Advertisement

Visual analytics of cellular signaling data

  • Haisheng LiEmail author
  • Yang Wei
  • Yuanjie Huang
  • Qiang Cai
  • Junping Du
Article
  • 52 Downloads

Abstract

Cellular signaling data is a type of traffic data, which contains rich spatio-temporal information. Rather than studying the trajectories of individuals, we propose a visual analytics methodology to analyze the crowd flows among a geographical network extracted from real-time cellular signaling data. We design a suite of visualization techniques to explore and reveal mobility patterns over the networks of spatiotemporal clustering. The feasibility of our approach was verified on a real real-time cellular signaling dataset in one week.

Keywords

Cellular signaling data Visual analytics Clustering Human mobility 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China(No.61320106006, No.61532006, No.61877002) and the Beijing Municipal Natural Science Foundation (No.4162019).

References

  1. 1.
    Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40 (1):200–210CrossRefGoogle Scholar
  2. 2.
    Darbari M, Kumar P, Rakesh S, Prakash S (2010) FRIX-traffic analyzer and transportation assistant. Int J Comput Sci Eng 2(4):1034–1040Google Scholar
  3. 3.
    Deville P, Linard C, Martin S, Gilbert M, Stevens FR, Gaughan AE, Blondel VD, Tatem AJ (2014) Dynamic population mapping using mobile phone data. Proc Natl Acad Sci 111(45):15888–15893CrossRefGoogle Scholar
  4. 4.
    Fan DP, Zhang SC, Wu YH et al (2018) Face sketch synthesis style similarity: a new structure co-occurrence texture measure. In: Proceedings of ACM multimedia conference (MM18). ACM, Seoul, Korea p 9Google Scholar
  5. 5.
    Fumo A, Fiore M, Stanica R (2017) Joint spatial and temporal classification of mobile traffic demands. In: INFOCOM 2017-IEEE conference on computer communications, Atlanta, GA, USA May 1 - May 4, pp 1–9Google Scholar
  6. 6.
    Gu J, Bae SJ, Min YC, Cheon KY (2010) Mobility-based handover decision mechanism to relieve ping-pong effect in cellular networks. In: Proceedings of the 16th Asia-Pacific conference on communications, pp 487–491Google Scholar
  7. 7.
    Haklay M, Weber P (2008) Openstreetmap: user-generated street maps. IEEE Pervasive Comput 7(4):12–18CrossRefGoogle Scholar
  8. 8.
    Han Z, Johnson T, Zhang J et al (2017) Functional virtual flow cytometry: a visual analytic approach for characterizing single-cell gene expression patterns. BioMed Res Int 2017:1–9Google Scholar
  9. 9.
    Isaacman S, Becker R, Cceres R, Kobourov S, Martonosi M, Rowland J, Varshavsky A (2011) Identifying important places in peoples lives from cellular network data. In: proceedings of pervasive11. Springer Press, Berlin, pp 133–151Google Scholar
  10. 10.
    Isaacman S, Becker R, Cceres R, Martonosi M, Rowland J, Varshavsky A, Willinger W (2012) Human mobility modeling at metropolitan scales. In: The 10th international conference on mobile systems, applications, and services. ACM, pp 239–252Google Scholar
  11. 11.
    Kim DH, Hightower J, Govindan R, Estrin D (2009) Discovering semantically meaningful places from pervasive RF-beacons. In: Proceedings UBICOMP 2009: ubiquitous computing, international conference, UBICOMP 2009, Orlando, Florida, USA, September 30 - October 3, pp 21–30Google Scholar
  12. 12.
    Lee JK, Hou JC (2006) Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application. ACM International Symposium on Mobile Ad Hoc Networking & Computing, pp 85–96Google Scholar
  13. 13.
    Liao Y, Lam W, Bing L et al (2018) Joint modeling of participant influence and latent topics for recommendation in event-based social networks. Acm Trans Inf Syst 36(3):1–31CrossRefGoogle Scholar
  14. 14.
    Ma Y, Lin T, Cao Z, Li C (2015) Mobility viewer: an eulerian approach for studying urban crowd flow. IEEE Trans Intell Transp Syst 17(9):2627–2636CrossRefGoogle Scholar
  15. 15.
    Min Y, Li Y (2015) Vehicles recognition based on the size characteristics and the CURE clustering algorithm. In: IEEE international conference on signal processing, communications and computing (ICSPCC 2015), pp 1–5, Ningbo, Zhejiang, ChinaGoogle Scholar
  16. 16.
    Selassie D, Heller B, Heer J (2011) Divided edge bundling for directional network data. IEEE Trans Vis Comput Graph 17(12):2354–2363CrossRefGoogle Scholar
  17. 17.
    Steenbruggen J, Borzacchiello MT, Nijkamp P, Scholten H (2013) Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities. GeoJournal 78(2):223–243CrossRefGoogle Scholar
  18. 18.
    Toole JL, Colak S, Sturt B, Alexander LP, Evsukoff A, Gonzlez MC (2015) The path most traveled: Travel demand estimation using big data resources. Trans Res Part C Emerg Technol 58:162–177CrossRefGoogle Scholar
  19. 19.
    von Landesberger T, Brodkorb F, Roskosch P, Andrienko N, Andrienko G, Kerrenr A (2016) Mobilitygraphs: visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Trans Vis Comput Graph 22(1):11–20CrossRefGoogle Scholar
  20. 20.
    Wang P, Hunter T, Bayen AM, Schechtner K, Gonzlez MC (2012) Understanding road usage patterns in urban areas. Sci Rep 2:1001CrossRefGoogle Scholar
  21. 21.
    Wu W, Xu J, Zeng H, Zheng Y (2016) Telcovis: visual exploration of co-occurrence in urban human mobility based on Telco data. IEEE Trans Vis Comput Graph 22(1):935–944CrossRefGoogle Scholar
  22. 22.
    Xiong H, Zhang D, Zhang D, Gauthier V (2012) Predicting mobile phone user locations by exploiting collective behavioral patterns. In: The 9th international conference on ubiquitous intelligence & computing and 9th international conference on autonomic & trusted computing (UIC/ATC). IEEE Computer Society Press, Los Alamitos, pp 164–171Google Scholar
  23. 23.
    Zhang Y (2014) User mobility from the view of cellular data networks. In: The 33rd annual IEEE conference on computer communications. IEEE Computer Society Press, New York, pp 1348–1356Google Scholar
  24. 24.
    Zhu T, Song Z, Wu D, Yu J (2016) A novel freeway traffic speed estimation model with massive cellular signaling data. Int J Web Serv Res 13(1):69–87CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Computer and Information EngineeringBeijing Technology and Business UniversityBeijingChina
  2. 2.National Engineering Laboratory for Agri-product Quality TraceabilityBeijingChina
  3. 3.Beijing Key Laboratory of Big Data Technology for Food SafetyBeijingChina
  4. 4.School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations