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arxiv: 1501.07788 · v2 · pith:DPWFT46Xnew · submitted 2015-01-30 · ⚛️ physics.soc-ph · cs.CY· cs.SI· physics.data-an· stat.AP

Human diffusion and city influence

classification ⚛️ physics.soc-ph cs.CYcs.SIphysics.data-anstat.AP
keywords citycitiesglobalhumaninfluencemobilitynetworkregional
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Cities are characterized by concentrating population, economic activity and services. However, not all cities are equal and a natural hierarchy at local, regional or global scales spontaneously emerges. In this work, we introduce a method to quantify city influence using geolocated tweets to characterize human mobility. Rome and Paris appear consistently as the cities attracting most diverse visitors. The ratio between locals and non-local visitors turns out to be fundamental for a city to truly be global. Focusing only on urban residents' mobility flows, a city to city network can be constructed. This network allows us to analyze centrality measures at different scales. New York and London play a predominant role at the global scale, while urban rankings suffer substantial changes if the focus is set at a regional level.

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