Understanding Skout users’ mobility patterns on a global scale: a data-driven study

  • Rong Xie
  • Yang Chen
  • Shihan Lin
  • Tianyong Zhang
  • Yu Xiao
  • Xin Wang
Part of the following topical collections:
  1. Special Issue on Web and Big Data


Location-based social apps, such as Skout, have been widely used by millions of users for sharing their location information. In this work, we collected all the location information published by over 1.2 million Skout users during December 2012 and June 2016. Based on the collected information, we model the inter-city mobility of Skout users with a global city network, and analyze the evolution of the network based on its structural characteristics. Moreover, we look into Skout users’ mobility patterns by discovering the most popular inter-city routes, destinations, and tightly connected city groups, and analyze the impact on the mobility patterns from geographical distances, languages and cultures. Finally, we leverage machine learning techniques to build a model for identifying the most influential cities in the world according to the Skout data. The results are able to assist individuals, governors and business leaders in making better decisions regarding traveling, immigrating, measuring city improvements and cooperation with cities.


Human mobility Skout Global city network PageRank 



This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700), Academy of Finland (No. 268096).


  1. 1.
    Bao, J., Zheng, Y., Mokbel, M.F.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of ACM SIGSPATIAL (2012)Google Scholar
  2. 2.
    Barrat, A., Barthelemy, M., Pastor-Satorras, R., Vespignani, A.: The architecture of complex weighted networks. PNAS 101(11), 3747–3752 (2004)CrossRefGoogle Scholar
  3. 3.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10,008 (2008)CrossRefGoogle Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439(7075), 462–465 (2006)CrossRefGoogle Scholar
  6. 6.
    Canzian, L., Musolesi, M.: Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In: Proceedings of ACM Ubicomp (2015)Google Scholar
  7. 7.
    Çelikten, E., Falher, G.L., Mathioudakis, M.: Modeling urban behavior by mining geotagged social data. IEEE Transactions on Big Data 3(2), 220–233 (2017)CrossRefGoogle Scholar
  8. 8.
    Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. In: Proceedings of IJCAI (2013)Google Scholar
  9. 9.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of ACM KDD (2011)Google Scholar
  10. 10.
    Cici, B., Gjoka, M., Markopoulou, A., Butts, C.T.: On the decomposition of cell phone activity patterns and their connection with urban ecology. In: Proceedings of ACM Mobihoc (2015)Google Scholar
  11. 11.
    Cranshaw, J., Schwartz, R., Hong, J.I., Sadeh, N.: The livehoods project: utilizing social media to understand the dynamics of a city. In: Proceedings of AAAI (2012)Google Scholar
  12. 12.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  13. 13.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  14. 14.
    Hao, Q., Cai, R., Wang, C., Xiao, R., Yang, J.M., Pang, Y., Zhang, L.: Equip tourists with knowledge mined from travelogues. In: Proceedings of WWW (2010)Google Scholar
  15. 15.
    Hufnagel, L., Brockmann, D., Geisel, T.: Forecast and control of epidemics in a globalized world. PNAS 101(42), 15,124–15,129 (2004)CrossRefGoogle Scholar
  16. 16.
    Kwak, H., Choi, Y., Eom, Y.H., Jeong, H., Moon, S.: Mining communities in networks: a solution for consistency and its evaluation. In: Proceedings of ACM IMC (2009)Google Scholar
  17. 17.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media?. In: Proceedings of the 19th International Conference on World Wide Web (2010)Google Scholar
  18. 18.
    Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 95 (1-2), 161–205 (2005)CrossRefzbMATHGoogle Scholar
  19. 19.
    le Cessie, S., van Houwelingen, J.: Ridge estimators in logistic regression. Appl. Stat. 41(1), 191–201 (1992)CrossRefzbMATHGoogle Scholar
  20. 20.
    Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2014)Google Scholar
  21. 21.
    Lin, S., Xie, R., Xie, Q., Zhao, H., Chen, Y.: Understanding user activity patterns of the swarm app: a data-driven study. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2017 ACM International Symposium on Wearable Computers (2017)Google Scholar
  22. 22.
    Liu, Q., Xiang, B., Yuan, N.J., Chen, E., Xiong, H., Zheng, Y., Yang, Y.: An influence propagation view of pagerank. ACM Trans. Knowl. Discov Data 11, 30:1–30:30 (2017)Google Scholar
  23. 23.
    Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. ICWSM 570–573 (2011)Google Scholar
  24. 24.
    Noulas, A., Shaw, B., Lambiotte, R., Mascolo, C.: Topological properties and temporal dynamics of place networks in urban environments. In: Proceedings of WWW (2015)Google Scholar
  25. 25.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Stanford InfoLab Technical Report 1999–66 (1999)Google Scholar
  26. 26.
    Preoţiuc-Pietro, D., Cohn, T.: Mining user behaviours: a study of check-in patterns in location based social networks. In: Proceedings of ACM Websci (2013)Google Scholar
  27. 27.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  28. 28.
    Tang, J., Lou, T., Kleinberg, J.: Inferring social ties across heterogenous networks. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (2012)Google Scholar
  29. 29.
    Watts, D.J.: Networks, dynamics, and the small-world phenomenon. Am. J. Sociol. 105(2), 493–527 (1999)CrossRefGoogle Scholar
  30. 30.
    Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P., Zhao, B.Y.: User interactions in social networks and their implications. In: Proceedings of ACM Eurosys (2009)Google Scholar
  31. 31.
    Yu, Y., Tang, S., Zimmermann, R., Aizawa, K.: Empirical observation of user activities: check-ins, venue photos and tips in foursquare. In: Proceedings of the 1st International Workshop on Internet-Scale Multimedia Management (2014)Google Scholar
  32. 32.
    Yuan, N.J., Zhang, F., Lian, D., Zheng, K., Yu, S., Xie, X.: We know how you live: exploring the spectrum of urban lifestyles. In: Proceedings of ACM COSN (2013)Google Scholar
  33. 33.
    Zhao, X., Sala, A., Wilson, C., Wang, X., Gaito, S., Zheng, H., Zhao, B.Y.: Multi-scale dynamics in a massive online social network. In: Proceedings of ACM IMC (2012)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Engineering Research Center of Cyber Security Auditing and MonitoringMinistry of EducationShanghaiChina
  3. 3.State Key Laboratory of Integrated Services NetworksXidian UniversityXi’anChina
  4. 4.Department of Communications and NetworkingAalto UniversityEspooFinland

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