Visual Perception & Exploration

会議の名前
CHI 2022
Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures
要旨

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.

著者
Katrin Angerbauer
University of Stuttgart, Stuttgart, Germany
Nils Rodrigues
University of Stuttgart, Stuttgart, Germany
Rene Cutura
University of Stuttgart, Stuttgart, Germany
Seyda Öney
University of Stuttgart, Stuttgart, Germany
Nelusa Pathmanathan
University of Stuttgart, Stuttgart, Germany
Cristina Morariu
VISUS, Stuttgart, Germany
Daniel Weiskopf
University of Stuttgart, Stuttgart, Germany
Michael Sedlmair
University of Stuttgart, Stuttgart, Germany
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3502133

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GAN'SDA Wrap: Geographic And Network Structured DAta on surfaces that Wrap around
要旨

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.

著者
Kun-Ting Chen
Monash University, Melbourne, Australia
Tim Dwyer
Monash University, Melbourne, VIC, Australia
Yalong Yang
Virginia Tech, Blacksburg, Virginia, United States
Benjamin Bach
University of Edinburgh, Edinburgh, United Kingdom
Kim Marriott
Monash University, Melbourne, Australia
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501928

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Understanding Visual Investigation Patterns Through Digital "Field" Observations
要旨

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.

著者
Irene Rae
Google, Madison, Wisconsin, United States
Feng Zhou
Google, Madison, Wisconsin, United States
Martin Bilsing
Google, Zurich, Switzerland
Philipp Bunge
Google, Zurich, Switzerland
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517445

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Investigating Perceptual Biases in Icon Arrays
要旨

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.

受賞
Honorable Mention
著者
Cindy Xiong
University of Massachusetts Amherst, Amherst, Massachusetts, United States
Ali Sarvghad
University of Massachusetts Amherst, Amherst, Massachusetts, United States
Daniel G. Goldstein
Microsoft Research, New York, New York, United States
Jake M. Hofman
Microsoft Research, NYC, New York, United States
Çağatay Demiralp
Sigma Computing, San Francisco, California, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501874

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Juvenile Graphical Perception: A Comparison between Children and Adults
要旨

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.

著者
Liudas Panavas
Northeastern University, Boston, Massachusetts, United States
Amy E.. Worth
Ivy After School Program, Quincy, Massachusetts, United States
Tarik Crnovrsanin
Northeastern University, Boston, Massachusetts, United States
Tejas Sathyamurthi
Northeastern University, Boston, Massachusetts, United States
Sara Cordes
Boston College, Chestnut Hill, Massachusetts, United States
Michelle A.. Borkin
Northeastern University, Boston, Massachusetts, United States
Cody Dunne
Northeastern University, Boston, Massachusetts, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501893

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