Abstract
The advent of geographic online social networks such as Foursquare, where users voluntarily signal their current location, opens the door to powerful studies on human movement. In this chapter, the authors study urban mobility patterns of people in several metropolitan cities around the globe by analysing a large set of Foursquare users. Moreover, a universal law for human mobility is identified, which isolates as a key component the rank-distance, factoring in the number of places between origin and destination, rather than pure physical distance, as considered in some previous works. Building on their findings, the authors also show how a rank-based movement model accurately captures real human movements in different cities. The mobility data set used in this work comprises check-ins made by Foursquare users that has become publicly available through Twitter's streaming application programming interface.
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CITATION STYLE
Noulas, A., Scellato, S., Lambiotte, R., Pontii, M., & Mascolo, C. (2022). A tale of many cities: Universal patterns in human urban mobility. In Machine Learning and the City: Applications in Architecture and Urban Design (pp. 467–471). Wiley Blackwell.
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