Tactile Vega-Lite: Rapidly Prototyping Tactile Charts with Smart Defaults
Name
3706598.3714132.pdf
Size
13.07 MB
Format
Adobe PDF
Checksum (MD5)
154ffabf3997a23c0fd828a729affe38
Author(s)
Chen, Mengzhu (Katie)
Pedraza Pineros, Isabella
Satyanarayan, Arvind
Zong, Jonathan
Date Issued
April 25, 2025
Publisher
ACM|CHI Conference on Human Factors in Computing Systems
Citation
Mengzhu (Katie) Chen, Isabella Pedraza Pineros, Arvind Satyanarayan, and Jonathan Zong. 2025. Tactile Vega-Lite: Rapidly Prototyping Tactile Charts with Smart Defaults. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 931, 1–23.
Version
Final published version
Abstract
Tactile charts are essential for conveying data to blind and low vision (BLV) readers but are difficult for designers to construct. Non-expert designers face barriers to entry due to complex guidelines, while experts struggle with fragmented and time-consuming workflows that involve extensive customization. Inspired by formative interviews with expert tactile graphics designers, we created Tactile Vega-Lite (TVL): an extension of Vega-Lite that offers tactile-specific abstractions and synthesizes existing guidelines into a series of smart defaults. Predefined stylistic choices enable non-experts to produce guideline-compliant tactile charts quickly. Expert users can override defaults to tailor customizations for their intended audience. In a user study with 12 tactile graphics creators, we show that Tactile Vega-Lite enhances flexibility and consistency by automating tasks like adjusting spacing and translating braille while accelerating iterations through pre-defined textures and line styles. Through expert critique, we also learn more about tactile chart design best practices and design decisions.
Description
CHI ’25, Yokohama, Japan
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3706598.3714132
