Evaluating the effects of size in linesets

LineSets represent information about sets by drawing one line for each set on an existing visualization of data items. This paper addresses the following question: does manipulating the size of visual elements affect the comprehension of LineSets? We empirically evaluated two types of size treatments applied to LineSets drawn on networks: varying set-line thickness, to reflect relative set cardinality, and varying node diameter, to reflect data items' relative degree of connectivity. The evaluation required participants to perform tasks that were thought to be aided by the size variations alongside tasks where no benefit was anticipated. Viewing comprehension through accuracy and time performance, we found that varying set-line thickness and node diameter significantly improves the effectiveness of LineSets. As a consequence, this research leads to the recommendation that LineSets vary sizes of lines and nodes.

[1]  James T. Enns,et al.  Attention and Visual Memory in Visualization and Computer Graphics , 2012, IEEE Transactions on Visualization and Computer Graphics.

[2]  Gem Stapleton,et al.  Does the Orientation of an Euler Diagram Affect User Comprehension? , 2012, DMS.

[3]  Liang Gou,et al.  TreeNetViz: Revealing Patterns of Networks over Tree Structures , 2011, IEEE Transactions on Visualization and Computer Graphics.

[4]  Stephen G. Kobourov,et al.  Group-Level Graph Visualization Taxonomy , 2014, EuroVis.

[5]  Björn Gottfried,et al.  Set space diagrams , 2014, J. Vis. Lang. Comput..

[6]  Gem Stapleton,et al.  A task-based evaluation of combined set and network visualization , 2016, Inf. Sci..

[7]  Ben Shneiderman,et al.  A Task Taxonomy for Network Evolution Analysis , 2014, IEEE Transactions on Visualization and Computer Graphics.

[8]  Silvia Miksch,et al.  Visualizing Sets and Set-typed Data: State-of-the-Art and Future Challenges , 2014, EuroVis.

[9]  Mary Czerwinski,et al.  Design Study of LineSets, a Novel Set Visualization Technique , 2011, IEEE Transactions on Visualization and Computer Graphics.

[10]  John J. Bertin,et al.  The semiology of graphics , 1983 .

[11]  Gem Stapleton,et al.  The Impact of Shape on the Perception of Euler Diagrams , 2014, Diagrams.

[12]  A. Treisman.  Features and Objects: The Fourteenth Bartlett Memorial Lecture , 1988, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[13]  Jean-Daniel Fekete,et al.  MatLink: Enhanced Matrix Visualization for Analyzing Social Networks , 2007, INTERACT.

[14]  Gem Stapleton,et al.  Evaluating the Effects of Colour in LineSets , 2016, Diagrams.

[15]  Christopher G. Healey,et al.  Choosing effective colours for data visualization , 1996, Proceedings of Seventh Annual IEEE Visualization '96.

[16]  Emden R. Gansner,et al.  Graphviz and Dynagraph – Static and Dynamic Graph Drawing Tools , 2003 .

[17]  James T. Enns,et al.  Building perceptual textures to visualize multidimensional datasets , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[18]  Ian Spence,et al.  The discrimination of graphical elements , 2001 .

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

[20]  Yifan Hu,et al.  How to Display Group Information on Node-Link Diagrams: An Evaluation , 2014, IEEE Transactions on Visualization and Computer Graphics.

[21]  Danah Boyd,et al.  Vizster: visualizing online social networks , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[22]  Bettina Speckmann,et al.  KelpFusion: A Hybrid Set Visualization Technique , 2013, IEEE Transactions on Visualization and Computer Graphics.

[23]  Gem Stapleton,et al.  Visualizing Sets with Linear Diagrams , 2015, TCHI.

[24]  Gem Stapleton,et al.  How Should We Use Colour in Euler Diagrams? , 2014, VINCI '14.

[25]  G. Jantzen.  1988 , 1988, The Winning Cars of the Indianapolis 500.

[26]  L. Couturat,et al.  Opuscules et Fragments Inédits de Leibniz : Extraits des manuscrits de la Bibliothèque royale de Hanovre , 1903 .