Urban structure and mobility as spatio-temporal complex networks

Yeghikyan G.

Urban mobility  Machine learning  Complex networks  Socio-economic attributes  Spatio-temporal activity  Neural networks 

Contemporary urban life and functioning have become increasingly dependent on mobility. Having become an inherent constituent of urban dynamics, the role of urban mobility in influencing urban processes and morphology has increased dramatically. However, the relationship between urban mobility and spatial socio-economic structure has still not been thoroughly understood. This work will attempt to take a complex network theoretical approach to study this intricate relationship through o the Spatio-temporal evolution of ad-hoc developed network centralities based on the Google PageRank, o multilayer network regression with statistical random graphs respecting network structures for explaining urban mobility flows from urban socio-economic attributes, o and Graph Neural Networks for predicting mobility flows to or from a specific location in the city. Making both practical and theoretical contributions to urban science by offering methods for describing, monitoring, explaining, and predicting urban dynamics, this work will thus be aimed at providing a network theoretical framework for developing tools to facilitate better decision-making in urban planning and policymaking.