Gestaltlines

We propose a general technique to visualize multivariate data sequences. It is based on a symbiotic combination of three powerful concepts from information visualization: sparklines, glyphs and gestalt theory. By visualizing several well‐known data sets in new ways we first demonstrate how explicit consideration of gestalt principles can be used to leverage visual perception capabilities for the identification of patterns such as trends, periodicities, change points, or outliers. A more detailed case study with complex and noisy data from a psychological experiment then demonstrates how basic design ideas for gestaltlines can be applied in less controlled, and thus more realistic, situations. The case study is complemented with reports on feedback from domain experts and a user study, both indicating that gestaltlines can be a convenient and valid means to explore and communicate patterns in micro‐visualizations.

[1]  Jeffrey Heer,et al.  Multi-Scale Banking to 45 Degrees , 2006, IEEE Transactions on Visualization and Computer Graphics.

[2]  Padhraic Smyth,et al.  Adaptive event detection with time-varying poisson processes , 2006, KDD '06.

[3]  Matthew O. Ward,et al.  A Taxonomy of Glyph Placement Strategies for Multidimensional Data Visualization , 2002, Inf. Vis..

[4]  Petra Isenberg,et al.  Evaluation of alternative glyph designs for time series data in a small multiple setting , 2013, CHI.

[5]  Pat Hanrahan,et al.  An Empirical Model of Slope Ratio Comparisons , 2012, IEEE Transactions on Visualization and Computer Graphics.

[6]  T. Yokum.  Sparklines: The Tom Thumb of Statistical Graphs , 2009 .

[7]  A. Galston.  Fundamentals of Ecology , 1972, The Yale Journal of Biology and Medicine.

[8]  Silvia Miksch,et al.  Metaphors of movement: a visualization and user interface for time-oriented, skeletal plans , 2001, Artif. Intell. Medicine.

[9]  E. Fanea,et al.  An interactive 3D integration of parallel coordinates and star glyphs , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[10]  C Popow,et al.  Support for Fast Comprehension of ICU Data: Visualization using Metaphor Graphics , 2001, Methods of Information in Medicine.

[11]  Felix Mödritscher,et al.  Foundations of a pattern language based on Gestalt principles , 2006, CHI Extended Abstracts.

[12]  Stephen G. Eick,et al.  Glyphs for software visualization , 1997, Proceedings Fifth International Workshop on Program Comprehension. IWPC'97.

[13]  Laurence S. Dooley,et al.  Gestalt theory in visual screen design: a new look at an old subject , 2002 .

[14]  Margarita Esponda-Argüero,et al.  Techniques for Visualizing Data Structures in Algorithmic Animations , 2010, Inf. Vis..

[15]  Ulrik Brandes,et al.  Asymmetric Relations in Longitudinal Social Networks , 2011, IEEE Transactions on Visualization and Computer Graphics.

[16]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[17]  W. Cleveland,et al.  Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods , 1984 .

[18]  John M. Chambers,et al.  Graphical Methods for Data Analysis , 1983 .

[19]  Robert Sedgewick,et al.  Algorithms in C , 1990 .

[20]  M. Wertheimer.  Untersuchungen zur Lehre von der Gestalt. II , 1923 .

[21]  Peter L. Brooks,et al.  Visualizing data , 1997 .

[22]  Michael D. Ward,et al.  The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models , 2011 .

[23]  A. Steffen.  Decision-making in the (stressed) brain , 2010 .

[24]  James T. Enns,et al.  Visualizing real-time multivariate data using preattentive processing , 1995, TOMC.

[25]  A. Bowman,et al.  A look at some data on the old faithful geyser , 1990 .

[26]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[27]  Jeffrey Heer,et al.  Crowdsourcing graphical perception: using mechanical turk to assess visualization design , 2010, CHI.