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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zheng, Y., Zhou, X.: Computing with spatial trajectories. Springer Science+Business Media (2011)
Garlaschelli, D., Loffredo, M.I.: Structure and evolution of the world trade network. Physica A: Statistical Mechanics and its Applications 355, 138–144 (2005)
Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from GPS trajectories, pp. 791–800 (2009)
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)
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.: Mining user similarity based on location history, p. 34 (2008)
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)
Wikipedia, http://en.wikipedia.org/wiki/Sina_Weibo
Goodchild, M.F., Glennon, J.A.: Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth 3, 231–241 (2010)
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)
Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: Exploiting semantic annotations for clustering geographic areas and users in location-based social networks (2011)
Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning, vol. 1. Springer, New York (2006)
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)
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)
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)
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)
Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. In: ICWSM 2011 (2011)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, J., Hu, Q., Li, Q. (2014). A Study of Users’ Movements Based on Check-In Data in Location-Based Social Networks. In: Pfoser, D., Li, KJ. (eds) Web and Wireless Geographical Information Systems. W2GIS 2014. Lecture Notes in Computer Science, vol 8470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55334-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-55334-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55333-2
Online ISBN: 978-3-642-55334-9
eBook Packages: Computer ScienceComputer Science (R0)