Augmenting Visualizations with Statistical and User-Defined Data Facts
Author(s)
Guo, Grace
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Abstract
When designing visualizations and visualization systems, we often augment charts and graphs with visual elements in order to convey richer and more nuanced information about relationships in the data. However, we do not fully understand user considerations when creating these augmentations, nor do we have toolkits to support augmentation authoring. This thesis first outlines a design space of user-created augmentations, then introduces Auteur, a front-end JavaScript toolkit designed to help developers add augmentations to web-based D3 visualizations and systems to convey statistical and custom data relationships. The library is then customized and extended for the domains of online learning and causal inference, where users may be interested in domain-specific data relationships or work with unique chart types and data sets. Collectively, these contributions aim to help us better incorporate user-defined augmentations into visualizations for analysis and storytelling, thus conveying human context, user preferences, and domain knowledge through
our charts and graphs.
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Date
2024-07-28
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Text
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Dissertation