Beyond One-Size-Fits-All: User Strategies for Simplification Technique and Level Selection in Responsive Line Charts

TL;DR

This study explores how users choose different simplification techniques for line charts across devices, revealing preferences for dataset-level strategies over device-specific optimization, and discusses implications for designing responsive visualization tools.

Contribution

It investigates user strategies in selecting simplification techniques for responsive line charts, highlighting the importance of dataset-level adaptation and interaction design considerations.

Findings

Users prefer dataset-level strategies over device-specific optimization.

Interaction complexity does not always increase user engagement.

Responsive simplification tools should balance flexibility with simplicity.

Abstract

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.