DevLan/vispubs huggingface dataset:
GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation
Authors: Fan Lei, Yuxin Ma, A. Stewart Fotheringham, Elizabeth A. Mack, Ziqi Li, Mehak Sachdeva, Sarah Bardin, Ross Maciejewski
Published at Vis, 2023

Geographic regression models of various descriptions are often applied to identify patterns and anomalies in the determinants of spatially distributed observations. These types of analyses focus on answering why questions about underlying spatial phenomena, e.g., why is crime higher in this locale, why do children in one school district outperform those in another, etc.? Answers to these questions require explanations of the model structure, the choice of parameters, and contextualization of the findings with respect to their geographic context. This is particularly true for local forms of regression models which are focused on the role of locational context in determining human behavior. In this paper, we present GeoExplainer, a visual analytics framework designed to support analysts in creating explanative documentation that summarizes and contextualizes their spatial analyses. As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results. As analysts explore the model results, all visualizations and annotations can be captured in an interactive report generation widget. We demonstrate our framework using a case study modeling the determinants of voting in the 2016 US Presidential Election.
ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics
Authors: Matthew Kay 0001
Published at Vis, 2023

The grammar of graphics is ubiquitous, providing the foundation for a variety of popular visualization tools and toolkits. Yet support for uncertainty visualization in the grammar graphics—beyond simple variations of error bars, uncertainty bands, and density plots—remains rudimentary. Research in uncertainty visualization has developed a rich variety of improved uncertainty visualizations, most of which are difficult to create in existing grammar of graphics implementations. ggdist, an extension to the popular ggplot2 grammar of graphics toolkit, is an attempt to rectify this situation. ggdist unifies a variety of uncertainty visualization types through the lens of distributional visualization, allowing functions of distributions to be mapped to directly to visual channels (aesthetics), making it straightforward to express a variety of (sometimes weird!) uncertainty visualization types. This distributional lens also offers a way to unify Bayesian and frequentist uncertainty visualization by formalizing the latter with the help of confidence distributions. In this paper, I offer a description of this uncertainty visualization paradigm and lessons learned from its development and adoption: ggdist has existed in some form for about six years (originally as part of the tidybayes R package for post-processing Bayesian models), and it has evolved substantially over that time, with several rewrites and API re-organizations as it changed in response to user feedback and expanded to cover increasing varieties of uncertainty visualization types. Ultimately, given the huge expressive power of the grammar of graphics and the popularity of tools built on it, I hope a catalog of my experience with ggdist will provide a catalyst for further improvements to formalizations and implementations of uncertainty visualization in grammar of graphics ecosystems. A free copy of this paper is available at https://osf.io/2gsz6. All supplemental materials are available at https://github.com/mjskay/ggdist-paper and are archived on Zenodo at doi:10.5281/zenodo.7770984.
Global Topology of 3D Symmetric Tensor Fields
Authors: Shih-Hsuan Hung, Yue Zhang 0009, Eugene Zhang
Published at Vis, 2023

There have been recent advances in the analysis and visualization of 3D symmetric tensor fields, with a focus on the robust extraction of tensor field topology. However, topological features such as degenerate curves and neutral surfaces do not live in isolation. Instead, they intriguingly interact with each other. In this paper, we introduce the notion of topological graph for 3D symmetric tensor fields to facilitate global topological analysis of such fields. The nodes of the graph include degenerate curves and regions bounded by neutral surfaces in the domain. The edges in the graph denote the adjacency information between the regions and degenerate curves. In addition, we observe that a degenerate curve can be a loop and even a knot and that two degenerate curves (whether in the same region or not) can form a link. We provide a definition and theoretical analysis of individual degenerate curves in order to help understand why knots and links may occur. Moreover, we differentiate between wedges and trisectors, thus making the analysis more detailed about degenerate curves. We incorporate this information into the topological graph. Such a graph can not only reveal the global structure in a 3D symmetric tensor field but also allow two symmetric tensor fields to be compared. We demonstrate our approach by applying it to solid mechanics and material science data sets.
Guaranteed Visibility in Scatterplots with Tolerance
Authors: Loann Giovannangeli, Frédéric Lalanne, Romain Giot, Romain Bourqui
Published at Vis, 2023

