feb;62:175-188 [PMID: 28126097]
  7. J Geogr Syst. 2011 Jun;13(2):127-145 [PMID: 21643546]
  8. PLoS One. 2017 Feb 15;12(2):e0171686 [PMID: 28199347]
  9. Science. 2010 Feb 19;327(5968):1018-21 [PMID: 20167789]
  10. Proc Natl Acad Sci U S A. 2018 Jul 24;115(30):7735-7740 [PMID: 29987019]
  11. Nature. 2008 Jun 5;453(7196):779-82 [PMID: 18528393]
  12. Lancet. 2017 Apr 8;389(10077):1475-1490 [PMID: 28402829]
  13. Popul Index. 1986 Summer;52(2):198-221 [PMID: 12340704]
  14. R Soc Open Sci. 2018 Oct 3;5(10):180749 [PMID: 30473825]
  15. Am Econ Rev. 2016 Apr;106(4):855-902 [PMID: 29546974]

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