Accessible Color Sequences for Data Visualization

Sicong Wu Hui Huang J. Lanir + 3 lainnya

27 Mei 2022

Appropriate color mapping for categorical data visualization can significantly facilitate the discovery of underlying data patterns and effectively bring out visual aesthetics. Some systems suggest predefined palettes for this task. However, a predefined color mapping is not always optimal, failing to consider users’ needs for customization. Given an input categorical data visualization and a reference image, we present an effective method to automatically generate a coloring that resembles the reference while allowing classes to be easily distinguished. We extract a color palette with high perceptual distance between the colors by sampling dominant and discriminable colors from the image’s color space. These colors are assigned to given classes by solving an integer quadratic program to optimize point distinctness of the given chart while preserving the color spatial relations in the source image. We show results on various coloring tasks, with a diverse set of new coloring appearances for the input data. We also compare our approach to state-of-the-art palettes in a controlled user study, which shows that our method achieves comparable performance in class discrimination, while being more similar to the source image. User feedback after using our system verifies its efficiency in automatically generating desirable colorings that meet the user’s expectations when choosing a reference.

Nils Rodrigues Nelusa Pathmanathan Seyda Öney + 5 lainnya

27 April 2022

We present an exploratory study on the accessibility of images in publications when viewed with color vision deficiencies (CVDs). The study is based on 1,710 images sampled from a visualization dataset (VIS30K) over five years. We simulated four CVDs on each image. First, four researchers (one with a CVD) identified existing issues and helpful aspects in a subset of the images. Based on the resulting labels, 200 crowdworkers provided 30,000 ratings on present CVD issues in the simulated images. We analyzed this data for correlations, clusters, trends, and free text comments to gain a first overview of paper figure accessibility. Overall, about 60 % of the images were rated accessible. Furthermore, our study indicates that accessibility issues are subjective and hard to detect. On a meta-level, we reflect on our study experience to point out challenges and opportunities of large-scale accessibility studies for future research directions.

Kathleen Garrity Shakila Cherise S. Joyner Yea-Seul Kim + 2 lainnya

27 April 2022

Data visualizations are now widely used across many disciplines. However, many of them are not easily accessible for visually impaired people. In this work, we use three-staged mixed methods to understand the current practice of accessible visualization design for visually impaired people. We analyzed 95 visualizations from various venues to inspect how they are made inaccessible. To understand the rationale and context behind the design choices, we also conducted surveys with 144 practitioners in the U.S. and follow-up interviews with ten selected survey participants. Our findings include the difficulties of handling modern complex and interactive visualizations and the lack of accessibility support from visualization tools in addition to personal and organizational factors making it challenging to perform accessible design practices.

J. Horcas J. Galindo David Benavides

12 September 2022

Data visualization aims to effectively communicate quantitative information by understanding which techniques and displays work better for different circumstances and why. There are a variety of software solutions capable of generating a multitude of different visualizations of the same dataset. However, data visualization exposes a large space of visual configurations depending on the type of data to be visualized, the different displays (e.g., scatter plots, line graphs, pie charts), the visual components to encode the data (e.g., lines, dots, bars), or the specific visual attributes of those components (e.g., color, shape, size, length). Researchers and developers are not usually aware about best practices in data visualization, and they are required to learn about both the design practices that make communication effective and the low level details of the specific software tool used to generate the visualization. This paper proposes a software product line approach to model and materialize the variability of the visualization design process, guided by feature models. We encode the visualization knowledge regarding the best design practices, resolve the variability following a step-wise configuration approach, and then evaluate our proposal for a specific software visualization tool. Our solution helps researchers and developers communicate their quantitative results effectively by assisting them in the selection and generation of the visualizations that work best for each case. We open a new window of research where data visualization and variability meet each other.

Bongshin Lee Kathleen F. McCoy Edward Cutrell + 6 lainnya

27 April 2021

driven communication by business, government, and science. Furthermore, the use and need for visualizations is not just confined to data experts: Data visualizations are becoming ubiquitous in textbooks, presentations, and reports, as well as in popular media, both online and in print. The design of these visualizations, however, is premised on implicit assumptions about the reader's sensory, cognitive, and motor abilities. People without these abilities are ultimately disenfranchised, and access to the benefits of data visualization and to the underlying information is limited. Data visualizations, such as statistical charts, diagrams, and maps, are an effective means to represent, analyze, and explore data as well as identify and communicate insights. They take advantage of the human visual system’s high bandwidth, parallel processing, and ability to quickly recognize patterns. For instance, a table of numbers may be hard to understand, while those same numbers shown in a graphic form (such as a line chart) will immediately reveal a steadily increasing trend. For these reasons, interactive data visualization is central to both exploratory data analysis and dataD Insights → Lack of accessible access to data visualizations is a significant equity issue. → It's not only visual impairments that can restrict access but also other kinds of disabilities including cognitive and learning disabilities, and motor disabilities. → Overcoming this challenge requires visualization practitioners, visualization and accessibility researchers, and the relevant disability communities to work together.