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We present Animated Vega-Lite, a set of extensions to Vega-Lite that model animated visualizations as time-varying data queries. In contrast to alternate approaches for specifying animated visualizations, which prize a highly expressive design space, Animated Vega-Lite prioritizes unifying animation with the language's existing abstractions for static and interactive visualizations to enable authors to smoothly move between or combine these modalities. Thus, to compose animation with static visualizations, we represent time as an encoding channel. Time encodings map a data field to animation keyframes, providing a lightweight specification for animations without interaction. To compose animation and interaction, we also represent time as an event stream; Vega-Lite selections, which provide dynamic data queries, are now driven not only by input events but by timer ticks as well. We evaluate the expressiveness of our approach through a gallery of diverse examples that demonstrate coverage over taxonomies of both interaction and animation. We also critically reflect on the conceptual affordances and limitations of our contribution by interviewing five expert developers of existing animation grammars. These reflections highlight the key motivating role of in-the-wild examples, and identify three central tradeoffs: the language design process, the types of animated transitions supported, and how the systems model keyframes.
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Comparative Visualizations through Parameterization and Variability
Comparative visualizations and the comparison tasks they support constitute a crucial part of visual data analysis on complex data sets. Existing approaches are ad hoc and often require significant effort to produce comparative visualizations, which is impractical especially in cases where visualizations have to be amended in response to changes in the underlying data. We show that the combination of parameterized visualizations and variations yields an effective model for comparative visualizations. Our approach supports data exploration and automatic visualization updates when the underlying data changes. We provide a prototype implementation and demonstrate that our approach covers most of existing comparative visualizations.
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- Award ID(s):
- 1717300
- PAR ID:
- 10096843
- Date Published:
- Journal Name:
- 2018 IEEE Symposium on Visual Languages and Human-Centric Computing
- Page Range / eLocation ID:
- 7 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/VLHCC.2018.8506578
