DevLan/vispubs huggingface dataset:
Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles
Authors: Leah Zhang-Kennedy, Saira Aziz, Oluwafunminitemi (Temi) Oluwadare, Lyndon Pan, Zeyu Wu, Sydney E. C. Lamorea, Soda Li, Michael Sun, Ville Mäkelä
Published at CHI, 2023

Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people’s crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety.
Pearl: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement Analysis
Authors: Weizhou Luo, Zhongyuan Yu, Rufat Rzayev, Marc Satkowski, Stefan Gumhold, Matthew McGinity, Raimund Dachselt
Published at CHI, 2023

This paper presents Pearl, a mixed-reality approach for the analysis of human movement data in situ. As the physical environment shapes human motion and behavior, the analysis of such motion can benefit from the direct inclusion of the environment in the analytical process. We present methods for exploring movement data in relation to surrounding regions of interest, such as objects, furniture, and architectural elements. We introduce concepts for selecting and filtering data through direct interaction with the environment, and a suite of visualizations for revealing aggregated and emergent spatial and temporal relations. More sophisticated analysis is supported through complex queries comprising multiple regions of interest. To illustrate the potential of Pearl, we developed an Augmented Reality-based prototype and conducted expert review sessions and scenario walkthroughs in a simulated exhibition. Our contribution lays the foundation for leveraging the physical environment in the in-situ analysis of movement data.
Perceptual Pat: A Virtual Human Visual System for Iterative Visualization Design
Authors: Sungbok Shin, Sanghyun Hong 0001, Niklas Elmqvist
Published at CHI, 2023

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.
Polagons: Designing and Fabricating Polarized Light Mosaics with User-Defined Color-Changing Behaviors
Authors: Ticha Sethapakdi, Laura Huang, Vivian Hsinyueh Chan, Lung-Pan Cheng, Fernando Fuzinatto Dall'Agnol, Mackenzie Leake, Stefanie Mueller 0001
Published at CHI, 2023

Polarized light mosaics (PLMs) are color-changing structures that alter their appearance based on the orientation of incident polarized light. While a few artists have developed techniques for crafting PLMs by hand, the underlying material properties are difficult to reason about; there exist no tools to bridge the high-level design objectives with the low-level physics knowledge needed to create PLMs. In this paper, we introduce the first system for creating Polagons: machine-made PLMs crafted from cellophane with user-defined color changing behaviors. Our system includes an interface for designing and visualizing Polagons as well as a fabrication process based on laser cutting and welding that requires minimal assembly by the user. We define the design space for Polagons and demonstrate how formalizing the process for creating PLMs can enable new applications in fields such as education, data visualization, and fashion.
ProxSituated Visualization: An Extended Model of Situated Visualization using Proxies for Physical Referents
Authors: Kadek Ananta Satriadi, Andrew Cunningham, Ross T. Smith, Tim Dwyer, Adam Drogemuller, Bruce H. Thomas
Published at CHI, 2023

Existing situated visualization models assume the user is able to directly interact with the objects and spaces to which the data refers (known as physical referents). We review a growing body of work exploring scenarios where the user interacts with a proxy representation of the physical referent rather than immediately with the object itself. This introduces a complex mixture of immediate situatedness and proxies of situatedness that goes beyond the expressiveness of current models. We propose an extended model of situated visualization that encompasses Immediate Situated Visualization and ProxSituated (Proxy of Situated) Visualization. Our model describes a set of key entities involved in proxSituated scenarios and important relationships between them. From this model, we derive design dimensions and apply them to existing situated visualization work. The resulting design space allows us to describe and evaluate existing scenarios, as well as to creatively generate new conceptual scenarios.
Rapsai: Accelerating Machine Learning Prototyping of Multimedia Applications through Visual Programming
Authors: Ruofei Du, Na Li, Jing Jin, Michelle Carney, Scott Miles, Maria Kleiner, Xiuxiu Yuan, Yinda Zhang 0001, Anuva Kulkarni, Xingyu Liu, Ahmed Sabie, Sergio Orts-Escolano, Abhishek Kar, Ping Yu, Ram Iyengar, Adarsh Kowdle, Alex Olwal
Published at CHI, 2023