In 2D visualizations, visibility of every datum's representation is crucial to ease the completion of visual tasks. Such a guarantee is barely respected in complex visualizations, mainly because of overdraws between datum representations that hide parts of the information (e.g., outliers). The literature proposes various Layout Adjustment algorithms to improve the readability of visualizations that suffer from this issue. Manipulating the data in high-dimensional, geometric or visual space; they rely on different strategies with their own strengths and weaknesses. Moreover, most of these algorithms are computationally expensive as they search for an exact solution in the geometric space and do not scale well to large datasets. This article proposes GIST, a layout adjustment algorithm that aims at optimizing three criteria: (i) node visibility guarantee (at least 1 pixel), (ii) node size maximization, and (iii) the original layout preservation. This is achieved by combining a search for the maximum node size that enables to draw all the data points without overlaps, with a limited budget of movements (i.e., limiting the distortions of the original layout). The method's basis relies on the idea that it is not necessary for two data representations to be strictly not overlapping in order to guarantee their visibility in visual space. Our algorithm therefore uses a tolerance in the geometric space to determine the overlaps between pairs of data. The tolerance is optimized such that the approximation computed in the geometric space can lead to visualization without noticeable overdraw after the data rendering rasterization. In addition, such an approximation helps to ease the algorithm's convergence as it reduces the number of constraints to resolve, enabling it to handle large datasets. We demonstrate the effectiveness of our approach by comparing its results to those of state-of-the-art methods on several large datasets.
Guided Visual Analytics for Image Selection in Time and Space
Authors: Ignacio Pérez-Messina, Davide Ceneda, Silvia Miksch
Published at Vis, 2023

Unexploded Ordnance (UXO) detection, the identification of remnant active bombs buried underground from archival aerial images, implies a complex workflow involving decision-making at each stage. An essential phase in UXO detection is the task of image selection, where a small subset of images must be chosen from archives to reconstruct an area of interest (AOI) and identify craters. The selected image set must comply with good spatial and temporal coverage over the AOI, particularly in the temporal vicinity of recorded aerial attacks, and do so with minimal images for resource optimization. This paper presents a guidance-enhanced visual analytics prototype to select images for UXO detection. In close collaboration with domain experts, our design process involved analyzing user tasks, eliciting expert knowledge, modeling quality metrics, and choosing appropriate guidance. We report on a user study with two real-world scenarios of image selection performed with and without guidance. Our solution was well-received and deemed highly usable. Through the lens of our task-based design and developed quality measures, we observed guidance-driven changes in user behavior and improved quality of analysis results. An expert evaluation of the study allowed us to improve our guidance-enhanced prototype further and discuss new possibilities for user-adaptive guidance.
Handling Non-Visible Referents in Situated Visualizations
Authors: Ambre Assor, Arnaud Prouzeau, Martin Hachet, Pierre Dragicevic
Published at Vis, 2023

Situated visualizations are a type of visualization where data is presented next to its physical referent (i.e., the physical object, space, or person it refers to), often using augmented-reality displays. While situated visualizations can be beneficial in various contexts and have received research attention, they are typically designed with the assumption that the physical referent is visible. However, in practice, a physical referent may be obscured by another object, such as a wall, or may be outside the user's visual field. In this paper, we propose a conceptual framework and a design space to help researchers and user interface designers handle non-visible referents in situated visualizations. We first provide an overview of techniques proposed in the past for dealing with non-visible objects in the areas of 3D user interfaces, 3D visualization, and mixed reality. From this overview, we derive a design space that applies to situated visualizations and employ it to examine various trade-offs, challenges, and opportunities for future research in this area.
HealthPrism: A Visual Analytics System for Exploring Children's Physical and Mental Health Profiles with Multimodal Data
Authors: Zhihan Jiang, Handi Chen, Rui Zhou, Jing Deng, Xinchen Zhang, Running Zhao, Cong Xie, Yifang Wang 0001, Edith C. H. Ngai
Published at Vis, 2023

The correlation between children's personal and family characteristics (e.g., demographics and socioeconomic status) and their physical and mental health status has been extensively studied across various research domains, such as public health, medicine, and data science. Such studies can provide insights into the underlying factors affecting children's health and aid in the development of targeted interventions to improve their health outcomes. However, with the availability of multiple data sources, including context data (i.e., the background information of children) and motion data (i.e., sensor data measuring activities of children), new challenges have arisen due to the large-scale, heterogeneous, and multimodal nature of the data. Existing statistical hypothesis-based and learning model-based approaches have been inadequate for comprehensively analyzing the complex correlation between multimodal features and multi-dimensional health outcomes due to the limited information revealed. In this work, we first distill a set of design requirements from multiple levels through conducting a literature review and iteratively interviewing 11 experts from multiple domains (e.g., public health and medicine). Then, we propose HealthPrism, an interactive visual and analytics system for assisting researchers in exploring the importance and influence of various context and motion features on children's health status from multi-level perspectives. Within HealthPrism, a multimodal learning model with a gate mechanism is proposed for health profiling and cross-modality feature importance comparison. A set of visualization components is designed for experts to explore and understand multimodal data freely. We demonstrate the effectiveness and usability of HealthPrism through quantitative evaluation of the model performance, case studies, and expert interviews in associated domains.
Heuristics for Supporting Cooperative Dashboard Design
Authors: Vidya Setlur, Michael Correll, Arvind Satyanarayan, Melanie Tory
Published at Vis, 2023

