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Doraiswamy, Harish; Ferreira, Nivan; Damoulas, Theodoros; Freire, Juliana; Silva, Claudio T. (2014)
Publisher: Institute of Electrical and Electronics Engineers
Languages: English
Types: Article
Subjects: HT
The explosion in the volume of data about urban environments has opened up opportunities to inform both policy and administration and thereby help governments improve the lives of their citizens, increase the efficiency of public services, and reduce the environmental harms of development. However, cities are complex systems and exploring the data they generate is challenging. The interaction between the various components in a city creates complex dynamics where interesting facts occur at multiple scales, requiring users to inspect a large number of data slices over time and space. Manual exploration of these slices is ineffective, time consuming, and in many cases impractical. In this paper, we propose a technique that supports event-guided exploration of large, spatio-temporal urban data. We model the data as time-varying scalar functions and use computational topology to automatically identify events in different data slices. To handle a potentially large number of events, we develop an algorithm to group and index them, thus allowing users to interactively explore and query event patterns on the fly. A visual exploration interface helps guide users towards data slices that display interesting events and trends. We demonstrate the effectiveness of our technique on two different data sets from New York City (NYC): data about taxi trips and subway service. We also report on the feedback we received from analysts at different NYC agencies.\ud
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] P. K. Agarwal, H. Edelsbrunner, J. Harer, and Y. Wang. Extreme Elevation on a 2-manifold. Disc. Comput. Geom., 36(4):553-572, 2006.
    • [2] G. Andrienko and N. Andrienko. Spatio-temporal Aggregation for Visual Analysis of Movements. In Proc. of IEEE VAST, pages 51-58, 2008.
    • [3] G. Andrienko, N. Andrienko, P. Bak, D. Keim, and S. Wrobel. Visual Analytics Focusing on Spatial Events. In Visual Analytics of Movement, pages 209-251. Springer Berlin Heidelberg, 2013.
    • [4] G. Andrienko, N. Andrienko, G. Fuchs, A.-M. O. Raimond, J. Symanzik, and C. Ziemlicki. Extracting Semantics of Individual Places from Movement Data by Analyzing Temporal Patterns of Visits. In Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place, COMP '13, pages 9:9-9:16. ACM, 2013.
    • [5] G. Andrienko, N. Andrienko, C. Hurter, S. Rinzivillo, and S. Wrobel. From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data. In Proc. of IEE VAST 2011, pages 161-170. IEEE, 2011.
    • [6] G. Andrienko, N. Andrienko, C. Hurter, S. Rinzivillo, and S. Wrobel. Scalable Analysis of Movement Data for Extracting and Exploring Significant Places. IEEE TVCG, 19(7):1078-1094, July 2013.
    • [7] T. F. Banchoff. Critical Points and Curvature for Embedded Polyhedral Surfaces. Am. Math. Monthly, 77:475-485, 1970.
    • [8] P.-T. Bremer, G. Weber, V. Pascucci, M. Day, and J. Bell. Analyzing and Tracking Burning Structures in Lean Premixed Hydrogen Flames. IEEE TVCG, 16(2):248-260, Mar. 2010.
    • [9] H. Bunke and K. Shearer. A Graph Distance Metric Based on the Maximal Common Subgraph. Pattern Recogn. Lett., 19(3):255-259, 1998.
    • [10] H. Carr, J. Snoeyink, and U. Axen. Computing Contour Trees in All Dimensions. Comput. Geom. Theory Appl., 24(2):75-94, 2003.
    • [11] H. Carr, J. Snoeyink, and M. van de Panne. Simplifying Flexible Isosurfaces Using Local Geometric Measures. In Proc. IEEE Visualization, pages 497-504, 2004.
    • [12] J. Chae, D. Thom, H. Bosch, Y. Jang, R. Maciejewski, D. S. Ebert, and T. Ertl. Spatiotemporal Social Media Analytics for Abnormal Event Detection and Examination using Seasonal-Trend Decomposition. In Proc. of IEEE VAST 2012, pages 143-152. IEEE, 2012.