In recent years, there has been a proliferation of multimedia applications that leverage machine learning (ML) for interactive experiences. Prototyping ML-based applications is, however, still challenging, given complex workflows that are not ideal for design and experimentation. To better understand these challenges, we conducted a formative study with seven ML practitioners to gather insights about common ML evaluation workflows. The study helped us derive six design goals, which informed Rapsai1, a visual programming platform for rapid and iterative development of end-to-end ML-based multimedia applications. Rapsai features a node-graph editor to facilitate interactive characterization and visualization of ML model performance. Rapsai streamlines end-to-end prototyping with interactive data augmentation and model comparison capabilities in its no-coding environment. Our evaluation of Rapsai in four real-world case studies (N=15) suggests that practitioners can accelerate their workflow, make more informed decisions, analyze strengths and weaknesses, and holistically evaluate model behavior with real-world input.
Showing Flow: Comparing Usability of Chord and Sankey Diagrams
Authors: Carl Gutwin, Aristides Mairena, Venkat Bandi
Published at CHI, 2023

Chord and Sankey diagrams are two common techniques for visualizing flows. Chord diagrams use a radial layout with a single circular axis, and Sankey diagrams use a left-to-right layout with two vertical axes. Previous work suggests both strengths and weaknesses of the radial approach, but little is known about the usability and interpretability of these two layout styles for showing flow. We carried out a study where participants answered questions using equivalent Chord and Sankey diagrams. We measured completion time, errors, perceived effort, and preference. Our results show that participants took substantially longer to answer questions with Chord diagrams and made more errors; participants also rated Chord as requiring more effort, and strongly preferred Sankey diagrams. Our study identifies and explains limitations of the popular Chord layout, provides new understanding about radial vs. linear layouts that can help guide visualization designers, and identifies possible design improvements for both visualization types.
Soliloquy: Fostering Poetry Comprehension Using an Interactive Think-aloud Visualization
Authors: Zak Risha, Deniz Sonmez Unal, Erin Walker
Published at CHI, 2023

Complex texts like poetry are distinct from informative texts, requiring additional subprocesses to decode and interpret. Approaching a poem without knowledge of these cognitive strategies can result in confusion and frustration—rather than comprehension. In this work, we explore how interfaces can surface and demonstrate these cognitive processes to novice readers. We introduce Soliloquy, an interface that visualizes the thoughts of an expert as they read and interpret a poem by using animations of text and pop-up tooltips. We evaluate the interface in a five-condition Mechanical Turk study (n=254) by varying the detail of thoughts, including audio, and substituting a static text control. Our study detected a significant difference in comprehension between the detail of thoughts, but not between the Soliloquy interface and static text control. We further investigate this finding in a think-aloud study (n=13), revealing the impact individual differences, experience, and cognitive load could have on Soliloquy’s effectiveness.
Speech-Augmented Cone-of-Vision for Exploratory Data Analysis
Authors: Riccardo Bovo, Daniele Giunchi, Ludwig Sidenmark, Joshua Newn, Hans Gellersen, Enrico Costanza, Thomas Heinis
Published at CHI, 2023

Mutual awareness of visual attention is crucial for successful collaboration. Previous research has explored various ways to represent visual attention, such as field-of-view visualizations and cursor visualizations based on eye-tracking, but these methods have limitations. Verbal communication is often utilized as a complementary strategy to overcome such disadvantages. This paper proposes a novel method that combines verbal communication with the Cone of Vision to improve gaze inference and mutual awareness in VR. We conducted a within-group study with pairs of participants who performed a collaborative analysis of data visualizations in VR. We found that our proposed method provides a better approximation of eye gaze than the approximation provided by head direction. Furthermore, we release the first collaborative head, eyes, and verbal behaviour dataset. The results of this study provide a foundation for investigating the potential of verbal communication as a tool for enhancing visual cues for joint attention.
The tactile dimension: a method for physicalizing touch behaviors
Authors: Laura J. Perovich, Bernice E. Rogowitz, Victoria Crabb, Jack Vogelsang, Sara Hartleben, Dietmar Offenhuber
Published at CHI, 2023

