DevLan/vispubs huggingface dataset:
Should I make it round? Suitability of circular and linear layouts for comparative tasks with matrix and connective data
Authors: Emilia Ståhlbom, Jesper Molin, Anders Ynnerman, Claes Lundström
Published at EuroVis, 2024

Visual representations based on circular shapes are frequently used in visualization applications. One example are circos plots within bioinformatics, which bend graphs into a wheel of information with connective lines running through the center like spokes. The results are aesthetically appealing and impressive visualizations that fit long data sequences into a small quadratic space. However, the authors' experiences are that when asked, a visualization researcher would generally advise against making visualizations with radial layouts. Upon reviewing the literature we found that there is evidence that circular layouts are preferable in some cases, but we found no clear evidence for what layout is preferable for matrices and connective data in particular, which both are common data types in circos plots. In this work, we thus performed a user study to compare circular and linear layouts. The tasks are inspired by genomics data, but our results generalize to many other application areas, involving comparison and connective data. To build the prototype we utilized Gosling, a grammar for visualizing genomics data. We contribute empirical evidence on the suitedness of linear versus circular layouts, adding to the specific and general knowledge concerning perception of circular graphs. In addition, we contribute a case study evaluation of the grammar Gosling as a rapid prototyping language, confirming its utility and providing guidance on suitable areas for future development.
Sparse q-ball imaging towards efficient visual exploration of HARDI data
Authors: Danhua Lei, Ehsan Miandji, Jonas Unger, Ingrid Hotz
Published at EuroVis, 2024

Diffusion-weighted magnetic resonance imaging (D-MRI) is a technique to measure the diffusion of water, in biological tissues. It is used to detect microscopic patterns, such as neural fibers in the living human brain, with many medical and neuroscience applications e.g. for fiber tracking. In this paper, we consider High-Angular Resolution Diffusion Imaging (HARDI) which provides one of the richest representations of water diffusion. It records the movement of water molecules by measuring diffusion under 64 or more directions. A key challenge is that it generates high-dimensional, large, and complex datasets. In our work, we develop a novel representation that exploits the inherent sparsity of the HARDI signal by approximating it as a linear sum of basic atoms in an overcomplete data-driven dictionary using only a sparse set of coefficients. We show that this approach can be efficiently integrated into the standard q-ball imaging pipeline to compute the diffusion orientation distribution function (ODF). Sparse representations have the potential to reduce the size of the data while also giving some insight into the data. To explore the results, we provide a visualization of the atoms of the dictionary and their frequency in the data to highlight the basic characteristics of the data. We present our proposed pipeline and demonstrate its performance on 5 HARDI datasets.
State of the Art of Graph Visualization in non-Euclidean Spaces
Authors: Jacob Miller 0001, Dhruv Bhatia, Stephen G. Kobourov
Published at EuroVis, 2024

Visualizing graphs and networks in non-Euclidean space can have benefits such as natural focus+context in hyperbolic space and the familiarity of interactions in spherical space. Despite work on these topics going back to the mid 1990s, there is no survey, or a part of a survey for this area of research. In this paper we review and categorize over 60 relevant papers and analyze them by geometry, (e.g., spherical, hyperbolic, torus), by contribution (e.g., technique, evaluation, proof, application), and by graph class (e.g., tree, planar, complex).
The State of the Art in Visual Analytics for 3D Urban Data
Authors: Fabio Miranda 0001, Thomas Ortner, Gustavo Moreira, Maryam Hosseini, Milena Vuckovic, Filip Biljecki, Cláudio T. Silva, Marcos Lage, Nivan Ferreira
Published at EuroVis, 2024

Urbanization has amplified the importance of three-dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, and how), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine-grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions.
Topological Characterization and Uncertainty Visualization of Atmospheric Rivers
Authors: Fangfei Lan, Brandi Gamelin, Lin Yan 0003, Jiali Wang, Bei Wang 0001, Hanqi Guo 0001
Published at EuroVis, 2024

Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid-latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.
Transmittance-based Extinction and Viewpoint Optimization
Authors: Paul Himmler, Tobias Günther
Published at EuroVis, 2024

A long-standing challenge in volume visualization is the effective communication of relevant spatial structures that might be hidden due to occlusions. Given a scalar field that indicates the importance of every point in the domain, previous work synthesized volume visualizations by weighted averaging of samples along view rays or by optimizing a spatially-varying extinction field through an energy minimization. This energy minimization, however, did not directly measure the contribution of an individual sample to the final pixel color. In this paper, we measure the visibility of relevant structures directly by incorporating the transmittance into a non-linear energy minimization. For the first time, we not only perform a transmittance-based extinction optimization, we concurrently optimize the camera position to find ideal viewpoints. We derive the partial derivatives for the gradient-based optimization symbolically, which makes the application of automatic differentiation methods unnecessary. The transmittance-based formulation gives a direct visibility measure that is communicated to the user in order to make aware of potentially overlooked relevant structures. Our approach is compatible with any measure of importance and its versatility is demonstrated in multiple data sets.
Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making
Authors: Laura E. Matzen, Breannan C. Howell, Marie Tuft, Kristin Divis
Published at EuroVis, 2024

