Beyond One-Size-Fits-All: User Strategies for Simplification Technique and Level Selection in Responsive Line Charts
For visualization designers and researchers, this work provides empirical evidence that responsive line chart simplification should offer algorithmic choice with progressive disclosure, rather than a one-size-fits-all approach.
This paper investigates whether users benefit from having a choice of simplification algorithms when adapting line charts to different screen sizes. A user study (N=30) found that users adapt technique selection based on dataset characteristics rather than device, and that interaction complexity does not uniformly increase engagement.
Simplifying line charts for responsive displays typically applies a single algorithm uniformly across devices, despite the availability of multiple techniques that preserve different signal characteristics (e.g., peaks, trends, periodicity). We investigate whether users benefit from algorithmic choice when adapting charts across screen sizes. In a within-subjects study (N=30), participants simplified nine datasets under three conditions: single pre-assigned technique (C1), multiple techniques (C2), and multiple techniques with manual point selection (C3), each with control over simplification level. We found that users adapted technique selections across datasets rather than devices, leveraging dataset-level strategies rather than per-device optimization. Additionally, interaction complexity did not always increase engagement uniformly, suggesting that responsive simplification tools should balance algorithmic flexibility with progressive disclosure and strong defaults. Supplemental materials are available at https://osf.io/yjp76/?view_only=b77b5e97f0cc4f689fbf48ad0d965af3.