Beyond Vision Impairments: Redefining the Scope of Accessible Data Representations

Date: 2024-01-22

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 paper, 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, 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. All supplemental materials are available at https://osf.io/ yv4xm/?view_only=b36a3fbf7a14b3888029966faa3def9

Reccomended Citation: B. L. Wimer, L. South, K. Wu, D. A. Szafir, M. A. Borkin and R. Metoyer (2024). Beyond Vision Impairments: Redefining the Scope of Accessible Data Representations. In EEE Transactions on Visualization and Computer Graphics doi: 10.1109/TVCG.2024.3356566..