Is There a Robust Technique for Selecting Aspect Ratios in Line Charts?

Area of Science:

• Data Visualization
• Information Design
• Computational Geometry

Background:

• The aspect ratio of line charts significantly impacts data interpretation.
• Existing methods for selecting aspect ratios lack a comprehensive guideline for diverse datasets.
• Understanding parameterization invariance is key to robust aspect ratio selection.

Purpose of the Study:

• To provide a guideline for selecting appropriate aspect ratios for any 1D curve.
• To introduce a novel aspect ratio selection method, -LOR (local orientation resolution).
• To connect and compare existing and new aspect ratio selection methods.

Main Methods:

• Formulating existing methods (AL, banking to 45, RV, AS) as line integrals to analyze parameterization invariance.
• Deriving the new -LOR method with parameterization invariance.
• Connecting -LOR with AO (average absolute orientation).
• Conducting comparative evaluations across various datasets.

Main Results:

• Demonstrated parameterization invariance for several aspect ratio selection methods.
• Showcased the complementary nature of RV (resultant vector) and -LOR selections.
• Validated the practicability of the proposed dual-scale banking technique.

Conclusions:

• The -LOR method offers improved aspect ratio selection with parameterization invariance.
• The dual-scale banking technique effectively combines the strengths of RV and -LOR for optimal aspect ratio selection.
• This research provides practical guidance for enhancing data visualization through appropriate aspect ratio choices.