A Crowdsourcing Platform for Constructing Accessibility Maps Supporting Multiple Participation Modes

Miyata, Akira; Okugawa, Kazuki; Yamato, Yoji; Maeda, Tadashi; Murayama, Yusaku; Aibara, Megumi; Furuichi, Masakazu; Murayama, Yuko

doi:10.1145/3411763.3451688

Cited by 12 publications

(2 citation statements)

References 25 publications

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“…Our analysis revealed that the most familiar domain was navigation (38.2%, N=13), which includes papers that focus on accessible data for tasks such as navigation (e.g., [58]), wayfinding (e.g., [59]), assisting people using public transportation (e.g., [60]), and provide people with motor impairments to practice navigating maps through a virtual simulator [61]. This prevalence highlights the potential for enriching visual representations with access-related details-such as information on sidewalk conditions or transit accessibility.…”

Section: B Research Methodology 1) Study Methodsmentioning

confidence: 99%

“…Our review found numerous instances where research contributions targeted multiple disability com-munities [33], [35], [38], [59], [75], [76]. Despite the apparent complexity in addressing the heterogeneity of disabilities, various accessibility frameworks have demonstrated utility across multiple disability communities [11], [43], [56].…”

Section: B Opportunities and Challengesmentioning

confidence: 99%

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Beyond Vision Impairments: Redefining the Scope of Accessible Data Representations

Wimer,

South,

et al. 2024

Preprint

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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.

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