Journal of Visualization

, Volume 20, Issue 2, pp 183–194 | Cite as

Spatial–temporal visualization of city-wide crowd movement

  • Feiran Wu
  • Minfeng Zhu
  • Qi Wang
  • Xin Zhao
  • Wei Chen
  • Ross Maciejewski
Regular Paper

Abstract

Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macro-patterns of crowd movement, or analyze the trajectory of an individual object; however, they focus on geographical characteristics. In this paper, we propose a novel data representation, the mobility transition graph, to characterize spatio-temporal mobility transition of crowd from city-wide human mobility data. We describe the design, creation, and manipulation of the mobility transition graph and demonstrate the efficiency of our approach by a case study.

Graphical abstract

Keywords

Mobility modeling Multi-modal information visualization Spatial–temporal visual analysis 

Notes

Acknowledgments

This work is supported by the National 973 Program of China (2015CB352503), National Natural Science Foundation of China (61232012, 61422211), and the Fundamental Research Funds for the Central Universities. Ross Maciejewski is supported by the National Science Foundation under Grant No. 1350573.

References

  1. Barabasi AL (2005) The origin of bursts and heavy tails in human dynamics. Nature 435(7039):207–211CrossRefGoogle Scholar
  2. Chen W, Guo F, Wang F (2015) A survey of traffic data visualization. IEEE Trans Vis Comput Gr 16(6):2970–2984Google Scholar
  3. Cui W, Liu S, Tan L, Shi C, Song Y, Gao Z, Qu H, Tong X (2011) Textflow: towards better understanding of evolving topics in text. IEEE Trans Vis Comput Gr 17(12):2412–2421CrossRefGoogle Scholar
  4. de Montjoye YA., Hidalgo CA., Verleysen M, Blondel VD (2013) Unique in the crowd: The privacy bounds of human mobility. Scientific reports 3Google Scholar
  5. Doraiswamy H, Ferreira N, Damoulas T, Freire J, Silva CT (2014) Using topological analysis to support event-guided exploration in urban data. IEEE Trans Vis Comput Gr 20(12):2634–2643CrossRefGoogle Scholar
  6. Gao J, Goodman J, Cao G, Li H (2002) Exploring asymmetric clustering for statistical language modeling. In: Proceedings of the Association for Computational Linguistics, pp 183–190Google Scholar
  7. Gonzalez MC, Hidalgo CA, Barabasi AL (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782CrossRefGoogle Scholar
  8. Havre S, Hetzler B, Nowell L (2000) ThemeRiver: visualizing theme changes over time. In: IEEE Symposium on Information Visualization, pp 115–123Google Scholar
  9. Liao ZF, Li Y, Peng Y, Zhao Y, Zhao FF, Liao ZN, Dudley S, Ghavami M (2015) A semantic-enhanced trajectory visual analytics for digital forensic. J Vis 18(2):173–184CrossRefGoogle Scholar
  10. Liu S, Wu Y, Wei E, Liu M, Liu Y (2013) Storyflow: tracking the evolution of stories. IEEE Trans Vis Comput Gr 19(12):2436–2445CrossRefGoogle Scholar
  11. Lu X, Bengtsson L, Holme P (2012) Predictability of population displacement after the 2010 haiti earthquake. Proc Natl Acad Sci 109(29):11576–11581CrossRefGoogle Scholar
  12. Ma Y, Lin T, Cao Z, Li C (2015) Mobility viewer: an eulerian approach for studying urban crowd flow. IEEE Trans Intell Trans Syst pp 1–10Google Scholar
  13. Ogawa M, Ma KL (2010) Software evolution storylines. In: International Symposium on Software visualization, pp 35–42Google Scholar
  14. Schneider CM, Belik V, Couronné T, Smoreda Z, González MC (2013) Unravelling daily human mobility motifs. J R Soc Interface 10(84):2013–2046CrossRefGoogle Scholar
