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Visualization of multi-dimensional sparse spatial-temporal data

  • 1.

    Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumchi 830011, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumchi 830011, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.07.003
  • Received Date: 28 August 2016
  • Rev Recd Date: 08 December 2016
  • Publish Date: 31 July 2017
    Abstract

  • Abstract

    Multi-dimensionality and sparseness of spatial-temporal data are major challenges for data analysis. Data visualization can effectively address certain data analysis challenges and has increasingly drawn attention from both industry and academia. A hybrid approach for the visualization of multi-dimensional sparse spatial-temporal data was proposed. The method combined multiple data view models and human-machine interaction mechanisms in order to intuitively express the multi-dimensional features, statistical group features, as well as typical individual behavior patterns. Furthermore, a visual analysis method was introduced for the identification and detection of abnormal individual behaviors. A data visualization system based on gas filling data gathered from gas stations in Xinjiang Province was implemented. By using different view models (parallel coordinates, map view, calendar matrix, Sankey

    Abstract

    Multi-dimensionality and sparseness of spatial-temporal data are major challenges for data analysis. Data visualization can effectively address certain data analysis challenges and has increasingly drawn attention from both industry and academia. A hybrid approach for the visualization of multi-dimensional sparse spatial-temporal data was proposed. The method combined multiple data view models and human-machine interaction mechanisms in order to intuitively express the multi-dimensional features, statistical group features, as well as typical individual behavior patterns. Furthermore, a visual analysis method was introduced for the identification and detection of abnormal individual behaviors. A data visualization system based on gas filling data gathered from gas stations in Xinjiang Province was implemented. By using different view models (parallel coordinates, map view, calendar matrix, Sankey
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    • References(29)
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    LIU H, GAO Y, LU L, et al. Visual analysis of route diversity[C]// Proceedings of the IEEE Conference on Visual Analytics Science and Technology. Los Alamitos: IEEE Computer Society, 2011: 171-180.
    [25]
    WANG Z C, LU M, YUAN X R, et al. Visual traffic jam analysis based on trajectory data[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 2159-2168.
    [26]
    KRGER R, THOM D, WRNER M, et al. TrajectoryLenses—A set-based filtering and exploration technique for long-term trajectory data[J]. Computer Graphics Forum, 2013, 32(3/4): 451-460.
    [27]
    PU J S, LIU S Y, DING Y, et al. T-Watcher: A new visual analytic system for effective traffic surveillance[C]// Proceedings IEEE 14th International Conference on Mobile Data Management. Los Alamitos: IEEE Computer Society, 2013, 1:127-136.
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    RIEHMANN P, HANFLER M, FROEHLICH B. Interactive sankey diagrams[C/OL]. Proceedings of the IEEE Symposium on Information Visualization. Minneapolis, USA: IEEE Press, 2005. https://static.aminer.org/pdf/PDF/000/404/817/interactive_sankey_diagrams.pdf.
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    SZMIDT E, KACPRZYK J. On an enhanced method for a more meaningful Pearson’s correlation coefficient between intuitionistic fuzzy sets[C]// Proceedings of the 11th International Conference on Artificial Intelligence & Soft Computing. Zakopane, Poland: Springer-Verlag, 2012: 334-341.
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    [1]
    KEIM D, ANDRIENKO G, FEKETE J D, et al. Visual Analytics: Defi-Nition, Process, and Challenge[M] //Lecture Notes in Computer Science. Heidelberg: Springer, 2008, 4950: 154-175.
    [2]
    姜晓睿, 郑春益, 蒋莉,等. 大规模出租车起止点数据可视分析[J]. 计算机辅助设计与图形学学报, 2015, 27(10): 1907-1917.
    JIANG Xiaorui, ZHWNG Chunyi, JIANG Li, et al. Visual analysis of large taxi origin-destination data[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(10): 1907-1917.
    [3]
    WANG Z C, YE T Z, LU M, et al. Visual exploration of sparse traffic trajectory data[J]. IEEE Transactions on Visualization & Computer Graphics, 2014, 20(12): 1813-1822.
    [4]
    DEKA L, QUDDUS M. Trip-based weighted trajectory matching algorithm for sparse GPS data[R]. Transportation Research Board Annual Meeting, http://transp-or.epfl.ch/heart/2014/abstracts/268.pdf,2015.
