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Proceedings Paper

Visualizing trends and clusters in ranked time-series data
Author(s): Michael B. Gousie; John Grady; Melissa Branagan
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Paper Abstract

There are many systems that provide visualizations for time-oriented data. Of those, few provide the means of finding patterns in time-series data in which rankings are also important. Fewer still have the fine granularity necessary to visually follow individual data points through time. We propose the Ranking Timeline, a novel visualization method for modestly-sized multivariate data sets that include the top ten rankings over time. The system includes two main visualization components: a ranking over time and a cluster analysis. The ranking visualization, loosely based on line plots, allows the user to track individual data points so as to facilitate comparisons within a given time frame. Glyphs represent additional attributes within the framework of the overall system. The user has control over many aspects of the visualization, including viewing a subset of the data and/or focusing on a desired time frame. The cluster analysis tool shows the relative importance of individual items in conjunction with a visualization showing the connection(s) to other, similar items, while maintaining the aforementioned glyphs and user interaction. The user controls the clustering according to a similarity threshold. The system has been implemented as a Web application, and has been tested with data showing the top ten actors/actresses from 1929-2010. The experiments have revealed patterns in the data heretofore not explored.

Paper Details

Date Published: 3 February 2014
PDF: 12 pages
Proc. SPIE 9017, Visualization and Data Analysis 2014, 90170F (3 February 2014); doi: 10.1117/12.2037038
Show Author Affiliations
Michael B. Gousie, Wheaton College (United States)
John Grady, Wheaton College (United States)
Melissa Branagan, Wheaton College (United States)


Published in SPIE Proceedings Vol. 9017:
Visualization and Data Analysis 2014
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)

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