HCC: Small: Accessible Data Visualizations for Blind Users

该项目的目标是提高盲人和低视力人群使用其他感官分析大数据的能力。即使对于有视力的人来说，扫描表格数据也很慢，但是对于盲人来说，这个问题会更加严重，因为当前的无障碍工具（如屏幕阅读器）带宽很低。图表、图形和其他数据可视化通过图形表示帮助普通受众实现数据访问的民主化，但这种表示对于盲人和低视力用户来说是一条封闭的途径。通过听觉和触觉传递数据的工具确实存在，但这些努力是分散的，不一致的，并且通常依赖于昂贵的硬件。即使这些设备是现成的，也很少有实用的工具支持由数千或数十万个数据项组成的实际数据集大小。该项目旨在缩小这些差距，使盲人能够访问大规模可视化和数据分析，这可能会对数百万视障和盲人产生根本性的影响，同时也为没有视觉障碍的人提供分析数据的新方法。项目团队将定义一个全面的无障碍可视化模型：基于声音、触摸和物理计算的高带宽数据表示，使盲人用户能够像视力正常的用户一样轻松地查看、分析和理解大规模数据集。该研究分为两个目标：（1）通过采访、观察和工具审查，了解盲人信息专业人员当前使用大规模数据的背景;（2）设计实用、现实且廉价的大规模可访问数据应用程序解决方案，使盲人专业人员能够与大规模数据进行交互。研究团队由两名研究人员组成，他们在可视化和可访问性方面具有互补的专业知识。虽然该团队将探索各种不同的设备，但一个基本目标是设计出廉价且与盲人用户已经拥有的无障碍设备兼容的解决方案。为了促进这一点，该团队将与盲人社区和技术组织合作，审查领先的数据分析工具的可访问性，使用这些工具作为基线比较，并建议未来的指导方针和工具，以支持可访问的数据图形。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This project’s goal is to improve blind and low-vision people’s ability to use their other senses to analyze big data. Scanning tabular data is slow even for a sighted person, but this problem is exacerbated for blind individuals because of the low bandwidth of current accessibility tools such as screen-readers. Charts, graphs, and other data visualizations help democratize data access for a general audience through graphical representations, but such representations are a closed-off avenue for blind and low-vision users. Tools that convey data through hearing and touch do exist, but these efforts are scattered, inconsistent, and often rely on expensive hardware. Even if these devices were readily available, few practical tools support realistic dataset sizes consisting of thousands or hundreds of thousands of data items. This project aims to close these gaps through work to make large-scale visualization and data analytics accessible to blind individuals, which may have a fundamental impact on millions of visually impaired and blind people, while also providing new ways for people without visual impairments to analyze data.The project team will define a comprehensive model of accessible visualization: high-bandwidth data representations based on sound, touch, and physical computing to enable blind users to view, analyze, and understand large-scale datasets as effortlessly as sighted users. The research is organized into two aims: (1) understanding current contexts of use for large-scale data among blind information professionals through interviews, observations, and tool reviews; and (2) designing large-scale accessible data applications that are practical, realistic, and inexpensive solutions to enable blind professionals to interact with large-scale data. The research team consists of two investigators with complementary expertise in visualization and accessibility. While the team will explore a wide variety of different devices, a fundamental goal will be to design solutions that are inexpensive and compatible with the accessibility equipment blind users already possess. To facilitate this, the team will work with the blindness community and technology organizations to review accessibility in leading data analytics tools, to use these tools as baseline comparisons, and to suggest future guidelines and tools to support accessible data graphics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.