    • [13] V. Chandola, A. Banerjee, and V. Kumar. Anomaly Detection: A Survey. ACM Computing Surveys (CSUR), 41(3):15, 2009.
    • [14] R. Chang, G. Wessel, R. Kosara, E. Sauda, and W. Ribarsky. Legible Cities: Focus-Dependent Multi-Resolution Visualization of Urban Relationships. IEEE TVCG, 13(6):1169-1175, 2007.
    • [15] Y.-J. Chiang, T. Lenz, X. Lu, and G. Rote. Simple and Optimal OutputSensitive Construction of Contour Trees Using Monotone Paths. Comput. Geom. Theory Appl., 30(2):165-195, 2005.
    • [16] T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. MIT Press, 2001.
    • [17] H. Doraiswamy, V. Natarajan, and R. S. Nanjundiah. An Exploration Framework to Identify and Track Movement of Cloud Systems. IEEE TVCG, 19(12):2896-2905, 2013.
    • [18] H. Edelsbrunner and J. Harer. Persistent Homology - A Survey. In J. E. Goodman, J. Pach, and R. Pollack, editors, Surveys on Discrete and Computational Geometry. Twenty Years Later, pages 257-282. Amer. Math. Soc., Providence, Rhode Island, 2008. Contemporary Mathematics 453.
    • [19] H. Edelsbrunner and J. Harer. Computational Topology: An Introduction. Amer. Math. Soc., Providence, Rhode Island, 2009.
    • [20] H. Edelsbrunner, J. Harer, V. Natarajan, and V. Pascucci. Morse-Smale Complexes for Piecewise Linear 3-Manifolds. In Proc. Symp. Comput. Geom., pages 361-370, 2003.
    • [21] H. Edelsbrunner, D. Letscher, and A. Zomorodian. Topological Persistence and Simplification. Disc. Comput. Geom., 28(4):511-533, 2002.
    • [22] N. Ferreira, J. Poco, H. T. Vo, J. Freire, and C. T. Silva. Visual Exploration of Big Spatio-temporal Urban Data: A Study of New York City Taxi Trips. IEEE TVCG, 19(12):2149-2158, 2013.
    • [23] A. T. Fomenko and T. L. Kunii, editors. Topological Modeling for Visualization. Springer Verlag, 1997.
    • [24] H. L. Gantt. Work, Wages, and Profits. Engineering Magazine Co., 1913.
    • [25] Y. Gu and C. Wang. itree: Exploring Time-Varying Data Using Indexable Tree. In IEEE PacificVis, pages 137-144, 2013.
    • [26] J. Gudmundsson, P. Laube, and T. Wolle. Computational Movement Analysis. In Springer Handbook of Geographic Information, pages 423- 438. Springer, 2012.
    • [27] A. Hatcher. Algebraic Topology. Cambridge U. Press, New York, 2002.
    • [28] M. Hoai and F. De la Torre. Max-Margin Early Event Detectors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2863-2870, 2012.
    • [29] M. Hu, S. Liu, F. Wei, Y. Wu, J. Stasko, and K.-L. Ma. Breaking News on Twitter. In Proceedings of the ACM annual conference on Human Factors in Computing Systems, pages 2751-2754, 2012.
    • [30] H. Janetzko, F. Stoffel, S. Mittelsta┬Ędt, and D. A. Keim. Anomaly Detection for Visual Analytics of Power Consumption Data. Computers & Graphics, 38:27-37, 2014.
    • [31] J. Kasten, I. Hotz, B. Noack, and H.-C. Hege. Vortex merge graphs in two-dimensional unsteady flow fields. In EuroVis - Short Papers, pages 1-5, 2012.
    • [32] M. Kulldorff. A Spatial Scan Statistic. Communications in StatisticsTheory and methods, 26(6):1481-1496, 1997.
    • [33] M. Kulldorff, F. Mostashari, L. Duczmal, W. Katherine Yih, K. Kleinman, and R. Platt. Multivariate Scan Statistics for Disease Surveillance. Statistics in Medicine, 26(8):1824-1833, 2007.
    • [34] D. Laney, P. T. Bremer, A. Mascarenhas, P. Miller, and V. Pascucci. Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities. IEEE TVCG, 12(5):1053-1060, Sept. 2006.
