Skip to main content
SpringerLink
Log in

MViewer: mobile phone spatiotemporal data viewer

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Nowadays movement patterns and people’s behavioral models are needed for traffic engineers and city planners. These observations could be used to reason about mobility and its sustainability and to support decision makers with reliable information. The very same knowledge about human diaspora and behavior extracted from these data is also valuable to the urban planner, so as to localize new services, organize logistics systems and to detect changes as they occur in the movement behavior. Moreover, it is interesting to investigate movement in places like a shopping area or a working district either for commercial purposes or for improving the service quality. These kinds of tracking data are made available by wireless and mobile communication technologies. It is now possible to record and collect a large amount of mobile phone calls in a city. Technologies for object tracking have recently become affordable and reliable and hence we were able to collect mobile phone data from a city in China from January 1, 2008 to December 31, 2008. The large amount of phone call records from mobile operators can be considered as life mates and sensors of persons to inform howmany people are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by analyzing mobile phone data. Through mobile phones, we can learn the information about locations, communications between mobile phone users during their daily lives. In this work, we propose a comprehensive visual analysis system named as MViewer, Mobile phone spatiotemporal data Viewer, which is the first system to visualize and analyze the population’smobility patterns from millions of phone call records. Our system consists of three major components: 1) visual analysis of user groups in a base station; 2) visual analysis of the mobility patterns on different user groups making phone calls in certain base stations; 3) visual analysis of handoff phone call records. Some well-established visualization techniques such as parallel coordinates and pixelbased representations have been integrated into our system. We also develop a novel visualization schemes, Voronoidiagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone datasets that are kindly provided by our project partners and obtained some interesting findings regarding people’s mobility patterns.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Statistical communique of telecommunication industry development in china. Technical report, 2008

  2. Liu S, Chen L, Ni L M, Fan J. CIM: categorical influence maximization. In: Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication (ICUIMC), 2011, 124

    Google Scholar 

  3. Liu S, Liu Y, Ni L M, Fan J, Li M. Towards mobility-based clustering. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, 919–928

    Chapter  Google Scholar 

  4. Chen C. Top 10 unsolved information visualization problems. Computer Graphics and Applications, IEEE, 2005, 25(4): 12–16

    Article  Google Scholar 

  5. Keim D, Mansmann F, Schneidewind J, Ziegler H. Challenges in visual data analysis. In: Proceedings of the 10th International Conference on. Information Visualization, 2006, 9–16

    Google Scholar 

  6. Card S K, Mackinlay J D, Shneiderman B, et al. Readings in Information Visualization: Using Vision to Think. San Francisco: Morgan Kaufmann Publishers Inc., 1999

    Google Scholar 

  7. Treisman A. Preattentive processing in vision. Computer Vision, Graphics and Image Processing, 1985, 31(2): 156–177

    Article  MathSciNet  Google Scholar 

  8. Ware C. Information Visualization: Perception for Design. San Francisco: Morgan Kaufmann Publishers Inc., 2004

    Google Scholar 

  9. Ratti C, Pulselli R M, Williams S, Frenchman D. Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 2006, 33(5): 727–748

    Article  Google Scholar 

  10. Calabrese F, Ratti C. Real time rome. Networks and Communication Studies, 2006, 20: 247–258

    Google Scholar 

  11. Calabrese F, Reades J, Ratti C. Eigenplaces: analyzing cities using the space-time structure of the mobile phone network Eigenplaces: analyzing cities using the space-time structure of the mobile phone network. Environment and Planning B: Planning and Design, 2009

    Google Scholar 

  12. Girardin F, Blat J, Calabrese F, Dal Fiore F, Ratti C. Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Computing, 2008, 7(4): 78–85

    Article  Google Scholar 

  13. Girardin F, Vaccari A, Gerber A, Biderman A, Ratti C. Towards estimating the presence of visitors from the aggregate mobile phone network activity they generate. In: Proceedings of the 11th International Conference on Computers in Urban Planning and Urban Management, 2009

    Google Scholar 

  14. Krisp J. Planning fire and rescue services by visualizing mobile phone density. Journal of Urban Technology, 2010, 17(1): 61–69

    Article  Google Scholar 

  15. Ahas R, Silm S, Järv O, Saluveer E, Tiru M. Using mobile positioning data to model locations meaningful to users of mobile phones. Journal of Urban Technology, 2010, 17(1): 3–27

    Article  Google Scholar 

  16. Andrienko G, Andrienko N, Mladenov M, Mock M, Pölitz C. Discovering bits of place histories from people’s activity traces. In: Proceedings of the IEEE Visual Analytics Science and Technology (VAST 2010), 2010, 59–66

    Chapter  Google Scholar 

  17. Gonzalez M C, Hidalgo C A, Barabasi A L. Understanding individual human mobility patterns. Nature, 2008, 453(7196): 479–482

    Article  Google Scholar 

  18. Song C, Qu Z, Blumm N, Barabási A L. Limits of predictability in human mobility. Science, 2010, 327: 1018–1021

    Article  MATH  MathSciNet  Google Scholar 

  19. Vaccari A, Liu L, Biderman A, Ratti C, Pereira F, Oliveirinha J, Gerber A. A holistic framework for the study of urban traces and the profiling of urban processes and dynamics. In: Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, 2009, 1–6

