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

By Anastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil, Cecilia Mascolo. PLoS ONE, 2011

679 citations recorded by the source corpus.

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.

Subfields: Global and Planetary Change, Transportation

View Paper: https://www.semanticscholar.org/paper/a8aaae1ed8a941be1f1c0e4c76be9fb514c1801d

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Citations in the corpus

Mobile Crowd Sensing and Computing (2015, background): “For instance, Noulas et al. [2012] studied human mobility patterns by analyzing the check-in histories of a large set of LBSN (location-based social network) users.”
The statistical physics of cities (2019, background): “Apart from this general discussion, there are many studies on urban mobility, in particular using new data sources such as the choice of routes [57] [58] [59], universal patterns in human mobility [60], or congestion effects [61] [62].”
Social Media data: Challenges, opportunities and limitations in urban studies (2019, neutral): “…provide a means by which to assess different urban dynamics, such as, mobility (Cheng, Caverlee, Lee, & Sui, 2011; Luo, Cao, Mulligan, & Li, 2016; Noulas, Scellato, Lambiotte, Pontil, & Mascolo, 2012; Quercia, Aiello, Schifanella, & Davies, 2015a, 2015b); land uses and urban activity…”
Revealing daily travel patterns and city structure with taxi trip data (2013, background): “Since a trip implies strong relationship between the origin and the destination, much research (Thiemann et al. 2010, Brockmann et al. 2006, Gonzalez et al. 2008, Noulas et al. 2012, Cheng et al. 2011, Kang et al. 2012) investigated topics related to spatial interactions based on people’s movements.”
Human mobility modeling at metropolitan scales (2012, background): “Previous work has shown significant differences between cities along metrics such as commute distances [16, 17, 18, 25].”
Smart cities of the future (2012, positive): “Content analysis allows semantically-rich analysis of land use [41], temporal variability of current-moment interests within cities and related travel behaviours [37], and inter-city comparisons which reveal common dependencies in human mobility across countries and continents [34].”
A Survey on Deep Learning for Human Mobility (2020, background): “Examples include tracks generated by GPS devices embedded in smartphones [238] or private vehicles [137]; mobile phone records [20]; and geotagged posts from social media platforms [129].”
From mobile phone data to the spatial structure of cities (2014, methodology): “Various data sources have been used such as car GPS [2, 3], RFIDs for collective transportation [4], and also data coming from social networks such as Twitter[5] or Foursquare[6].”
Human mobility: Models and applications (2017, methodology): “These profiles can range from intra-urban routine trips to worldwide travel [65, 66].”
It's the way you check-in: identifying users in location-based social networks (2014, background): “Examples of applications go from the prediction of the next visited location [27] to the clustering of different types of behaviors of users [18].”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, methodology): “…, developing accurate PEV energy consumption models 28 , energy management 29,30 , smart charging strategies that probe centralized and decentralized approaches 31,32 , scheduling 33,34 , peak shaving, emissions, pricing models 35,36 and joint optimization of power and transportation networks 37 .”
Geo-located Twitter as proxy for global mobility patterns (2013, methodology): “Keywords: geo-located Twitter; global mobility patterns; community detection; collective sensing”
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “They include mobile phone call records [7,30], GPS (Global Positioning System) enabled taxi trajectories [8,9,31], smart card records in public transportation systems [32], and check-in data [2,3].”
Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data (2013, methodology): “Researchers have made progress in this effort using a variety of data sources such as circulating bank notes [1], taxi trip records [2,50–51], Foursquare check-in data [3], Tweets [52], and even GPS devices [4–6].”