Evaluating Line Chart Strategies for Mitigating Density of Temporal Data: The Impact on Trend, Prediction, and Decision-Making

2025-10-13

AI Summary

Overlapping lines in line charts hinder trend identification and decision-making for high-density time-series data. Method: We systematically compare three alternative visualization strategies—aggregated plots, lattice (faceted) plots, and spiral plots—through a controlled experiment evaluating performance across three tasks: trend identification, numerical prediction, and trust-based decision-making. Contribution/Results: Aggregated plots significantly outperform standard line charts in both trend identification and prediction accuracy without compromising user trust. In contrast, lattice and spiral plots exhibit inconsistent performance; notably, lattice plots induce significantly higher levels of distrust. This study provides the first empirical evidence that aggregation achieves a balanced trade-off among readability, analytical accuracy, and perceived credibility. Our findings offer both theoretical grounding and practical guidelines for designing effective visualizations of high-density time-series data.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Data transparency and provenance

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.

Problem

• Evaluating visualization strategies for dense temporal data clarity
• Comparing chart types for trend identification and prediction accuracy
• Assessing how different visualizations impact trust in decision-making

Innovation

• Aggregated charts enhance trend recognition accuracy
• Trellis charts show lower trust in decision-making
• Spiral charts exhibit varied trust in user evaluation