Reframing Pattern: A Comprehensive Approach to a Composite Visual Variable

Area of Science:

• Information Visualization
• Human-Computer Interaction
• Visual Analytics

Background:

• Patterns are crucial visual variables in data visualization but lack consistent theoretical foundations and terminology.
• Inconsistent use of 'pattern' and 'texture' hinders effective application and research in visualization.

Purpose of the Study:

• To establish a comprehensive and consistent theory for pattern as a visual variable in visualization.
• To clarify ambiguities in existing literature regarding the use of patterns and textures.
• To formalize a new system for pattern design and explore its implications for visualization.

Main Methods:

• Conducted a comprehensive cross-disciplinary literature review to analyze existing concepts and terminology of patterns in visualization.
• Developed a new theoretical framework defining patterns as composite visual variables made of structured graphic primitives.
• Formalized a system for pattern design based on spatial arrangement, appearance relationships, and primitive visual variables.

Main Results:

• Presented a novel, consistent treatment of pattern as a composite visual variable, clarifying its conceptual foundations.
• Introduced a formal system for pattern design, expanding the design space for this visual variable.
• Demonstrated the explanatory power of the proposed pattern system, particularly for complex spatial arrangements and cartographic theory.

Conclusions:

• The new theory provides a robust framework for understanding, designing, and utilizing patterns in data visualization.
• This work bridges existing visualization theory with broader concepts, offering new opportunities for visualization design.
• The formalized system facilitates more effective and research-informed application of patterns in visual representations.