Designing Semantically-Resonant Abstract Patterns for Data Visualization

TL;DR

This paper introduces a structured methodology for creating semantically-resonant abstract patterns in data visualization, making pattern design accessible and effective for both experts and the general public.

Contribution

It provides the first systematic framework for designing semantically-resonant abstract patterns, validated through workshops with experts and non-design participants.

Findings

The methodology helps non-designers create effective semantic patterns.

Semantically-resonant patterns improve intuitive understanding of data.

Workshop results confirm the approach's usability and effectiveness.

Abstract

We present a structured design methodology for creating semantically-resonant abstract patterns, making the pattern design process accessible to the general public. Semantically-resonant patterns are those that intuitively evoke the concept they represent within a specific set (e.g., in a vegetable concept set, small dots for olives and large dots for tomatoes), analogous to the concept of semantically-resonant colors (e.g., using olive green for olives and red for tomatoes). Previous research has shown that semantically-resonant colors can improve chart reading speed, and designers have made attempts to integrate semantic cues into abstract pattern designs. However, a systematic framework for developing such patterns was lacking. To bridge this gap, we conducted a series of workshops with design experts, resulting in a design methodology that summarizes the methodology for designing semantically-resonant abstract patterns. We evaluated our design methodology through another series of workshops with non-design participants. The results indicate that our proposed design methodology effectively supports the general public in designing semantically-resonant abstract patterns for both abstract and concrete concepts.