Using human mobility data to quantify experienced urban inequalities

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.