Urban accessibility disparities - Applications of big data to measure spatial and temporal inequalities in mobility and accessibility in São Paulo
Matheus Cunha Barboza is a PhD student in the department Transport Engineering and Management. (Co)Promotors are prof.dr.ing. K.T. Geurs and dr. A.B. Grigolon from the Faculty of Engineering Technology, University of Twente and dr. M. Abrantes Giannotti, University of São Paulo.
Understanding spatial and temporal inequalities in mobility and accessibility is crucial for developing equitable urban policies. This thesis explores how big data can be leveraged to measure and address these disparities, providing insights that bridge the gap between academic research and practical urban planning.
The main research question of this thesis is: How can big data contribute to measuring spatial and temporal inequalities in mobility and accessibility? Through three interconnected studies, it integrates big data with space-time approaches to advance the understanding of urban accessibility disparities. The first study examines the impact of public transportation investments on accessibility to non-mandatory activities, such as parks. It uses the household origin-destination survey (OD) and compares multiple location-based and space-time accessibility metrics, revealing that these capture higher inequalities. The second study utilizes Call Detail Records (CDR) to analyze mobility variability between residents of favelas and non-favela areas. It highlights significant disparities and validates the utility of big data in identifying mobility challenges faced by marginalized groups. To understand how much of the resulting inequalities are due to living in favelas, we control the results for other variables, including income. The third study examines how transit fares influence accessibility inequalities, utilizing big data and space-time accessibility measures to integrate spatial, temporal, and financial constraints. Findings suggest that fare policies could significantly enhance accessibility equity for low-income populations.
The thesis makes the following key contributions: (1) it develops and applies advanced space-time accessibility metrics based on big data to improve the understanding of inequalities; (2) it validates CDR and SCD as scalable tools for mobility analysis, particularly in highlighting inequalities; and (3) it integrates equity metrics with space-time measures, advancing methodological frameworks for accessibility studies.
By addressing its central research question, this thesis underscores the potential of big data and space-time approaches in urban accessibility research. It highlights the persistent challenges marginalized populations face in São Paulo and calls for prioritizing equity in transportation planning. The findings provide a foundation for future research and policy action, aiming to create more inclusive and equitable urban mobility systems.
