Designing Semantically-Resonant Abstract Patterns for Data Visualization

TL;DR

This work addresses the lack of a systematic framework for designing semantically-resonant abstract patterns in data visualization. It introduces a two-step methodology—identifying content to visualize and encoding it into patterns—grounded in design-expert workshops and validated with non-expert participants across three concept sets (vegetables, music genres, emotions). The study demonstrates that the methodology can guide novices to ideate and create semantically meaningful black-and-white patterns for both abstract and concrete concepts, with abstract concepts generally perceived as more easily guided by the framework. By formalizing how content can be mapped to pattern-based representations and evaluating usability among non-designers, the authors expand the visualization design toolkit and lower access barriers for broader public engagement. The work also outlines limitations and future directions, including tooling, libraries, and AI-assisted support to scale pattern generation and enhance readability and interpretability in various contexts.

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.

Figures (93)

• Figure 1: Procedure of the design workshop with each design expert, as described in detail in \ref{['sec:workshop']}. Left: Preparation before the workshop, including the three concept sets, a design template with a blank pie chart, and two rounds of pilot studies. Middle: Steps of the formal workshop conducted individually with 13 design experts. Right: Data collected and the corresponding analysis results.
• Figure 2: Overview of our design methodology. The design methodology categorize the methodology into two steps, with each step categorizing potential approaches for designing se-man-ti-cal-ly-re-so-nant patterns.
• Figure 3: Frequencies of use of each visual variable in our design workshop across the three concept sets. Each use of a visual variable for a concept was counted once. We use part of Ex5's patterns for the three concept sets to fill the bars in the corresponding rows; we show the whole designs in \ref{['fig:ex005-ve']}\ref{['fig:ex005-ve']}, \ref{['fig:ex005-mus']}, and \ref{['fig:ex005-emo']} in \ref{['appendix:all-designs-workshop1']}.
• Figure 4: Examples of pattern design from our design workshop: (left) Basic patterns for the vegetable concept set by Ex5, and (right) Complex patterns for the music concept set by Ex7. We show larger versions of these figures in \ref{['fig:ex005-ve']} and \ref{['fig:ex007-mus']}, respectively, in \ref{['appendix:all-designs-workshop1']}.
• Figure 5: Rating of how useful, easy to use, and helpful our design methodology is, for the concrete concept set. The percentages on the left represent negative scores (1--3), and the percentages on the right represent positive scores (5--7).