Traces of touch provide valuable insight into how we interact with the physical world. Measuring touch behavior, however, is expensive and imprecise. Utilizing a fluorescent UV tracer powder, we developed a low-cost analog method to capture persistent, high-contrast touch records on arbitrary objects. We describe our process for selecting a tracer, methods for capturing, enhancing, and aggregating traces, and approaches to examining qualitative aspects of the user experience. Three user studies demonstrate key features of this method. First, we show that it provides clear and durable traces on objects representative of scientific visualization, physicalization, and product design. Second, we demonstrate how this method could be used to study touch perception, by measuring how task and narrative framing elicit different touch behaviors on the same object. Third, we demonstrate how this method can be used to evaluate data physicalizations by observing how participants touch two different physicalizations of COVID-19 time-series data.
This Watchface Fits with my Tattoos: Investigating Customisation Needs and Preferences in Personal Tracking
Authors: Rúben Gouveia 0001, Daniel A. Epstein
Published at CHI, 2023

People engage in self-tracking with diverse data collection and visualisation needs and preferences. Customisable self-tracking tools offer the potential to support individualized preferences by letting people make changes to the aesthetics and functionality of tracker displays. In this paper, we use the customisation options offered by the displays of commercial fitness smartwatches as a lens to investigate when, why and how 386 self-trackers engage in customisations in their daily lives. We find that people largely customise their trackers’ display frequently, multiple times a day, or not at all, with frequent customisations reflecting situational data, aesthetic and personal meaning needs. We discuss implications for the design of tracking tools aiming to support customisation and discuss the utility of customisations towards goal scaffolding and maintaining interest in tracking.
Through Their Eyes and In Their Shoes: Providing Group Awareness During Collaboration Across Virtual Reality and Desktop Platforms
Authors: David Saffo, Andrea Batch, Cody Dunne, Niklas Elmqvist
Published at CHI, 2023

Many collaborative data analysis situations benefit from collaborators utilizing different platforms. However, maintaining group awareness between team members using diverging devices is difficult, not least because common ground diminishes. A person using head-mounted VR cannot physically see a user on a desktop computer even while co-located, and the desktop user cannot easily relate to the VR user’s 3D workspace. To address this, we propose the “eyes-and-shoes” principles for group awareness and abstract them into four levels of techniques. Furthermore, we evaluate these principles with a qualitative user study of 6 participant pairs synchronously collaborating across distributed desktop and VR head-mounted devices. In this study, we vary the group awareness techniques between participants and explore two visualization contexts within participants. The results of this study indicate that the more visual metaphors and views of participants diverge, the greater the level of group awareness is needed. A copy of this paper, the study preregistration, and all supplemental materials required to reproduce the study are available on OSF (link).
Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work
Authors: Jen Rogers, Anamaria Crisan
Published at CHI, 2023

Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this research, we construct a taxonomy of data work artifacts that captures AutoML and human processes. We present a rigorous methodology for its creation and discuss its transferability to the visual design process. We operationalize the taxonomy through the development of AutoML Trace a visual interactive sketch showing both the context and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage scenario with an enterprise software development team. Collectively, our research process and findings explore challenges and fruitful avenues for developing data visualization tools that interrogate the sociotechnical relationships in automated data work. Availability of Supplemental Materials: https://osf.io/3nmyj/?view_only=19962103d58b45d289b5c83421f48b36
Troubling Collaboration: Matters of Care for Visualization Design Study
Authors: Derya Akbaba, Devin Lange, Michael Correll, Alexander Lex, Miriah Meyer
Published at CHI, 2023

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 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.
Tutor In-sight: Guiding and Visualizing Students' Attention with Mixed Reality Avatar Presentation Tools
Authors: Santawat Thanyadit, Matthias Heintz 0001, Effie L.-C. Law
Published at CHI, 2023

