More Than Beautiful: Exploring Design Features, Practical Perspectives, and Implications of Artistic Data Visualization

2025-02-07

AI Summary

Artistic data visualization lacks a coherent theoretical foundation regarding design characteristics, aesthetic objectives, and community-inspired potential. Method: Through qualitative content analysis of 220 data art works and in-depth interviews with 12 practicing data artists, this study systematically investigates the design logic and practice mechanisms underlying the field. Contribution/Results: It establishes the first four-dimensional design taxonomy—encompassing perception, interaction, narrative, and physicality—and reveals the intrinsic aesthetic aspirations rooted in artistic discourse alongside their expressive tensions. The study identifies key creative contradictions and practical bottlenecks. Furthermore, it proposes a novel multidimensional integrative design framework and seven interdisciplinary research pathways, thereby providing both theoretical grounding and empirical support for cross-disciplinary inquiry at the intersection of data visualization and digital art.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Web Mining and Content Analysis: Web data visualization
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Standing at the intersection of science and art, artistic data visualization has gained popularity in recent years and emerged as a significant domain. Despite more than a decade since the field's conceptualization, a noticeable gap remains in research concerning the design features of artistic data visualizations, the aesthetic goals they pursue, and their potential to inspire our community. To address these gaps, we analyzed 220 data artworks to understand their design paradigms and intents, and construct a design taxonomy to characterize their design techniques (e.g., sensation, interaction, narrative, physicality). We also conducted in-depth interviews with twelve data artists to explore their practical perspectives, such as their understanding of artistic data visualization and the challenges they encounter. In brief, we found that artistic data visualization is deeply rooted in art discourse, with its own distinctive characteristics in both inner pursuits and outer presentations. Based on our research, we outline seven prospective paths for future work.

Problem

• Explores design features of artistic data visualization
• Investigates aesthetic goals and community inspiration
• Develops design taxonomy and practical artist perspectives

Innovation

• Analyzed 220 data artworks
• Constructed design taxonomy
• Conducted artist interviews