IEEE Trans Vis Comput Graph. 2024 Dec;30(12):7619-7636. doi: 10.1109/TVCG.2024.3356566. Epub 2024 Oct 28.
The increasing ubiquity of data in everyday life has elevated the importance of data literacy and accessible data representations, particularly for individuals with disabilities. While prior research predominantly focuses on the needs of the visually impaired, our survey aims to broaden this scope by investigating accessible data representations across a more inclusive spectrum of disabilities. After conducting a systematic review of 152 accessible data representation papers from ACM and IEEE databases, we found that roughly 78% of existing articles center on vision impairments. In this article, we conduct a comprehensive review of the remaining 22% of papers focused on underrepresented disability communities. We developed categorical dimensions based on accessibility, visualization, and human-computer interaction to classify the papers. These dimensions include the community of focus, issues addressed, contribution type, study methods, participants involved, data type, visualization type, and data domain. Our work redefines accessible data representations by illustrating their application for disabilities beyond those related to vision. Building on our literature review, we identify and discuss opportunities for future research in accessible data representations.
日常生活中数据的普及程度不断提高，这使得数据素养和可访问的数据表示变得尤为重要，特别是对于残疾人士而言。虽然先前的研究主要关注视障人士的需求，但我们的调查旨在通过研究更广泛的残疾人群体的可访问数据表示来扩大研究范围。在对来自 ACM 和 IEEE 数据库的 152 篇可访问数据表示论文进行系统审查后，我们发现大约 78%的现有文章集中在视力障碍上。在本文中，我们对其余 22%的重点关注代表性不足的残疾群体的论文进行了全面审查。我们根据可访问性、可视化和人机交互开发了分类维度，对这些论文进行了分类。这些维度包括关注的社区、解决的问题、贡献类型、研究方法、涉及的参与者、数据类型、可视化类型和数据领域。我们的工作通过说明它们在视觉以外的残疾领域的应用，重新定义了可访问的数据表示。基于我们的文献综述，我们确定并讨论了可访问数据表示的未来研究机会。