    • [35] L. Lins, J. T. Klosowski, and C. Scheidegger. Nanocubes for Real-Time Exploration of Spatiotemporal Datasets. IEEE TVCG, 19(12):2456- 2465, 2013.
    • [36] S. Maadasamy, H. Doraiswamy, and V. Natarajan. A Hybrid Parallel Algorithm for Computing and Tracking Level Set Topology. In Proc. Intl. Conf. High Performance Computing, pages 12.1-12.10, 2012.
    • [37] R. Maciejewski, S. Rudolph, R. Hafen, A. Abusalah, M. Yakout, M. Ouzzani, W. S. Cleveland, S. J. Grannis, M. Wade, and D. S. Ebert. Understanding Syndromic Hotspots-A Visual Analytics Approach. In Proc. of IEEE VAST 2008, pages 35-42. IEEE, 2008.
    • [38] E. McFowland III, S. Speakman, and D. B. Neill. Fast Generalized Subset Scan for Anomalous Pattern Detection. Journal of Machine Learning Research, 14:1533-1561, 2013.
    • [39] J. Milnor. Morse Theory. Princeton Univ. Press, New Jersey, 1963.
    • [40] NYC MTA API. http://web.mta.info/developers/.
    • [41] D. B. Neill and G. F. Cooper. A Multivariate Bayesian Scan Statistic for Early Event Detection and Characterization. Machine learning, 79(3):261-282, 2010.
    • [42] D. B. Neill, A. W. Moore, and G. F. Cooper. A Bayesian Spatial Scan Statistic. Adv. Neur. In., 18:1003, 2006.
    • [43] Chicago Open Data. https://data.cityofchicago.org/.
    • [44] NYC Open Data. http://data.ny.gov.
    • [45] Seattle Open Data. http://data.seattle.gov.
    • [46] V. Pascucci and K. Cole-McLaughlin. Parallel Computation of the Topology of Level Sets. Algorithmica, 38(1):249-268, 2003.
    • [47] V. Pascucci, X. Tricoche, H. Hagen, and J. Tierny, editors. Topological Methods in Data Analysis and Visualization. Springer, 2010.
    • [48] V. Pascucci, G. Weber, J. Tierny, P.-T. Bremer, M. Day, and J. Bell. Interactive Exploration and Analysis of Large-Scale Simulations Using Topology-Based Data Segmentation. IEEE TVCG, 17(9):1307-1324, 2011.
    • [49] R. E. Roth. An Empirically-Derived Taxonomy of Interaction Primitives for Interactive Cartography and Geovisualization. IEEE TVCG, 19(12):2356-2365, 2013.
    • [50] R. W. Scholz and Y. Lu. Detection of Dynamic Activity Patterns at a Collective Level from Large-Volume Trajectory Data. International Journal of Geographical Information Science, (ahead-of-print):1-18, 2014.
    • [51] G.-D. Sun, Y.-C. Wu, R.-H. Liang, and S.-X. Liu. A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges. J. of Comp. Sci. and Tech., 28(5):852-867, 2013.
    • [52] Twitter API. https://dev.twitter.com/.
    • [53] J. Wakefield and A. Kim. A Bayesian Model for Cluster Detection. Biostatistics, 14(4):752-765, 2013.
    • [54] Z. Wang, M. Lu, X. Yuan, J. Zhang, and H. v. d. Wetering. Visual Traffic Jam Analysis Based on Trajectory Data. IEEE TVCG, 19(12):2159-2168, 2013.
    • [55] W. Widanagamaachchi, C. Christensen, P.-T. Bremer, and V. Pascucci. Interactive Exploration of Large-Scale Time-Varying Data Using Dynamic Tracking Graphs. In Proc. of IEEE LDAV, pages 9-17, 2012.
    • [56] P. H. T. Zannin, M. S. Engel, P. E. K. Fiedler, and F. Bunn. Characterization of Environmental Noise Based on Noise Measurements, Noise Mapping and Interviews: A Case Study at a University Campus in Brazil. Cities, 31:317-327, 2013.
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