Feature-Action Design Patterns for Storytelling Visualizations with Time
  Series Data
Saiful Khan Scott Jones Benjamin Bach Jaehoon Cha Min Chen Julie Meikle Jonathan C Roberts Jeyan Thiyagalingam Jo Wood Panagiotis D. Ritsos
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.