There is More to Streamgraphs than Movies: Better Aesthetics via Ordering and Lassoing

Streamgraphs were popularized in 2008 when The New York Times used them to visualize box office revenues for 7500 movies over 21 years. The aesthetics of a streamgraph is affected by three components: the ordering of the layers, the shape of the lowest curve of the drawing, known as the baseline, an...

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Veröffentlicht in:Computer graphics forum Jg. 35; H. 3; S. 341 - 350
Hauptverfasser: Di Bartolomeo, Marco, Hu, Yifan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.06.2016
Schlagworte:
Aesthetics
Algorithms
Analysis
Categories and Subject Descriptors (according to ACM CCS)
Computer graphics
Distortion
I.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation
Motion pictures
Optimization
Order disorder
Revenues
Studies
Visual
Visualization
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Zusammenfassung:Streamgraphs were popularized in 2008 when The New York Times used them to visualize box office revenues for 7500 movies over 21 years. The aesthetics of a streamgraph is affected by three components: the ordering of the layers, the shape of the lowest curve of the drawing, known as the baseline, and the labels for the layers. As of today, the ordering and baseline computation algorithms proposed in the paper of Byron and Wattenberg are still considered the state of the art. However, their ordering algorithm exploits statistical properties of the movie revenue data that may not hold in other data. In addition, the baseline optimization is based on a definition of visual energy that in some cases results in considerable amount of visual distortion. We offer an ordering algorithm that works well regardless of the properties of the input data, and propose a 1‐norm based definition of visual energy and the associated solution method that overcomes the limitation of the original baseline optimization procedure. Furthermore, we propose an efficient layer labeling algorithm that scales linearly to the data size in place of the brute‐force algorithm adopted by Byron and Wattenberg. We demonstrate the advantage of our algorithms over existing techniques on a number of real world data sets.
Bibliographie:ark:/67375/WNG-9T5R0TD6-G
istex:5B4AC7488B51D33A1686863C927A55ACC9DB6991
ArticleID:CGF12910
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12910