People frequently make decisions based on uncertain information. Prior research has shown that visualizations of uncertainty can help to support better decision making. However, research has also shown that different representations of the same information can lead to different patterns of decision making. It is crucial for researchers to develop a better scientific understanding of when, why and how different representations of uncertainty lead viewers to make different decisions. This paper seeks to address this need by comparing geospatial visualizations of wildfire risk to verbal descriptions of the same risk. In three experiments, we manipulated the specificity of the uncertain information as well as the visual cues used to encode risk in the visualizations. All three experiments found that participants were more likely to evacuate in response to a hypothetical wildfire if the risk information was presented verbally. When the risk was presented visually, participants were less likely to evacuate, particularly when transparency was used to encode the risk information. Experiment 1 showed that evacuation rates were lower for transparency maps than for other types of visualizations. Experiments 2 and 3 sought to replicate this effect and to test how it related to other factors. Experiment 2 varied the hue used for the transparency maps and Experiment 3 manipulated the salience of the borders between the different risk levels. These experiments showed lower evacuation rates in response to transparency maps regardless of hue. The effect was partially, but not entirely, mitigated by adding salient borders to the transparency maps. Taken together, these experiments show that using transparency to encode information about risk can lead to very different patterns of decision making than other encodings of the same information.
Visual Analytics for Fine-grained Text Classification Models and Datasets
Authors: Munkhtulga Battogtokh, Yiwen Xing, Cosmin Davidescu, Alfie Abdul-Rahman, Michael Luck, Rita Borgo
Published at EuroVis, 2024

In natural language processing (NLP), text classification tasks are increasingly fine-grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse-grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design-and-evaluation process to characterize and tackle the growing requirements in their workflow of developing fine-grained text classification models. The result of this collaboration is the development of SemLa, a novel Visual Analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine-grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation.
Visual Highlighting for Situated Brushing and Linking
Authors: Nina Doerr, Benjamin Lee, Katarina Baricova, Dieter Schmalstieg, Michael Sedlmair
Published at EuroVis, 2024

Brushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques in helping users identify and link physical referents to brushed data marks in a situated scatterplot. In an exploratory virtual reality user study (N=20), we evaluated four highlighting techniques under different physical layouts and tasks. We discuss the effectiveness of these techniques, as well as implications for the design of brushing and linking operations in situated analytics.
"Ah! I see" - Facilitating Process Reflection in Gameplay through a Novel Spatio-Temporal Visualization System
Authors: Sai Siddartha Maram, Erica Kleinman, Jennifer Villareale, Jichen Zhu, Magy Seif El-Nasr
Published at CHI, 2024

Educational games have emerged as potent tools for helping students understand complex concepts and are now ubiquitous in global classrooms, amassing vast data. However, there is a notable gap in research concerning the effective visualization of this data to serve two key functions: (a) guiding students in reflecting upon their game-based learning and (b) aiding them in analyzing peer strategies. In this paper, we engage educators, students, and researchers as essential stakeholders. Taking a Design-Based Research (DBR) approach, we incorporate UX design methods to develop an innovative visualization system that helps players learn through gaining insights from their own and peers' gameplay and strategies.
"Customization is Key": Reconfigurable Textual Tokens for Accessible Data Visualizations
Authors: Shuli Jones, Isabella Pedraza Pineros, Daniel Hajas, Jonathan Zong, Arvind Satyanarayan
Published at CHI, 2024

Customization is crucial for making visualizations accessible to blind and low-vision (BLV) people with widely-varying needs. But what makes for usable or useful customization? We identify four design goals for how BLV people should be able to customize screen-reader-accessible visualizations: presence, or what content is included; verbosity, or how concisely content is presented; ordering, or how content is sequenced; and, duration, or how long customizations are active. To meet these goals, we model a customization as a sequence of content tokens, each with a set of adjustable properties. We instantiate our model by extending Olli, an open-source accessible visualization toolkit, with a settings menu and command box for persistent and ephemeral customization respectively. Through a study with 13 BLV participants, we find that customization increases the ease of identifying and remembering information. However, customization also introduces additional complexity, making it more helpful for users familiar with similar tools.
"Is Text-Based Music Search Enough to Satisfy Your Needs?" A New Way to Discover Music with Images
Authors: Jeongeun Park 0003, Hyorim Shin, Changhoon Oh, Ha Young Kim
Published at CHI, 2024

