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Jameson L Toole1, Carlos Herrera-Yaqüe2, Christian M Schneider3

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This summary is machine-generated.

Human mobility patterns are predictable by social connections. People’s movements are more similar to their contacts than strangers, revealing distinct social tie categories and informing mobility models.

Keywords:
city sciencecomplex systemshuman mobilitymobile phonesnetworks

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Area of Science:

  • Social network analysis
  • Human mobility studies
  • Urban science

Background:

  • Massive communication data reveal social network dynamics.
  • Geographically tagged data enable human mobility and urban studies.
  • Combining these fields uncovers reproducible social mobility patterns.

Purpose of the Study:

  • To investigate reproducible mobility patterns among social contacts.
  • To introduce and measure mobility similarity and predictability.
  • To explore the relationship between social ties, geography, and mobility behavior.

Main Methods:

  • Analysis of passively collected communication data with geographical information from three urban areas.
  • Development of measures for mobility similarity and predictability.
  • Unsupervised clustering of hourly mobility variations and ego network composition analysis.
  • Extension of a mobility model to incorporate social contact-based movement choices.

Main Results:

  • Individual mobility patterns are significantly more similar and predictable by social contacts than by strangers.
  • Mobility similarity and predictability positively correlate with social tie strength.
  • Clustering identified three distinct categories of social ties based on mobility patterns.
  • Ego network composition correlates with user mobility behavior.
  • An extended mobility model incorporating social contacts shows improved empirical reproduction.

Conclusions:

  • Geography is a crucial factor in contextualizing social relationships.
  • Social contacts play a significant role in shaping human mobility.
  • Understanding social-mobility links can enhance urban planning and social network analysis.