Stacking-Based Visualization of Trajectory Attribute Data

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well.

[1]  Joachim Gudmundsson,et al.  Computational Movement Analysis , 2012, Springer Handbook of Geographic Information.

[2]  T. Saito,et al.  Two-tone pseudo coloring: compact visualization for one-dimensional data , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[3]  Arthur H. Robinson,et al.  The thematic maps of Charles Joseph Minard , 1967 .

[4]  Matthias Grossglauser,et al.  CRAWDAD dataset epfl/mobility (v.2009-02-24) , 2009 .

[5]  Jacques Bertin,et al.  Semiology of Graphics - Diagrams, Networks, Maps , 2010 .

[6]  Leland Wilkinson,et al.  Stacking Graphic Elements to Avoid Over-Plotting , 2010, IEEE Transactions on Visualization and Computer Graphics.

[7]  Jeffrey Heer,et al.  Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations , 2009, CHI.

[8]  Christophe Hurter,et al.  From movement tracks through events to places: Extracting and characterizing significant places from mobility data , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[9]  AndrienkoNatalia,et al.  Stacking-Based Visualization of Trajectory Attribute Data , 2012 .

[10]  Otto Huisman,et al.  Beyond exploratory visualization of space time paths , 2009 .

[11]  Bart Kranstauber,et al.  Exploration through enrichment: a visual analytics approach for animal movement , 2011, GIS.

[12]  Robert Kincaid,et al.  Line graph explorer: scalable display of line graphs using Focus+Context , 2006, AVI '06.

[13]  Dino Pedreschi,et al.  Visually driven analysis of movement data by progressive clustering , 2008, Inf. Vis..

[14]  Slava Kisilevich,et al.  A conceptual framework and taxonomy of techniques for analyzing movement , 2011, J. Vis. Lang. Comput..

[15]  Jeffrey C. Patton,et al.  SOME Truth with Maps: A Primer on Symbolization and Design , 1995 .

[16]  Bernice E. Rogowitz,et al.  A rule-based tool for assisting colormap selection , 1995, Proceedings Visualization '95.

[17]  Gennady L. Andrienko,et al.  Poster: Dynamic time transformation for interpreting clusters of trajectories with space-time cube , 2010, 2010 IEEE Symposium on Visual Analytics Science and Technology.

[18]  Niklas Elmqvist,et al.  A Taxonomy of 3D Occlusion Management for Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[19]  Menno-Jan Kraak,et al.  The space - time cube revisited from a geovisualization perspective , 2003 .

[20]  Marco Heurich,et al.  An event-based conceptual model for context-aware movement analysis , 2011, Int. J. Geogr. Inf. Sci..

[21]  William Wright,et al.  GeoTime Information Visualization , 2004, IEEE Symposium on Information Visualization.

[22]  Heidrun Schumann,et al.  Visualization of Time-Oriented Data , 2011, Human-Computer Interaction Series.

[23]  Siyuan Liu,et al.  Visual analysis of route diversity , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[24]  George F. Jenks,et al.  ERROR ON CHOROPLETHIC MAPS: DEFINITION, MEASUREMENT, REDUCTION , 1971 .

[25]  Terry A. Slocum.  Thematic Cartography and Visualization , 1998 .

[26]  Mats Lind,et al.  Evaluating 2D and 3D visualizations of spatiotemporal information , 2010, TAP.

[27]  Kwan-Liu Ma,et al.  Proximity-based visualization of movement trace data , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[28]  Colin Ware,et al.  Visualizing the underwater behavior of humpback whales , 2006, IEEE Computer Graphics and Applications.

[29]  Cynthia A. Brewer,et al.  ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps , 2003 .

[30]  G. Andrienko,et al.  Exploratory analysis of spatial and temporal data , 2013 .

[31]  Heidrun Schumann,et al.  Space, time and visual analytics , 2010, Int. J. Geogr. Inf. Sci..

[32]  Ben Shneiderman,et al.  Why Not Make Interfaces Better than 3D Reality? , 2003, IEEE Computer Graphics and Applications.

[33]  Dino Pedreschi,et al.  Mobility, Data Mining and Privacy - Geographic Knowledge Discovery , 2008, Mobility, Data Mining and Privacy.

[34]  Denis Gracanin,et al.  Color LinesView:AnApproach toVisualization of Families of Function Graphs , 2007, 2007 11th International Conference Information Visualization (IV '07).