Visualization Grammars and Design

会議の名前
CHI 2023
Perceptual Pat: A Virtual Human Visual System for Iterative Visualization Design
要旨

Designing a visualization is often a process of iterative refinement where the designer improves a chart over time by adding features, improving encodings, and fixing mistakes. However, effective design requires external critique and evaluation. Unfortunately, such critique is not always available on short notice and evaluation can be costly. To address this need, we present Perceptual Pat, an extensible suite of AI and computer vision techniques that forms a virtual human visual system for supporting iterative visualization design. The system analyzes snapshots of a visualization using an extensible set of filters—including gaze maps, text recognition, color analysis, etc—and generates a report summarizing the findings. The web-based Pat Design Lab provides a version tracking system that enables the designer to track improvements over time. We validate Perceptual Pat using a longitudinal qualitative study involving 4 professional visualization designers that used the tool over a few days to design a new visualization.

著者
Sungbok Shin
University of Maryland, College Park, Maryland, United States
Sanghyun Hong
Oregon State University, Corvallis, Oregon, United States
Niklas Elmqvist
University of Maryland, College Park, College Park, Maryland, United States
論文URL

https://doi.org/10.1145/3544548.3580974

動画
Troubling Collaboration: Matters of Care for Visualization Design Study
要旨

A common research process in visualization is for visualization researchers to collaborate with domain experts to solve particular applied data problems. While there is existing guidance and expertise around how to structure collaborations to strengthen research contributions, there is comparatively little guidance on how to navigate the implications of, and power produced through the socio-technical entanglements of collaborations. In this paper, we qualitatively analyze reflective interviews of past participants of collaborations from multiple perspectives: visualization graduate students, visualization professors, and domain collaborators. We juxtapose the perspectives of these individuals, revealing tensions about the tools that are built and the relationships that are formed --- a complex web of competing motivations. Through the lens of \textit{matters of care}, we interpret this web, concluding with considerations that both trouble and necessitate reformation of current patterns around collaborative work in visualization design studies to promote more equitable, useful, and care-ful outcomes.

著者
Derya Akbaba
Linköping University, Norrköping, Sweden
Devin Lange
University of Utah, Salt Lake City, Utah, United States
Michael Correll
Tableau Software, Seattle, Washington, United States
Alexander Lex
University of Utah, Salt Lake City, Utah, United States
Miriah Meyer
Linköping University, Nörrkoping, Sweden
論文URL

https://doi.org/10.1145/3544548.3581168

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DataPilot: Utilizing Quality and Usage Information for Subset Selection during Visual Data Preparation
要旨

Selecting relevant data subsets from large, unfamiliar datasets can be difficult. We address this challenge by modeling and visualizing two kinds of auxiliary information: (1) quality - the validity and appropriateness of data required to perform certain analytical tasks; and (2) usage - the historical utilization characteristics of data across multiple users. Through a design study with 14 data workers, we integrate this information into a visual data preparation and analysis tool, DataPilot. DataPilot presents visual cues about "the good, the bad, and the ugly" aspects of data and provides graphical user interface controls as interaction affordances, guiding users to perform subset selection. Through a study with 36 participants, we investigate how DataPilot helps users navigate a large, unfamiliar tabular dataset, prepare a relevant subset, and build a visualization dashboard. We find that users selected smaller, effective subsets with higher quality and usage, and with greater success and confidence.

著者
Arpit Narechania
Georgia Institute of Technology, Atlanta, Georgia, United States
Fan Du
Adobe Research, San Jose, California, United States
Atanu R. Sinha
Adobe Systems, Inc, Bangalore, India
Ryan Rossi
Adobe Research, San Jose, California, United States
Jane Hoffswell
Adobe Research, Seattle, Washington, United States
Shunan Guo
Adobe Research, San Jose, California, United States
Eunyee Koh
Adobe Research, San Jose, California, United States
Shamkant B. Navathe
Georgia Institute of Technology, Atlanta, Georgia, United States
Alex Endert
Georgia Institute of Technology, Atlanta, Georgia, United States
論文URL

https://doi.org/10.1145/3544548.3581509

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Deimos: A Grammar of Dynamic Embodied Immersive Visualisation Morphs and Transitions
要旨

