Evaluating Line Chart Strategies for Mitigating Density of Temporal Data: The Impact on Trend, Prediction, and Decision-Making
Rifat Ara Proma, Ghulam Jilani Quadri, Paul Rosen
TL;DR
Dense temporal line charts suffer from overplotting, hindering trend detection, prediction, and decision-making. The authors conduct a between-subjects user study comparing four designs—Standard, Aggregated, Trellis, and Spiral—to evaluate interpretability, predictive tendency, and trust using five public time-series datasets. Aggregated charts match standard charts in trend identification and prediction with similar trust, while Trellis and Spiral generally underperform and reduce decisiveness in trust-based tasks. The results suggest Aggregated charts as a practical density-reduction option for dense temporal data, guiding practitioners in choosing visualization strategies that preserve intelligibility and user trust. Limitations include three-year datasets and potential design tweaks (e.g., color encoding) that could influence clarity; future work should explore longer time spans and additional encodings.
Abstract
Overplotted line charts can obscure trends in temporal data and hinder prediction. We conduct a user study comparing three alternatives-aggregated, trellis, and spiral line charts against standard line charts on tasks involving trend identification, making predictions, and decision-making. We found aggregated charts performed similarly to standard charts and support more accurate trend recognition and prediction; trellis and spiral charts generally lag. We also examined the impact on decision-making via a trust game. The results showed similar trust in standard and aggregated charts, varied trust in spiral charts, and a lean toward distrust in trellis charts. These findings provide guidance for practitioners choosing visualization strategies for dense temporal data.