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Visual analytics of bike-sharing data based on tensor factorization

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Abstract

Bike-sharing systems have grown tremendously worldwide in the recent years. Understanding the user activities in urban areas is invaluable, especially for bike rebalance and urban planning. However, it is difficult to directly capture the user activity patterns from the bike-sharing data due to its sparse and discontinuous characteristics. In the recent years, many methods have been explored to visualize the user activity patterns. Many previous methods focused on visually presenting the temporal and spatial distribution directly. In this paper, we construct a tensor based on the spatial, temporal, and user information of the bike-sharing data, and employ tensor factorization to extract latent user activity patterns. To facilitate the users to analyze and understand these patterns, a visual analytics system is designed to interactively explore these patterns from the spatial, temporal, and user dimensions and compare these patterns in/between cities. We demonstrate the effectiveness of our system via case studies with real-word datasets.

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References

  • Chen L, Yang D, Jakubowicz J, Pan G, Zhang D, Li S (2015) Sensing the pulse of urban activity centers leveraging bike sharing open data. In: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on scalable computing and communications and its associated workshops (UIC-ATC-ScalCom), p 135–142 (2015).

  • Chen L, Jakubowicz J, Yang D, Zhang D, Pan G (2016) Fine-grained urban event detection and characterization based on tensor cofactorization. In: IEEE Transactions on Human-Machine Systems

  • Wood J, Slingsby A, Dykes J (2011) Visualizing the dynamics of london’s bicycle-hire scheme. Cartographica 46(4):239–251

    Article  Google Scholar 

  • Hitchcock FL (1927) The expression of a tensor or a polyadic as a sum of products. J Math Phys 6(1):164–189

    Article  MATH  Google Scholar 

  • Tucker LR (1966) Some mathematical notes on three-mode factor analysis. Psychometrika 31(3):279–311

    Article  MathSciNet  Google Scholar 

  • Arroll JD, Chang JJ (1970) Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35(3):283–319

    Article  MATH  Google Scholar 

  • Harshman RA (1970) Foundations of the PARAFAC procedure: models and conditions for “explanatory” an multi-modal factor analysis. UCLA Working Papers in Phonetics 16(1), 84

  • Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500

    Article  MathSciNet  MATH  Google Scholar 

  • Liang Y, Caverlee J, Cheng Z, Kamath KY (2013) How big is the crowd?: Event and location based population modeling in social media. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media, HT ’13, p 99–108. ACM, New York

  • Zhang W, Qi G, Pan G, Lu H, Li S, Wu Z (2015) City-scale social event detection and evaluation with taxi traces. ACM Trans Intell Syst Technol 6(3):40:1–40:20

    Google Scholar 

  • Wang J, Gao F, Cui P, Li C, Xiong Z (2014) Discovering Urban Spatio-temporal structure from time-evolving traffic networks. Springer, Cham, pp 93–104

    Google Scholar 

  • Yang D, Zhang D, Zheng VW, Yu Z (2015) Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Trans. Systems, Man, and Cybernetics: Systems 45(1):129–142

  • Liu H, Gao Y, Lu L, Liu S, Qu H, Ni LM (2011) Visual analysis of route diversity. In: 2011 IEEE Conference on Visual Analytics Science and Technology, VAST 2011, Providence, Rhode Island, p 171–180

  • Wang Z, Yuan X (2014) Urban trajectory timeline visualization. In: International Conference on Big Data and Smart Computing, BIGCOMP 2014, Bangkok, Thailand, p 13–18

  • Guo H, Wang Z, Yu B, Zhao H, Yuan X (2011) Tripvista: Triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection. In: 2011 IEEE Pacific Visualization Symposium, p 163–170

  • Andrienko N, Andrienko G, Stange H, Liebig T, Hecker D (2012) Visual analytics for understanding spatial situations from episodic movement data. KI - Künstliche Intelligenz 26(3):241–251

    Article  Google Scholar 

  • Yang Y, Dwyer T, Goodwin S, Marriott K (2017) Many-to-many geographically-embedded flow visualisation: an evaluation. IEEE Trans Vis Comput Graph 23(1):411–420

    Article  Google Scholar 

  • Jiang X, Zheng C, Tian Y, Liang R (2015) Large-scale taxi o/d visual analytics for understanding metropolitan human movement patterns. J Vis 18(2):185–200

    Article  Google Scholar 

  • Lu M, Liang J, Wang Z, Yuan X (2016) Exploring od patterns of interested region based on taxi trajectories. J Vis 19(4):811–821

    Article  Google Scholar 

  • Wood J, Beecham R, Dykes J (2014) Moving beyond sequential design: reflections on a rich multi-channel approach to data visualization. IEEE Trans Vis Comput Graph 20(12):2171–2180

    Article  Google Scholar 

  • Wang Z, Lu M, Yuan X, Zhang J, van de Wetering H (2013) Visual traffic jam analysis based on trajectory data. IEEE Trans Vis Comput Graph 19(12):2159–2168

    Article  Google Scholar 

  • Liu D, Weng D, Li Y, Bao J, Zheng Y, Qu H, Wu Y (2017) Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans Vis Comput Graph 23(1):1–10

    Article  Google Scholar 

  • Chu D, Sheets DA, Zhao Y, Wu Y, Yang J, Zheng M, Chen G (2014) Visualizing hidden themes of taxi movement with semantic transformation. In: 2014 IEEE Pacific Visualization Symposium, p 137–144

  • Al-Dohuki S, Wu Y, Kamw F, Yang J, Li X, Zhao Y, Ye X, Chen W, Ma C, Wang F (2017) Semantictraj: a new approach to interacting with massive taxi trajectories. IEEE Trans Vis Comput Graph 23(1):11–20

    Article  Google Scholar 

  • iao ZF, Li Y, Peng Y, Zhao Y, Zhou FF, Liao ZN, Dudley S, Ghavami M (2015) A semantic-enhanced trajectory visual analytics for digital forensic. J Vis 18(2):173–184

    Article  Google Scholar 

  • Wu W, Zheng Y, Cao N, Zeng H, Ni B, Qu H, Ni LM (2017) Mobiseg: interactive region segmentation using heterogeneous mobility data. In: 2017 IEEE Pacific Visualization Symposium (PacificVis)

  • Chen W, Guo F, Wang FY (2015) A survey of traffic data visualization. IEEE Trans Intell Transp Syst 16(6):2970–2984

    Article  Google Scholar 

  • OpenStreetMap contributors: planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org (2017)

  • Bostock M, Ogievetsky V, Heer J (2011) D\({^3}\) data-driven documents. IEEE Trans Vis Comput Graph 17(12):2301–2309

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by National Natural Science Foundation of China no. 61472354 and 61672452, NSFC-Guangdong Joint Fund no. U1611263, and the Fundamental Research Funds for the Central Universities. Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org.

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Correspondence to Yubo Tao.

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Yan, Y., Tao, Y., Xu, J. et al. Visual analytics of bike-sharing data based on tensor factorization. J Vis 21, 495–509 (2018). https://doi.org/10.1007/s12650-017-0463-1

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