We present Deimos, a grammar for specifying dynamic embodied immersive visualisation morphs and transitions. A morph is a collection of animated transitions that are dynamically applied to immersive visualisations at runtime and is conceptually modelled as a state machine. It is comprised of state, transition, and signal specifications. States in a morph are used to generate animation keyframes, with transitions connecting two states together. A transition is controlled by signals, which are composable data streams that can be used to enable embodied interaction techniques. Morphs allow immersive representations of data to transform and change shape through user interaction, facilitating the embodied cognition process. We demonstrate the expressivity of Deimos in an example gallery and evaluate its usability in an expert user study of six immersive analytics researchers. Participants found the grammar to be powerful and expressive, and showed interest in drawing upon Deimos’ concepts and ideas in their own research.

著者
Benjamin Lee
Monash University, Melbourne, Australia
Arvind Satyanarayan
MIT, Cambridge, Massachusetts, United States
Maxime Cordeil
The University Of Queensland, Brisbane, Australia
Arnaud Prouzeau
Inria, Bordeaux, France
Bernhard Jenny
Monash University, Melbourne, Australia
Tim Dwyer
Monash University, Melbourne, Australia
論文URL

https://doi.org/10.1145/3544548.3580754

動画
DataParticles: Block-based and Language-oriented Authoring of Animated Unit Visualization
要旨

Unit visualizations have been widely used in data storytelling within interactive articles and videos. However, authoring data stories that contain animated unit visualizations is challenging due to the tedious, time-consuming process of switching back and forth between writing a narrative and configuring the accompanying visualizations and animations. To streamline this process, we present DataParticles, a block-based story editor that leverages the latent connections between text, data, and visualizations to help creators flexibly prototype, explore, and iterate on a story narrative and its corresponding visualizations. To inform the design of DataParticles, we interviewed 6 domain experts and studied a dataset of 44 existing animated unit visualizations to identify the narrative patterns and congruence principles they employed. A user study with 9 experts showed that DataParticles can significantly simplify the process of authoring data stories with animated unit visualizations by encouraging exploration and supporting fast prototyping.

受賞
Best Paper
著者
Yining Cao
University of California, San Diego, San Diego, California, United States
Jane L. E
UCSD, San Diego, California, United States
Zhutian Chen
Harvard University, Boston, Massachusetts, United States
Haijun Xia
University of California, San Diego, San Diego, California, United States
論文URL

https://doi.org/10.1145/3544548.3581472

動画
VisLab: Enabling Visualization Designers to Gather Empirically Informed Design Feedback
要旨

When creating a visualization, designers face various conflicting design choices. They typically rely on their hunches to deal with intricate trade-offs or resort to feedback from their colleagues. On the other hand, researchers have long used empirical methods to derive useful quantitative insights into visualization designs. Taking inspiration from this research tradition, we developed VisLab, an open-source online system to complement the existing qualitative feedback practice and help visualization practitioners run experiments to gather empirically informed design feedback. We surveyed practitioners’ perceptions of quantitative feedback and analyzed the research literature to inform VisLab’s motivation and design. VisLab operationalizes the experiment process using templates and dashboards to make empirical methods amenable for practitioners while supporting sharing and remixing experiments to aid knowledge exchange and validation. We demonstrated the validity of experiments in VisLab and evaluated the usability and potential usefulness of VisLab in visualization design practice.

著者
Jinhan Choi
Boston College, Chestnut Hill, Massachusetts, United States
Changhoon Oh
Yonsei University, Seoul, Korea, Republic of
Yea-Seul Kim
University of Wisconsin-Madison, Madison, Wisconsin, United States
Nam Wook Kim
Boston College, Chestnut Hill, Massachusetts, United States
論文URL

https://doi.org/10.1145/3544548.3581132

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