A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban Structure and Mobility as Spatio-temporal complex Networks
Contemporary urban life and functioning have become increasingly dependent on mobility. Having become an inherent constituent of urban dynamics, the role of urban moblity in influencing urban processes and morphology has increased dramat- ically. 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 studying this intricate relationship through • the spatio-temporal evolution of ad-hoc developed network centralities based on the Google PageRank, • multilayer network regression with statistical random graphs respecting net- work structures for explaining urban mobility flows from urban socio-economic attributes, • 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 policy making.
Urban Structure and Mobility as Spatio-temporal complex Networks
Contemporary urban life and functioning have become increasingly dependent on mobility. Having become an inherent constituent of urban dynamics, the role of urban moblity in influencing urban processes and morphology has increased dramat- ically. 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 studying this intricate relationship through • the spatio-temporal evolution of ad-hoc developed network centralities based on the Google PageRank, • multilayer network regression with statistical random graphs respecting net- work structures for explaining urban mobility flows from urban socio-economic attributes, • 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 policy making.
Urban Structure and Mobility as Spatio-temporal complex Networks
YEGHIKYAN, Gevorg (author) / Yeghikyan, Gevorg
2020-10-16
Theses
Electronic Resource
English
Settore MAT/06 - Probabilita' e Statistica Matematica , urban mobility , Settore ICAR/21 - Urbanistica , spatio-temporal activity , Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni , complex network , Settore SPS/10 - Sociologia dell'Ambiente e del Territorio , Settore ICAR/05 - Trasporti , socio-economic attribute , machine learning , neural networks
DDC:
710
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