    [5]
    SANAULLAH I, QUDDUS M, ENOCH M P. Developing travel time estimation methods using sparse GPS data[J]. Journal of Intelligent Transportation Systems, 2016, 20(6): 532-544.
    [6]
    WANG Y, ZHENG Y, XUE Y. Travel time estimation of a path using sparse trajectories[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2014: 25-34.
    [7]
    任磊, 杜一, 马帅,等. 大数据可视分析综述[J]. 软件学报, 2014, 25(9): 1909-1936.
    [8]
    HEY T, GANNON D, PINKELMAN J. The future of data-intensive science[J]. Computer, 2012, 45(5): 81-82.
    [9]
    PEUQUET D J, KRAAK M J. GeoBrowsing: Creative thinking and knowledge discovery using geographic visualization[J]. Information Visualization, 2002, 1(1): 80-91.
    [10]
    SLINGSBY A, DYKES J, WOOD J. Exploring uncertainty in geoDemoGraphics with interactive graphics[J]. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(12): 2545-2554.
    [11]
    INSELBERG A. The plane with parallel coordinates [J]. The Visual Computer, 1985, 1(2): 69-91.
    [12]
    VON LANDESBERGER T, BRODKORB F, ROSKOSCH P, et al. MobilityGraphs: Visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering[J]. IEEE Transactions on Visualization & Computer Graphics, 2016, 22(1):11-20.
    [13]
    ANDRIENKO G L, ANDRIENKO N V, DYKES J, et al. GeoVisualization of dynamics, movement and change: Key issues and developing approaches in visualization research[J]. Information Visualization, 2008, 7(3): 173-180.
    [14]
    LUNDBLAD P, EURENIUS O, HELDRING T. Interactive visualization of weather and ship data[C]// Proceedings of the 13th International Conference on Information Visualisation. Barcelona, Spain: IEEE Press, 2009: 379-386.
    [15]
    OpenDataCity. Visitor flow analysis by public wireless[EB/OL]. http://apps.opendatacity.de/relog/,2013.
    [16]
    THUDT A, BAUR D, CARPENDALE S. Visits: A spatiotemporal visualization of location histories[J]. Journal of Heat Transfer, 2013, 114(1): 255-163.
    [17]
    TOMINSKI C, SCHUMANN H, ANDRIENKO G, et al. Stacking-based visualization of trajectory attribute data[J]. IEEE Transactions on Visualization Computer Graphics, 2012, 18(12): 2565-2574.
    [18]
    KAPLER T, WRIGHT W. GeoTime information visualization[C]// Proceedings of the IEEE Symposium on Information Visualization. Austin, USA: IEEE Press, 2004: 25-32.
    [19]
    STOLL M, KRGER R, ERTL T, et al. Racecar tracking and its visualization using sparse data[C/OL]// Proceedings of the Workshop on Sports Data Visualization, http://www.vis.uni-stuttgart.de/~cvis/publications/stoll_sportvis2013.pdf.
    [20]
    GUO H Q, WANG Z C, YU B W, et al. TripVista: Triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection[C]// Proceedings of Pacific Visualization Symposium. Los Alamitos: IEEE Computer Society, 2011: 163-170.
    [21]
    BYRON L, WATTENBERG M. Stacked graphs-geometry & aesthetics[J]. IEEE Transactions on Visualization and Computer Graphics, 2008, 14(6): 1245-1252.
    [22]
    WILLEMS N, VAN DE WETERING H, VAN WIJK J J. Visualization of vessel movements[J]. Computer Graph Forum, 2009, 28(3): 959-966.
    [23]
    WANG Z C, GUO H Q, YUAN X R, et al. Discovery exhibition: Visual analysis on traffic trajectory data[EB/OL]. [2014-09-24]. http://discovery exhibition.org/uploads/Main/2011Wang.pdf.
    [24]
    LIU H, GAO Y, LU L, et al. Visual analysis of route diversity[C]// Proceedings of the IEEE Conference on Visual Analytics Science and Technology. Los Alamitos: IEEE Computer Society, 2011: 171-180.
    [25]
    WANG Z C, LU M, YUAN X R, et al. Visual traffic jam analysis based on trajectory data[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 2159-2168.
    [26]
    KRGER R, THOM D, WRNER M, et al. TrajectoryLenses—A set-based filtering and exploration technique for long-term trajectory data[J]. Computer Graphics Forum, 2013, 32(3/4): 451-460.
    [27]
    PU J S, LIU S Y, DING Y, et al. T-Watcher: A new visual analytic system for effective traffic surveillance[C]// Proceedings IEEE 14th International Conference on Mobile Data Management. Los Alamitos: IEEE Computer Society, 2013, 1:127-136.
    [28]
    RIEHMANN P, HANFLER M, FROEHLICH B. Interactive sankey diagrams[C/OL]. Proceedings of the IEEE Symposium on Information Visualization. Minneapolis, USA: IEEE Press, 2005. https://static.aminer.org/pdf/PDF/000/404/817/interactive_sankey_diagrams.pdf.
    [29]
    SZMIDT E, KACPRZYK J. On an enhanced method for a more meaningful Pearson’s correlation coefficient between intuitionistic fuzzy sets[C]// Proceedings of the 11th International Conference on Artificial Intelligence & Soft Computing. Zakopane, Poland: Springer-Verlag, 2012: 334-341.
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