PLoS ONE · 2012 · 629 citations · 33 references
Social SciencesGeographyHuman GeographySpatial AnalysisTransportationUrban GeographyMobility Pattern AnalysisMobility PatternsSpatial MobilityUrban MobilityUrban Spatial DistributionUrban TransportGranularityHuman Mobility AnalysisHuman Mobility PatternsHuman Movement AnalysisHuman Movement ModelingMobility Behavior ModelingMobility Data AnalysisSpatial Mobility Patterns
Geographic online social networks such as Foursquare, which provide GPS‑accurate location data down to 10 m and worldwide adoption, enable powerful studies of human movement. The study analyzes urban mobility patterns of Foursquare users across multiple global metropolitan cities. The authors analyze a large dataset of Foursquare users to examine urban mobility patterns. The analysis reveals that city‑specific variations in human movement are mainly due to differing place distributions, and identifies a universal rank‑distance law—where the number of places between origin and destination, rather than physical distance, governs movement—validated by a rank‑based model that accurately captures real human movements across cities.
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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