More Than Beautiful: Exploring Design Features, Practical Perspectives, and Implications of Artistic Data Visualization
Xingyu Lan, Yifan Wang, Lingyu Peng, Xiaofan Ma

TL;DR
This paper investigates the design features, artistic goals, and community impact of artistic data visualization by analyzing artworks and interviewing artists, revealing its roots in art discourse and proposing future research directions.
Contribution
It provides a comprehensive analysis of artistic data visualization design paradigms, constructs a taxonomy of techniques, and offers insights from artists' perspectives, filling research gaps in the field.
Findings
Artistic data visualization is rooted in art discourse and has unique characteristics.
A taxonomy of design techniques was developed, including sensation, interaction, narrative, and physicality.
Seven future research paths are proposed for advancing the field.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Visualization and Analytics · Aesthetic Perception and Analysis
