QUARTZ: Qualitative Understanding via Accessible Representation and Visualization

2026-08-11

AI Summary

This work addresses the inaccessibility of nonlinear, semantically rich qualitative visualizations—such as concept maps—for blind and low-vision (BLV) researchers, as existing accessibility solutions fail to support their navigation and comprehension needs. The paper introduces QUARTZ, the first system to establish an accessible interaction paradigm for qualitative visualizations, offering screen reader–compatible multimodal representations for four representative visualization types. Through iterative co-design with eight BLV users using the RITE methodology, the study identifies two core challenges: fragmented nonlinear navigation and a semantic understanding gap. Guided by these insights, the authors formulate targeted design principles. User evaluations demonstrate that QUARTZ significantly enhances BLV researchers’ ability to independently conduct qualitative analysis tasks, and the resulting accessibility guidelines open new pathways for inclusive visualization research.

Abstract

Qualitative data visualizations -- concept maps, network graphs, Sankey diagrams, and coding stripes -- are integral to research practice, yet remain entirely inaccessible to blind and low-vision (BLV) researchers. While visualization has seen advanced multimodal solutions for quantitative charts, qualitative visualizations, and their non-linear, semantically rich structures have received no attention. We present QUARTZ, a web-based system that provides screen-reader-accessible, multimodal representations of qualitative data visualizations. Using the Rapid Iterative Testing and Evaluation (RITE) method, we conducted a user study with 8 BLV participants who completed 12 tasks across four visualization types. Our findings expose accessibility barriers unique to qualitative visualizations -- non-linear navigation breakdowns and semantic comprehension gaps absent from quantitative chart research---and document how iterative co-design with BLV users resolved them. We contribute empirical evidence and design guidelines for an underexplored visualization domain, advancing the infrastructure BLV researchers need to participate independently in qualitative inquiry.

Problem

• qualitative data visualization
• accessibility
• blind and low-vision researchers
• non-linear navigation
• semantic comprehension

Innovation

• qualitative visualization
• accessibility
• blind and low-vision
• multimodal representation
• co-design