Using topological analysis to support event-guided exploration in urban data

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Doraiswamy, Harish ; Ferreira, Nivan ; Damoulas, Theodoros ; Freire, Juliana ; Silva, Claudio T. (2014)

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
  • References (56)
    56 references, page 1 of 6

    [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.

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