    Google Scholar 

  20. Ratti C, Sevtsuk A, Huang S, Pailer R. Mobile landscapes: Graz in real time. Location Based Services and TeleCartography, 2007: 433–444

    Chapter  Google Scholar 

  21. Dorling D, Barford A, Newman M. WORLDMAPPER: the world as you’ve never seen it before. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 757–764

    Article  Google Scholar 

  22. Roth R E, Robinson A, Stryker M, MacEachren A M, Lengerich E J, Koua E. Web-based geovisualization and geocollaboration: applications to public health. In: Proceedings of the 2008 Joint Statistical Meeting Invited Session on Web Mapping, 2008, (1): 2–5

    Google Scholar 

  23. Mehler A, Bao Y, Li X, Wang Y, Skiena S. Spatial analysis of news sources. IEEE transactions on Visualization and Computer Graphics, 2006, 12(5): 765–771

    Article  Google Scholar 

  24. Wood J, Dykes J, Slingsby A, Clarke K. Interactive visual exploration of a large spatio-temporal dataset: reflections on a geovisualization mashup. IEEE Transactions on Visualization and Computer Graphics, 2007, 13(6): 1176–1183

    Article  Google Scholar 

  25. Fisher D. Hotmap: looking at geographic attention. IEEE Transactions on Visualization and Computer Graphics, 2007, 13(6): 1184–1191

    Article  Google Scholar 

  26. Chang R, Wessel G, Kosara R, Sauda E, Ribarsky W. Legible cities: focus-dependent multi-resolution visualization of urban relationships. IEEE Transactions on Visualization and Computer Graphics, 2007, 13(6): 1169–1175

    Article  Google Scholar 

  27. Andrienko G, Andrienko N, Dykes J, Fabrikant S I. Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization. Cartography, 2008

    Google Scholar 

  28. Bak P, Mansmann F, Janetzko H, Keim D A. Spatiotemporal analysis of sensor logs using growth ring maps. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(6): 913–920

    Article  Google Scholar 

  29. Crnovrsanin T, Muelder C, Correa C, Ma K L. Proximity-based visualization of movement trace data. IEEE Conference on Visual Analytics Science and Technology (VAST 2009), 2009, 11–18

    Chapter  Google Scholar 

  30. Yeh R, Hanrahan P, Winograd T. Flow map layout. In: Proceedings of the IEEE Symposium on Information Visualization 2005 INFOVIS 2005, 2005, 17(c): 219–224

    Google Scholar 

  31. Baert A E, Sem D, Picardie D, Verne J. Voronoï mobile cellular networks: topological properties. In: Proceedings of the 3rd International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks, 2004

    Google Scholar 

  32. Portela J N, Fed C, Tec D E. Cellular network as a multiplicatively weighted voronoi diagram. In: Proceedings of the 3rd IEEE Consumer Communications and Networking Conference, 2006, 2: 913–917

    Google Scholar 

  33. http://www.cse.ust.hk/scrg/

  34. Shneiderman B. The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages. 1996, 336–343

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Hong Kong University of Science and Technology, Hong Kong, 999077, China

    Jiansu Pu, Panpan Xu, Huamin Qu & Lionel M. Ni

  2. iLab, Heinz College, Carnegie Mellon University, Pittsburgh, PA, 15601, USA

    Siyuan Liu

Authors
  1. Jiansu Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Panpan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huamin Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lionel M. Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiansu Pu.

Additional information

Jiansu Pu received bachelor degree in computer science and technology from Beihang University, China in 2008. He received his M.Phil degree in 2010 and PhD degree in 2013 both from Department of Computer Science and Engineering at Hong Kong University of Science and Technology (HKUST) China. His research interests are in information visualization, visual analytics, and spatio-temporal data analysis.

Siyuan Liu is a research scientist at Carnegie Mellon University, USA and received his PhD degree from Department of Computer Science and Engineering at Hong Kong University of Science and Technology in 2011, China. His research interests include data mining in spatiotemporal data, time series, and social networks.

Panpan Xu received bachelor degree in computer science and technology from Zhejiang University, China in 2009. She is a PhD candidate in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology, China. Her research interests are in information visualization and visual analytics.

Huamin Qu is an associate professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology, China. His main research interests are in visualization and computer graphics. He has co-authored more than 60 refereed papers including 19 papers in the IEEE Transactions on Visualization and Computer Graphics. He received Honorable Mention for Best Paper Award at IEEE Visualization 2009 and is a winner of 2009 IBM Faculty Award. He obtained a PhD (2004) in computer science from Stony Brook University.

Lionel M. Ni is a chair professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology, China. He also serves as the special assistant to the president of HKUST, dean of HKUST Fok Ying Tung Graduate School and visiting chair professor of Shanghai Key Lab of Scalable Computing and Systems at Shanghai Jiao Tong University. As a fellow of IEEE, Dr. Ni has chaired over 30 professional conferences and has received six awards for authoring outstanding papers.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pu, J., Liu, S., Xu, P. et al. MViewer: mobile phone spatiotemporal data viewer. Front. Comput. Sci. 8, 298–315 (2014). https://doi.org/10.1007/s11704-013-3009-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-013-3009-2

Keywords

Search

Navigation