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      A Tale of Many Cities: Universal Patterns in Human Urban Mobility

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          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 particular the fine granularity of the location data, with GPS accuracy down to 10 meters, and the worldwide scale of Foursquare adoption are unprecedented. In this paper we study urban mobility patterns of people in several metropolitan cities around the globe by analyzing a large set of Foursquare users. Surprisingly, while there are variations in human movement in different cities, our analysis shows that those are predominantly due to different distributions of places across different urban environments. 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 our findings, we also show how a rank-based movement model accurately captures real human movements in different cities.

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          On Information and Sufficiency

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            Power-Law Distributions in Empirical Data

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              Understanding individual human mobility patterns.

              Despite their importance for urban planning, traffic forecasting and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                29 May 2012
                : 7
                : 5
                : e37027
                Affiliations
                [1 ]Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
                [2 ]Department of Mathematics, University of Namur, Namur, Belgium
                [3 ]Department of Computer Science, University College London, London, United Kingdom
                University of Oxford, United Kingdom
                Author notes

                Conceived and designed the experiments: AN SS RL MP CM. Performed the experiments: AN SS. Analyzed the data: AN SS. Wrote the paper: AN SS RL MP CM.

                Article
                PONE-D-11-23287
                10.1371/journal.pone.0037027
                3362592
                22666339
                dcc42e17-c8b7-431c-ac39-bbbd815af11e
                Noulas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 18 November 2011
                : 17 April 2012
                Page count
                Pages: 10
                Categories
                Research Article
                Biology
                Ecology
                Biogeography
                Urban Ecology
                Computer Science
                Earth Sciences
                Geography
                Human Geography
                Behavioral Geography
                Social Geography
                Medicine
                Mental Health
                Psychology
                Human Relations
                Social and Behavioral Sciences
                Sociology
                Social Networks

                Uncategorized
                Uncategorized

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