EcoLens: visual analysis of ecological regions in urban contexts using traffic data


The increasing availability of spatiotemporal data provides unprecedented opportunities for understanding the structure of an urban area in terms of people’s activity pattern and how they form the latent regions over time. However, existing solutions are limited in their capacity of capturing the evolutionary patterns of dynamic latent regions within urban context. In this work, we introduce an interactive visual analysis approach, EcoLens, that allows analysts to progressively explore and analyze the complex dynamic segmentation patterns of a city using traffic data. We propose an extended nonnegative matrix factorization-based algorithm smoothed over both spatial and temporal dimensions to capture the spatiotemporal dynamics of the city. The algorithm also ensures the orthogonality of its result to facilitate the interpretation of different patterns. A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time. We evaluate the effectiveness of our system via case studies using a real-world dataset and a qualitative interview with the domain expert.

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  1. 1.

  2. 2.


  1. Aigner W, Miksch S, Schumann H, Tominski C (2011) Visualization of time-oriented data. Springer, Berlin

    Google Scholar 

  2. Andrienko G, Andrienko N, Bak P, Keim D, Wrobel S (2013) Visual analytics of movement. Springer, Berlin

    Google Scholar 

  3. Andrienko G, Andrienko N, Fuchs G, Wood J (2017) Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data. IEEE Trans Visual Comput Graphics 23(9):2120–2136

    Article  Google Scholar 

  4. Bakıcı T, Almirall E, Wareham J (2013) A smart city initiative: the case of barcelona. Journal of the Knowledge Economy 4(2):135–148

    Article  Google Scholar 

  5. Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799

    Article  Google Scholar 

  6. Demšar U, Reades J, Manley E, Batty M (2017) Revisiting the past: replicating fifty-year-old flow analysis using contemporary taxi flow data. Ann Am Assoc Geogr: 1–18

  7. Deng JS, Wang K, Hong Y, Qi JG (2009) Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landsc Urban Plan 92(3):187–198

    Article  Google Scholar 

  8. Dunteman GH (1989) Principal components analysis, vol 69. Sage, Thousand Oaks

    Google Scholar 

  9. Goddard JB (1970) Functional regions within the city centre: a study by factor analysis of taxi flows in Central London. Trans Inst Br Geogr 49:161–182

    Article  Google Scholar 

  10. Kim J, He Y, Park H (2014) Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework. J Glob Optim 58(2):285–319

    MathSciNet  Article  Google Scholar 

  11. Kloeckl K, Senn O, Di Lorenzo G, Ratti C (2011) Live singapore!-an urban platform for real-time data to program the city. Comput Urban Plan Urban Manag CUPUM 4

  12. Kraft S, Marada M (2017) Delimitation of functional transport regions: understanding the transport flows patterns at the micro-regional level. Geografiska Annaler Ser B Hum Geogr 99(1):79–93

    Article  Google Scholar 

  13. Kuhn HW (1955) The Hungarian method for the assignment problem. Nav Res Logist (NRL) 2(1–2):83–97

    MathSciNet  Article  Google Scholar 

  14. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791

    Article  Google Scholar 

  15. Robertson G, Fernandez R, Fisher D, Lee B, Stasko J (2008) Effectiveness of animation in trend visualization. IEEE Trans Vis Comput Graph 14(6):1325–1332

    Article  Google Scholar 

  16. Rosling H (2009) Gapminder, vol 91. GapMinder Foundation, Stockholm

    Google Scholar 

  17. Seto KC, Fragkias M (2005) Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landsc Ecol 20(7):871–888

    Article  Google Scholar 

  18. Su K, Li J, Fu H (2011) Smart city and the applications. In: International conference on electronics, communications and control. IEEE, pp 1028–1031

  19. Tominski C, Schumann H, Andrienko G, Andrienko N (2012) Stacking-based visualization of trajectory attribute data. IEEE Trans Vis Comput Graph 18(12):2565–2574

    Article  Google Scholar 

  20. van den Elzen S, Holten D, Blaas J, van Wijk JJ (2016) Reducing snapshots to points: a visual analytics approach to dynamic network exploration. IEEE Trans Vis Comput Graph 22(1):1–10

    Article  Google Scholar 

  21. Wakamiya S, Lee R, Kawai Y, Sumiya K (2015) Twitter-based urban area characterization by non-negative matrix factorization. In: Proceedings of the international conference on big data applications and services. ACM, pp 128–135

  22. Wang J, Gao F, Cui P, Li C, Xiong Z (2014) Discovering urban spatio-temporal structure from time-evolving traffic networks. In: Asia-pacific web conference. Springer, pp 93–104

  23. Wu W, Zheng Y, Qu H, Chen W, Gröller E, Ni LM (2014) Boundaryseer: visual analysis of 2d boundary changes. In: IEEE conference on visual analytics science and technology, pp 143–152

  24. Wu W, Xu J, Zeng H, Zheng Y, Qu H, Ni B, Yuan M, Ni LM (2016) Telcovis: visual exploration of co-occurrence in urban human mobility based on telco data. IEEE Trans Vis Comput Graph 22(1):935–944

    Article  Google Scholar 

  25. Wu W, Zheng Y, Cao N, Zeng H, Ni B, Qu H, Ni LM (2017) Mobiseg: interactive region segmentation using heterogeneous mobility data. In: IEEE pacific visualization symposium

  26. Yuan NJ, Zheng Y, Xie X, Wang Y, Zheng K, Xiong H (2015) Discovering urban functional zones using latent activity trajectories. IEEE Trans Knowl Data Eng 27(3):712–725

    Article  Google Scholar 

  27. Zhang K, Sun D, Shen S, Zhu Y (2017) Analyzing spatiotemporal congestion pattern on urban roads based on taxi gps data. J Transp Land Use 10(1):675–694

    Google Scholar 

  28. Zhao Y, Luo X, Lin X, Wang H, Kui X, Zhou F, Wang J, Chen Y, Chen W (2019) Visual analytics for electromagnetic situation awareness in radio monitoring and management. IEEE Trans Vis Comput Graph 26(1):590–600

    Article  Google Scholar 

  29. Zheng Y, Wu W, Chen Y, Qu H, Ni LM (2016a) Visual analytics in urban computing: an overview. IEEE Trans Big Data 2(3):276–296

    Article  Google Scholar 

  30. Zheng Y, Wu W, Zeng H, Cao N, Qu H, Yuan M, Zeng J, Ni LM (2016b) Telcoflow: visual exploration of collective behaviors based on telco data. In: IEEE international conference on big data, pp 843–852

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This work was supported in part by the Open Project Program of the State Key Lab of CAD&CG (Grant No. A2013), Zhejiang University, the National Natural Science Foundation of China (Grant No. 61802283), the Fundamental Research Funds for the Central Universities in China, and the Natural Science Foundation of Shanghai, China (Grant No. 20ZR1461500).

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Correspondence to Yang Shi.

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Jin, Z., Cao, N., Shi, Y. et al. EcoLens: visual analysis of ecological regions in urban contexts using traffic data. J Vis (2020).

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  • Visual analysis
  • Urban segmentation
  • Matrix factorization
  • Traffic data