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Using human mobility data to quantify experienced urban inequalities.

Fengli Xu1, Qi Wang2, Esteban Moro3,4

  • 1Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China. fenglixu@tsinghua.edu.cn.

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Urban mobility data reveals experienced inequalities in social mixing, access to facilities, and adaptation to events. This research offers a new framework to track dynamic urban inequality through people-place networks.

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Area of Science:

  • Urban Studies
  • Sociology
  • Data Science

Background:

  • Urban life is shaped by personal mobility, access to resources, and social dynamics.
  • Inequality and segregation are significant aspects of the urban experience.
  • Fine-grained mobility data offers new insights into experienced inequalities at scale.

Purpose of the Study:

  • To review emerging uses of urban mobility behavior data.
  • To propose an analytic framework for understanding experienced urban inequality.
  • To track dynamic inequalities through people-place network analysis.

Main Methods:

  • Utilizing fine-grained mobility data and contextual attributes.
  • Developing a temporal bipartite network model representing people and places.
  • Analyzing network reconfiguration to track inequality dimensions.

Main Results:

  • The proposed framework allows tracking experienced inequality across social mixing, facility access, and adaptation to events.
  • Mobility patterns reveal dynamic, lived experiences of urban inequality.
  • This approach complements existing studies on static inequalities.

Conclusions:

  • Urban mobility data, analyzed through a temporal bipartite network, provides a powerful lens for understanding experienced inequality.
  • The framework enables dynamic tracking of social mixing, access, and resilience in urban environments.
  • This research highlights the potential of data-driven approaches to reveal and address urban disparities.