  15. Song C, Koren T, Wang P, Barabási AL (2010) Modelling the scaling properties of human mobility. Nat Phys 6(10):818–823CrossRefGoogle Scholar
  16. Song C, Qu Z, Blumm N, Barabási AL (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021MathSciNetCrossRefMATHGoogle Scholar
  17. Song L, Kolar M, Xing EP (2009) Time-varying dynamic bayesian networks. In: Advances in neural information processing systems, pp 1732–1740Google Scholar
  18. Sun G, Wu Y, Liu S, Peng TQ, Zhu JJ, Liang R (2014) EvoRiver: visual analysis of topic coopetition on social media. IEEE Trans Vis Comput Gr 20:(12)Google Scholar
  19. Tanahashi Y, Ma KL (2012) Design considerations for optimizing storyline visualizations. IEEE Trans Vis Comput Gr 18(12):2679–2688CrossRefGoogle Scholar
  20. von Landesberger T, Brodkorb F, Roskosch P, Andrienko N, Andrienko G, Kerren A (2016) Mobilitygraphs: visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Trans Vis Comput Gr 22(1):11–20CrossRefGoogle Scholar
  21. Wang Z, Ye T, Lu M, Yuan X, Qu H, Yuan J, Wu Q (2014) Visual exploration of sparse traffic trajectory data. IEEE Trans Vis Comput Gr 20(12):1813–1822CrossRefGoogle Scholar
  22. Wang P, Hunter T, Bayen AM, Schechtner K, González MC (2012) Understanding road usage patterns in urban areas. Scientific reports 2Google Scholar
  23. Wang J, Wei D, He K, Gong H, Wang P (2014) Encapsulating urban traffic rhythms into road networks. Scientific reports 4Google Scholar
  24. Wongsuphasawat K, Gotz D (2012) Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. IEEE Trans Vis Comput Gr 18(12):2659–2668CrossRefGoogle Scholar
  25. Wu F, Zhu M, Zhao X, Wang Q, Chen W, Maciejewski R (2015) Visualizing the time-varying crowd mobility. In: SIGGRAPH Asia 2015 Visualization in High Performance Computing, SA ’15. ACM, New York, NY, USA, pp. 15:1–15:4. doi:10.1145/2818517.2818540.  http://doi.acm.org/10.1145/2818517.2818540
  26. Wu Y, Liu S, Yan K, Liu M, Wu F (2014) Opinionflow: Visual analysis of opinion diffusion on social media. IEEE Trans Vis Comput Gr 20:(12)Google Scholar
  27. Xiong H, Zhang D, Gauthier V (2012) Predicting mobile phone user locations by exploiting collective behavioral patterns. In: IEEE Conference on Ubiquitous Intelligence and Computing, pp 164–171Google Scholar
  28. Xu P, Wu Y, Wei E, Peng TQ, Liu S, Zhu JJ, Qu H (2013) Visual analysis of topic competition on social media. IEEE Trans Vis Comput Gr 19(12):2012–2021CrossRefGoogle Scholar
  29. Zeng W, Fu CW, Arisona SM, Erath A, Qu H (2014) Visualizing mobility of public transportation system. IEEE Trans Vis Comput Gr 20:12CrossRefGoogle Scholar
  30. Zhao J, Cao N, Wen Z, Song Y, Lin YR., Collins C (2014) FluxFlow: visual analysis of anomalous information spreading on social media. IEEE Trans Vis Comput Gr 20:(12)Google Scholar
  31. Zheng X, Chen W, Wang P, Shen D, Chen S, Wang X, Zhang Q, Yang L (2015) Big data for social transportation. IEEE Trans Intell Transp Syst pp 1–11Google Scholar
  32. Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol 5(3)Google Scholar

Copyright information

© The Visualization Society of Japan 2016

Authors and Affiliations

  • Feiran Wu
    • 1
  • Minfeng Zhu
    • 1
  • Qi Wang
    • 1
  • Xin Zhao
    • 2
  • Wei Chen
    • 1
  • Ross Maciejewski
    • 2
  1. 1.State Key Lab of CAD&CG, Innovation Joint Research Center for Cyber-Physical-Society System Zhejiang University Hangzhou China
  2. 2.The School of Computing, Informatics and Decision Systems EngineeringArizona State University TempeUSA

Personalised recommendations