Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data
Abstract
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.
S. Khan, S. Jones, B. Bach, J. Cha, M. Chen, J. Meikle, J. C. Roberts, J. Thiyagalingam, J. Wood, and P. D. Ritsos, “Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data.” 2024.
[Online] Available at: arXiv:
Bibtex
@misc{khan2024featureaction,
title = {Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data},
author = {Khan, Saiful and Jones, Scott and Bach, Benjamin and Cha, Jaehoon and Chen, Min and Meikle, Julie and Roberts, Jonathan C. and Thiyagalingam, Jeyan and Wood, Jo and Ritsos, Panagiotis D.},
year = {2024},
eprint = {2402.03116},
archiveprefix = {arXiv},
primaryclass = {cs.HC}
}