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Members of the Dissertation Committee:
Chair: Federico Battiston (CEU DNDS, voting)
Supervisor: Márton Karsai (CEU DNDS, non-voting)
Reviewer: Kinga Makovi (New York University Abu Dhabi, voting)
Reviewer: Paolo Bajardi (ISI Foundation/CENTAI, Turin, voting)
ABSTRACT / City is a living laboratory for more than half of population across the globe. Conceptualisation of city is not limited to the design of physical structure but rather far reaching to the dynamics of each element that makes it alive respectively people, places, and interactions. People visit various locations on daily basis to run their errands. Places occupy particular area given their functions. Individual preference regarding places they would like to visit differ from one to another and to the extent dictated by socioe- conomic background. Considering such multifaceted setting, this thesis aims to analyse the structure and dynamics of mobility segregation and spatial diversity in cities through the lenses of network science and complex system with computational approach. At first, we lay integrated theoretical foundation in portraying city, complex system, and urban mixing. It is followed by the investigation on mixing patterns of urban mobility, supported by data driven study in the twenty largest cities of the United States. We show that stratification exists in visit preference, presaging the ‘upwards bias’ and closely related to segregation at large. Furthermore, we focus on analysing the impact of COVID-19 on the configuration of mobility patterns in a number of main capital cities namely New York, London, Jakarta and Bogota. The findings suggest that dy- namical segregation phenomena exist which inevitably worsened during lockdown and sustained into long term effects as residual segregation. In the last study, we formulate artefact of measurement for economic diversity in urban context. It results in the notion of dual centrality, highlighting the importance of economic diversity in determining the location centrality. Finally, we present the overall contributions of this thesis and draw the line for both empirical implementations and future extensions.
BIO / Rafiazka’s work stands on two levels of playing fields, namely social science and computational social science, with network and data science as an intermediary in between. Her formal training at both bachelor and master level in Economics, Politics, and International Relations lay an integrated theoretical foundation for further scientific investigations related to dynamics of individuals, dynamics of social interactions, and collective social dynamics. Her current work focuses on the socioeconomic segregation in urban mobilities based on high dimensional spatiotemporal data.