Year: 2025
Type: article
Abstract: Line charts surface many features in time series data, from trends to periodicity to peaks & valleys. However, not every potentially important feature in the data may correspond to a visual feature that readers can detect or prioritize. In this study, we conducted a visual stenography task, where participants re-drew line charts to solicit information about the visual features they believed to be important. We systematically varied noise levels (SNR $\approx$≈ 5-30 dB) across line charts to observe how visual clutter influences which features people prioritize in their sketches. We identified three key strategies that correlated with the noise present in the stimuli: the $\color{green}{\textit{Replicator}}$greenReplicator attempted to retain all major features of the line chart including noise; the $\color{yellow}{\textit{Trend Keeper}}$yellowTrendKeeper prioritized trends disregarding periodicity and peaks; and the $\color{pink}{\textit{De-noiser}}$pinkDe-noiser filtered out noise while preserving other features. Further, we found that participants tended to faithfully retain trends and peaks & valleys when these features were present, whereas periodicity and noise were represented in more qualitative or gestural ways: semantically rather than accurately. These results suggest a need to consider more flexible and human-centric ways of presenting, summarizing, preprocessing, or clustering time series data.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4415594403