Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data
This work addresses the need for scalable, personalized storytelling visualizations for individuals relying on dynamic data, though it appears incremental as it complements existing authoring methods.
The authors tackled the problem of creating storytelling visualizations for dynamic time series data, such as during the COVID-19 pandemic, by developing a computer-assisted meta-authoring method that uses feature-action patterns to anticipate potential data features, enabling efficient construction of data-dependent storyboards for various contexts.
We present a method to create storytelling visualization with time series data. Many personal decisions nowadays rely on access to dynamic data regularly, as we have seen during the COVID-19 pandemic. It is thus desirable to construct storytelling visualization for dynamic data that is selected by an individual for a specific context. Because of the need to tell data-dependent stories, predefined storyboards based on known data cannot accommodate dynamic data easily nor scale up to many different individuals and contexts. Motivated initially by the need to communicate time series data during the COVID-19 pandemic, we developed a novel computer-assisted method for meta-authoring of stories, which enables the design of storyboards that include feature-action patterns in anticipation of potential features that may appear in dynamically arrived or selected data. In addition to meta-storyboards involving COVID-19 data, we also present storyboards for telling stories about progress in a machine learning workflow. Our approach is complementary to traditional methods for authoring storytelling visualization, and provides an efficient means to construct data-dependent storyboards for different data-streams of similar contexts.