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We present an exploratory study on the accessibility of images in publications when viewed with color vision deficiencies (CVDs).
The study is based on 1710 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.
There are many methods for projecting spherical maps onto the plane. Interactive versions of these projections allow the user to centre the region of interest. However, the effects of such interaction have not previously been evaluated. In a study with 120 participants we find interaction provides significantly more accurate area, direction and distance estimation in such projections. The surface of 3D sphere and torus topologies provides a continuous surface for uninterrupted network layout. But how best to project spherical network layouts to 2D screens has not been studied, nor have such spherical network projections been compared to torus projections. Using the most successful interactive sphere projections from our first study, we compare spherical, standard and toroidal layouts of networks for cluster and path following tasks with 96 participants, finding benefits for both spherical and toroidal layouts over standard network layouts in terms of accuracy for cluster understanding tasks.
An extensive body of work in visual analytics has examined how users conduct analyses in scientific and academic settings, identifying and categorizing user goals and the actions they undertake to achieve them. However, most of this work has studied the analysis process in simulated or isolated environments, leading to a gap in connecting these findings to large-scale business (enterprise) contexts, where visual analysis is most needed to make sense of the large amounts of data being generated. In this work, we conducted digital "field" observations to understand how users conduct visual analyses in an enterprise setting, where they operate within a large ecosystem of systems and people.
From these observations, we identified four common objectives, six recurring visual investigation patterns, and five emergent themes. We also performed a quantitative analysis of logs over 2530 user sessions from a second visual analysis product to validate that our patterns were not product-specific.
Icon arrays are graphical displays in which a subset of identical shapes are filled to convey probabilities. They are widely used for communicating probabilities to the general public. A primary design decision concerning icon arrays is how to fill and arrange these shapes. For example, a designer could fill the shapes from top to bottom or in a random fashion. We investigated the effect of different arrangements in icon arrays on probability perception. We showed participants icon arrays depicting probabilities between 0\% and 100\% in six different arrangements. Participants were more accurate in estimating probabilities when viewing the top, row, and diagonal arrangements, but they overestimated the proportions with the central arrangement and underestimated the proportions with the edge arrangement. They were biased to either overestimate or underestimate when viewing the random arrangement depending on the objective proportions, following a cyclical pattern consistent with existing findings in the psychophysics literature.
Data visualization is pervasive in the lives of children as they encounter graphs and charts in early education and online media. In spite of this prevalence, our guidelines and understanding of how children perceive graphs stem primarily from studies conducted with adults. Previous psychology and education research indicates that children’s cognitive abilities are different from adults. Therefore, we conducted a classic graphical perception study on a population of children aged 8–12 enrolled in the Ivy After School Program in Boston, MA and adult computer science students enrolled in Northeastern University to determine how accurately participants judge diferences in particular graphical encodings. We record the accuracy of participants’ answers for five encodings most commonly used with quantitative data. The results of our controlled experiment show that children have remarkably similar graphical perception to adults, but are consistently less accurate at interpreting the visual encodings. We found similar effectiveness rankings, relative differences in error between the different encodings, and patterns of bias across encoding types. Based on our fndings, we provide design guidelines and recommendations for creating visualizations for children. This paper and all supplemental materials are available at https://osf.io/ygrdv.