Advertisement

A Study of Users’ Movements Based on Check-In Data in Location-Based Social Networks

  • Jinzhou Cao
  • Qingwu Hu
  • Qingquan Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8470)

Abstract

With the development of GPS technology and the increasing popularity of mobile device, Location-based Social Networks (LBSN) has become a platform that promote the understanding of user behavior, which offers unique conditions for the study of users’ movement patterns.

Characteristics of users’ movements can be expressed by places they’ve visited. This paper presents a method to analyze characteristics of users’ movements in spatial and temporal domain based on data collected from a Chinese LBSN Sina Weibo. This paper analyzes spatial characteristics of users’ movement by clustering geographic areas through their check-in popularity. Meanwhile, temporal characteristics and variation of users’ movements on the timeline is analyzed by applying statistical method.

Keywords

Spectral Cluster Mobile Social Networking Scenic Spot Spectral Cluster Algorithm Area Division 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zheng, Y., Zhou, X.: Computing with spatial trajectories. Springer Science+Business Media (2011)Google Scholar
  2. 2.
    Garlaschelli, D., Loffredo, M.I.: Structure and evolution of the world trade network. Physica A: Statistical Mechanics and its Applications 355, 138–144 (2005)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from GPS trajectories, pp. 791–800 (2009)Google Scholar
  4. 4.
    Liang, L.Y., Ren, L.L., Wan, Y.H.: “LBS-based Social Network” of the Management and Operations in Urban public Space. Information Security and Technology 7, 56–63 (2011)Google Scholar
  5. 5.
    Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.: Mining user similarity based on location history, p. 34 (2008)Google Scholar
  6. 6.
    Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.: Recommending friends and locations based on individual location history. ACM Transactions on the Web (TWEB) 5, 5 (2011)Google Scholar
  7. 7.
  8. 8.
    Goodchild, M.F., Glennon, J.A.: Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth 3, 231–241 (2010)CrossRefGoogle Scholar
  9. 9.
    Scellato, S., Mascolo, C.: Measuring user activity on an online location-based social network. In: 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 918–923 (2011)Google Scholar
  10. 10.
    Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: Exploiting semantic annotations for clustering geographic areas and users in location-based social networks (2011)Google Scholar
  11. 11.
    Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning, vol. 1. Springer, New York (2006)zbMATHGoogle Scholar
  12. 12.
    Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 2, pp. 849–856 (2002)Google Scholar
  13. 13.
    Hagen, L., Kahng, A.B.: New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems 11, 1074–1085 (1992)CrossRefGoogle Scholar
  14. 14.
    Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 2, pp. 849–856 (2002)Google Scholar
  15. 15.
    Mei, Y.C., Wei, Y.K., Yit, K.C., Angeline, L., Teo, K.T.K.: Image segmentation via normalised cuts and clustering algorithm. In: 2012 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 430–435 (2012)Google Scholar
  16. 16.
    Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. In: ICWSM 2011 (2011)Google Scholar
  17. 17.
    Aubrecht, C., Ungar, J., Freire, S.: Exploring the potential of volunteered geo-graphic information for modeling spatio-temporal characteristics of urban population. In: Proceedings of 7VCT 11, p. 13 (2011)Google Scholar
  18. 18.
    Ye, M., Janowicz, K., Mülligann, C., Lee, W.: What you are is when you are: the temporal dimension of feature types in location-based social networks. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 102–111. ACM (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jinzhou Cao
    • 1
  • Qingwu Hu
    • 1
  • Qingquan Li
    • 2
    • 3
  1. 1.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanP.R. China
  2. 2.Shenzhen Key Laboratory of Spatial Smart Sensing and ServicesShenzhen UniversityShenzhenP.R. China
  3. 3.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanP.R. China

Personalised recommendations