Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data

• Published in: IEEE transactions on visualization and computer graphics Vol. 22; no. 1; pp. 559 - 568
• Main Authors: Bach, Benjamin, Conglei Shi, Heulot, Nicolas, Madhyastha, Tara, Grabowski, Tom, Dragicevic, Pierre
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Computer Science; Data visualization; Domains; Electronic publishing; Encyclopedias; Evolution; Folding; History; Human-Computer Interaction; Impact analysis; information visualization; Internet; multidimensional scaling; Network analysis; Progressions; Temporal data visualization; Visualization; Temporal data visualization; information visualization; multidimensional scaling; geospatial data; temporal data; video data
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2015.2467851

We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets.