An Image-based Typology for Visualization

2024-03-07

arXiv.org

AI Summary

To address the lack of content-oriented taxonomies for visualizations, this study introduces the first purely image-driven visualization classification framework. Building upon qualitative coding, expert collaborative annotation, and systematic semantic decoding, we develop a content-centric typology that identifies core visual stimuli and synthesizes ten standardized, image-derived visualization categories. This approach departs from conventional task-, data-, or chart-form–based taxonomies, enabling applications in design style identification, pedagogical categorization, and community evolution analysis. Employing a pre-registered research protocol, we publicly release an open-source image dataset (hosted on OSF) and a comprehensive coding manual to ensure reproducibility and scalability of the image-level typology. The resulting framework provides foundational tools and empirical evidence for visualization education, evaluation, and standardization discourse.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation

Abstract

We present and discuss the results of a qualitative analysis of visualization images to derive an image-based typology of visualizations. For each image, we seek to identify its main focus or the essential stimuli. As a result, we derived 10 image-based visualization types. We describe coding decisions we made in the derivation process. The resulting image typology can serve a number of purposes: enabling researchers and practitioners to identify visual design styles, facilitating the categorization of visualization images for the purpose of research and teaching, enabling researchers to study the evolution of the community and its research output over time, and facilitating a discussion of standardization in visualization. In addition, the tool and dataset enable scholars to closely examine the images and how they are published and communicated in our community. osf.io/dxjwt presents a pre-registration and all supplemental materials.

Problem

• Visualization Classification
• Design Style Recognition
• Image Categorization

Innovation

• Visualization Classification
• Critical Component Identification
• Standardization Discussion