LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts

Area of Science:

• Data Visualization
• Visual Analytics
• Computer Graphics

Background:

• Line charts visualize sequential data, often requiring smoothing for large or noisy datasets.
• Existing smoothing methods lack a unified evaluation framework, hindering optimal selection for visual analytics tasks.

Purpose of the Study:

• To establish a comprehensive framework for evaluating line chart smoothing methods.
• To analyze the effectiveness of various smoothing techniques across different visual analytics tasks.

Main Methods:

• Developed a framework with 8 effectiveness measures tied to 8 low-level visual analytics tasks.
• Evaluated 12 smoothing methods across 4 classes: rank filters, convolutional filters, frequency domain filters, and subsampling.

Main Results:

• No single smoothing method is universally optimal; effectiveness is task- and data-dependent.
• Gaussian filters and topology-based subsampling show strong general performance.
• Low-pass cutoff filters and Douglas-Peucker subsampling excel in specific tasks.
• Uniform subsampling yields poor results and is generally not recommended.

Conclusions:

• The proposed framework provides a systematic approach to selecting appropriate line chart smoothing methods.
• Method selection should be guided by specific visual analytics goals and data characteristics.
• Certain commonly used methods, like uniform subsampling, may degrade visualization quality.