iSonic: interactive sonification for non-visual data exploration
Abstract
iSonic is an interactive sonification tool for vision impaired users to explore geo-referenced statistical data, such as population or crime rates by geographical regions. Users use a keyboard or a smooth surface touchpad to interact with coordinated map and table views of the data. The integrated use of musical sounds and speech allows users to grasp the overall data trends and to explore the data to get more details. Scenarios of use are described.
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Gaining the same information is more difficult through audio-based representations than through visual and tactile graphs.
“Unlike visual and tactile graphs, where broad patterns can be readily retrieved and contextualized, gaining the same information is more difcult through audio-based representations [134].”
From The Accessibility of Data Visualizations on the Web for Screen Reader Users: Practices and Experiences During COVID-19 (2023) · cites this paper as [134]
Zhao et al. identified insight-seeking actions for audio-based data visualization consumption that provide a contextual overview or gist before the data and methods to situate data exploration within surrounding context.
“Zhao et al. identifed several insight-seeking actions that are utilized by audio-based methods for consuming data visualizations, which provide a contextual overview or “gist” before the data and supporting methods to “situate” data exploration within the surrounding context [134].”
From The Accessibility of Data Visualizations on the Web for Screen Reader Users: Practices and Experiences During COVID-19 (2023) · cites this paper as [134]
Accessible, non-visual techniques to present a variety of data graphics have largely been investigated in research, but few have been translated to standard web tools for use in practice.
“While accessible, non-visual techniques to present a variety of data graphics have largely been investigated in research [43,92,93,130,134], few have been translated to standard web tools for use in practice.”
From The Accessibility of Data Visualizations on the Web for Screen Reader Users: Practices and Experiences During COVID-19 (2023) · cites this paper as [134]
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- Authors
- Zhao, H., Plaisant, C., & Shneiderman, B.
- Affiliations
- University of Maryland, College Park (US)
- University of Maryland, College Park, MD
- Venue
- Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
- Publisher
- ACM
- Published
- 2005-10-09
- Pages
- 194-195
- OpenAlex
- W2040705065
- Cited by
- 14
- References
- 7
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 2 · 2017: 1 · 2015: 1 · 2013: 3
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Keywords
- sonification, visual impairment, interactive data visualization, population data, crime rates
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