Music is intrinsically connected to human experience, yet the plethora of choices often renders the search for the ideal piece perplexing, especially when the search terms are ambiguous. This study questions the viability of employing visual data, specifically images, in innovative queries for music search, and it aims to better align search results with users' moods and situational context. We designed and evaluated three prototype systems for music search—TTTune (text-based), VisTune (image-based), and VTTune (hybrid)—to comparatively assess user experience and system usability. In a comprehensive user study involving 236 participants, each participant interacted with one of the systems and subsequently completed post-experimental surveys. A subset of participants also participated in in-depth interviews to further elucidate the potential and the advantages of image-based music retrieval (IMR) systems. Our findings reveal a marked preference for the user experience and usability offered by the IMR approach, as compared with the traditional text-based method. This underscores the potential of the image in an effective search query. Based on these findings, we discuss interface design guidelines tailored for IMR systems and factors affecting system performance, contributing to the evolving landscape of music search methods.
"It is hard to remove from my eye": Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers
Authors: Zeyu Xiong, Shihan Fu, Yanying Zhu, Chenqing Zhu, Xiaojuan Ma, Mingming Fan 0001
Published at CHI, 2024

Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semi-structured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leading cause of human-induced skin problems, especially the difficulty in removing eye makeup. Therefore, we proposed EyeVis, a prototype that can visualize the residual eye makeup and record the time make-up was worn by Xiqu performers. We conducted a 7-day deployment study (N=12) to evaluate EyeVis. Results indicate that EyeVis helps to increase Xiqu performers’ awareness about removing makeup, as well as boosting their confidence and security in skincare. Overall, this work also provides implications for studying the work of people who wear makeup on a daily basis, and helps to promote and preserve the intangible cultural heritage of practitioners.
"Yeah, this graph doesn't show that": Analysis of Online Engagement with Misleading Data Visualizations
Authors: Maxim Lisnic, Alexander Lex, Marina Kogan
Published at CHI, 2024

Attempting to make sense of a phenomenon or crisis, social media users often share data visualizations and interpretations that can be erroneous or misleading. Prior work has studied how data visualizations can mislead, but do misleading visualizations reach a broad social media audience? And if so, do users amplify or challenge misleading interpretations? To answer these questions, we conducted a mixed-methods analysis of the public's engagement with data visualization posts about COVID-19 on Twitter. Compared to posts with accurate visual insights, our results show that posts with misleading visualizations garner more replies in which the audiences point out nuanced fallacies and caveats in data interpretations. Based on the results of our thematic analysis of engagement, we identify and discuss important opportunities and limitations to effectively leveraging crowdsourced assessments to address data-driven misinformation.
'We Do Not Have the Capacity to Monitor All Media': A Design Case Study on Cyber Situational Awareness in Computer Emergency Response Teams
Authors: Marc-André Kaufhold, Thea Riebe, Markus Bayer, Christian Reuter 0001
Published at CHI, 2024

Computer Emergency Response Teams (CERTs) provide advisory, preventive and reactive cybersecurity services for authorities, citizens, and businesses. However, their responsibility of monitoring, analyzing, and communicating cyber threats have become challenging due to the growing volume and varying quality of information disseminated through public channels. Based on a design case study conducted from 2021 to 2023, this paper combines three iterations of expert interviews, design workshops and cognitive walkthroughs to design an automated, cross-platform and real-time cybersecurity dashboard. By adopting the notion of cyber situational awareness, the study extracts user requirements and design heuristics for enhanced threat awareness and mission awareness in CERTs, discussing the aspects of source integration, data management, customizable visualization, relationship awareness, information assessment, software integration, (inter-)organizational collaboration, and communication of stakeholder warnings.
(Re)activate, (Re)direct, (Re)arrange: Exploring the Design Space of Direct Interactions with Flavobacteria
Authors: Clarice Risseeuw, Holly McQuillan, Joana Martins, Elvin Karana
Published at CHI, 2024

HCI designers increasingly engage in the integration of microbes into artefacts, leveraging their distinct biological affordances for novel interactions. While in many explorations the interaction between humans and microbes is mediated, scholars also highlight the potential of direct interactions, such as visualising mechanical distortions or fostering a sense of relationality with nonhumans through eliciting intimate encounters. Seizing upon this potential, our study delves into the realm of direct interactions involving Flavobacteria, recently introduced as a colour-changing interactive medium in HCI. We present a design space for direct interactions where humans can (re)activate, (re)direct, and (re)arrange Flavobacteria's colourations, thereby fostering a personal and dynamic interplay between humans and microbes. With our work, we aspire to provide pathways and ignite inspiration among HCI designers to create living artefacts that cultivate active engagement and heightened attentiveness towards microbial worlds and beyond.
A Human Information Processing Theory of the Interpretation of Visualizations: Demonstrating Its Utility
Authors: Peter C.-H. Cheng, Grecia Garcia Garcia, Daniel Raggi, Mateja Jamnik
Published at CHI, 2024

Providing an approach to model the memory structures that humans build as they use visualizations could be useful for researchers, designers and educators in the field of information visualization. Cheng and colleagues formulated Representation Interpretive Structure Theory (RIST) for that purpose. RIST adopts a human information processing perspective in order to address the immediate, short timescale, cognitive load likely to be experienced by visualization users. RIST is operationalized in a graphical modeling notation and browser-based editor. This paper demonstrates the utility of RIST by showing that (a): RIST models are compatible with established empirical and computational cognitive findings about differences in human performance on alternative representations; (b) they can encompass existing explanations from the literature; and, (c) they provide new explanations about causes of those performance differences.