A time-dependent stochastic block model with mixed or discrete membership classifies bicycle-sharing stations into home and work roles in Los Angeles, San Francisco, and a Manhattan subnetwork of New York City.
Unraveling the hidden organisation of urban systems and their mobility flows
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abstract
Increasing evidence suggests that cities are complex systems, with structural and dynamical features responsible for a broad spectrum of emerging phenomena. Here we use a unique data set of human flows and couple it with information on the underlying street network to study, simultaneously, the structural and functional organisation of 10 world megacities. We quantify the efficiency of flow exchange between areas of a city in terms of integration and segregation using well defined measures. Results reveal unexpected complex patterns that shed new light on urban organisation. Large cities tend to be more segregated and less integrated, while their overall topological organisation resembles that of small world networks. At the same time, the heterogeneity of flows distribution might act as a catalyst for further integrating a city. Our analysis unravels how human behaviour influences, and is influenced by, the urban environment, suggesting quantitative indicators to control integration and segregation of human flows that can be used, among others, for restriction policies to adopt during emergencies and, as an interesting byproduct, allows us to characterise functional (dis)similarities of different metropolitan areas, countries, and cultures.
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Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models
A time-dependent stochastic block model with mixed or discrete membership classifies bicycle-sharing stations into home and work roles in Los Angeles, San Francisco, and a Manhattan subnetwork of New York City.