Remote conferencing systems are increasingly used to supplement or even replace in-person teaching. However, prevailing conferencing systems restrict the teacher’s representation to a webcam live-stream, hamper the teacher’s use of body-language, and result in students’ decreased sense of co-presence and participation. While Virtual Reality (VR) systems may increase student engagement, the teacher may not have the time or expertise to conduct the lecture in VR. To address this issue and bridge the requirements between students and teachers, we have developed Tutor In-sight, a Mixed Reality (MR) avatar augmented into the student’s workspace based on four design requirements derived from the existing literature, namely: integrated virtual with physical space, improved teacher’s co-presence through avatar, direct attention with auto-generated body language, and usable workflow for teachers. Two user studies were conducted from the perspectives of students and teachers to determine the advantages of Tutor In-sight in comparison to two existing conferencing systems, Zoom (video-based) and Mozilla Hubs (VR-based). The participants of both studies favoured Tutor In-sight. Among others, this main finding indicates that Tutor In-sight satisfied the needs of both teachers and students. In addition, the participants’ feedback was used to empirically determine the four main teacher requirements and the four main student requirements in order to improve the future design of MR educational tools.
UndoPort: Exploring the Influence of Undo-Actions for Locomotion in Virtual Reality on the Efficiency, Spatial Understanding and User Experience
Authors: Florian Müller 0003, Arantxa Ye, Dominik Schön, Julian Rasch
Published at CHI, 2023

When we get lost in Virtual Reality (VR) or want to return to a previous location, we use the same methods of locomotion for the way back as for the way forward. This is time-consuming and requires additional physical orientation changes, increasing the risk of getting tangled in the headsets’ cables. In this paper, we propose the use of undo actions to revert locomotion steps in VR. We explore eight different variations of undo actions as extensions of point&teleport, based on the possibility to undo position and orientation changes together with two different visualizations of the undo step (discrete and continuous). We contribute the results of a controlled experiment with 24 participants investigating the efficiency and orientation of the undo techniques in a radial maze task. We found that the combination of position and orientation undo together with a discrete visualization resulted in the highest efficiency without increasing orientation errors.
VisLab: Enabling Visualization Designers to Gather Empirically Informed Design Feedback
Authors: Jinhan Choi, Changhoon Oh, Yea-Seul Kim, Nam Wook Kim
Published at CHI, 2023

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.
Visual Belief Elicitation Reduces the Incidence of False Discovery
Authors: Ratanond Koonchanok, Gauri Yatindra Tawde, Gokul Ragunandhan Narayanasamy, Shalmali Walimbe, Khairi Reda
Published at CHI, 2023

Visualization supports exploratory data analysis (EDA), but EDA frequently presents spurious charts, which can mislead people into drawing unwarranted conclusions. We investigate interventions to prevent false discovery from visualized data. We evaluate whether eliciting analyst beliefs helps guard against the over-interpretation of noisy visualizations. In two experiments, we exposed participants to both spurious and ‘true’ scatterplots, and assessed their ability to infer data-generating models that underlie those samples. Participants who underwent prior belief elicitation made 21% more correct inferences along with 12% fewer false discoveries. This benefit was observed across a variety of sample characteristics, suggesting broad utility to the intervention. However, additional interventions to highlight counterevidence and sample uncertainty did not provide significant advantage. Our findings suggest that lightweight, belief-driven interactions can yield a reliable, if moderate, reduction in false discovery. This work also suggests future directions to improve visual inference and reduce bias.
Visual Task Performance and Spatial Abilities: An Investigation of Artists and Mathematicians
Authors: Sara Tandon, Alfie Abdul-Rahman, Rita Borgo
Published at CHI, 2023

This study builds on past research to present a domain-specific empirical investigation of artists and math & computer scientists on their respective relationships to, perceptions of, and interactions with data visualization. We conducted a three-phase study utilizing mixed-methods to investigate performance on visual and text representations of data between domains. Our findings evidenced how math & computer scientists are proficient utilizing text representations of data while artists benefit more from visual chart representations. Finally, we present perspectives from artists to gain an understanding of their approach to visual and mathematical tasks. Our findings indicate that artists are especially adept at statistical visual tasks and that development of cognitive skills could be fostered by individuals to potentially benefit visualization task performance.