Dashboards are no longer mere static displays of metrics; through functionality such as interaction and storytelling, they have evolved to support analytic and communicative goals like monitoring and reporting. Existing dashboard design guidelines, however, are often unable to account for this expanded scope as they largely focus on best practices for visual design. In contrast, we frame dashboard design as facilitating an analytical conversation: a cooperative, interactive experience where a user may interact with, reason about, or freely query the underlying data. By drawing on established principles of conversational flow and communication, we define the concept of a cooperative dashboard as one that enables a fruitful and productive analytical conversation, and derive a set of 39 dashboard design heuristics to support effective analytical conversations. To assess the utility of this framing, we asked 52 computer science and engineering graduate students to apply our heuristics to critique and design dashboards as part of an ungraded, opt-in homework assignment. Feedback from participants demonstrates that our heuristics surface new reasons dashboards may fail, and encourage a more fluid, supportive, and responsive style of dashboard design. Our approach suggests several compelling directions for future work, including dashboard authoring tools that better anticipate conversational turn-taking, repair, and refinement and extending cooperative principles to other analytical workflows.
HoopInSight: Analyzing and Comparing Basketball Shooting Performance Through Visualization
Authors: Yu Fu, John T. Stasko
Published at Vis, 2023

Data visualization has the power to revolutionize sports. For example, the rise of shot maps has changed basketball strategy by visually illustrating where “good/bad” shots are taken from. As a result, professional basketball teams today take shots from very different positions on the court than they did 20 years ago. Although the shot map has transformed many facets of the game, there is still much room for improvement to support richer and more complex analytical tasks. More specifically, we believe that the lack of sufficient interactivity to support various analytical queries and the inability to visually compare differences across situations are significant limitations of current shot maps. To address these limitations and showcase new possibilities, we designed and developed HoopInSight, an interactive visualization system that centers around a novel spatial comparison visual technique, enhancing the capabilities of shot maps in basketball analytics. This article presents the system, with a focus on our proposed visual technique and its accompanying interactions, all designed to promote comparison of two different scenarios. Furthermore, we provide reflections on and a discussion of relevant issues, including considerations for designing spatial comparison techniques, the scalability and transferability of this approach, and the benefits and pitfalls of designing as domain experts.
Image or Information? Examining the Nature and Impact of Visualization Perceptual Classification
Authors: Anjana Arunkumar, Lace M. K. Padilla, Gi-Yeul Bae, Chris Bryan
Published at Vis, 2023

How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory encoding affects its retrieval. This exploratory work examines the influence of various design elements on a user's perception of a chart. Specifically, which design elements lead to perceptions of visualization as an image (aims to provide visual references, evoke emotions, express creativity, and inspire philosophic thought) or as information (aims to present complex data, information, or ideas concisely and promote analytical thinking)? Understanding how design elements contribute to viewers perceiving a visualization more as an image or information will help designers decide which elements to include to achieve their communication goals. For this study, we annotated 500 visualizations and analyzed the responses of 250 online participants, who rated the visualizations on a bilinear scale as ‘image’ or ‘information.’ We then conducted an in-person study ($n = 101$) using a free recall task to examine how the image/information ratings and design elements impacted memory. The results revealed several interesting findings: Image-rated visualizations were perceived as more aesthetically ‘appealing,’ ‘enjoyable,’ and ‘pleasing.’ Information-rated visualizations were perceived as less ‘difficult to understand’ and more aesthetically ‘likable’ and ‘nice,’ though participants expressed higher ‘positive’ sentiment when viewing image-rated visualizations and felt less ‘guided to a conclusion.’ The presence of axes and text annotations heavily influenced the likelihood of participants rating the visualization as ‘information.’ We also found different patterns among participants that were older. Importantly, we show that visualizations internalized as ‘images’ are less effective in conveying trends and messages, though they elicit a more positive emotional judgment, while ‘informative’ visualizations exhibit annotation focused recall and elicit a more positive design judgment. We discuss the implications of this dissociation between aesthetic pleasure and perceived ease of use in visualization design.
InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks
Authors: Yanna Lin, Haotian Li 0001, Leni Yang, Aoyu Wu, Huamin Qu
Published at Vis, 2023

Computational notebooks have become increasingly popular for exploratory data analysis due to their ability to support data exploration and explanation within a single document. Effective documentation for explaining chart findings during the exploration process is essential as it helps recall and share data analysis. However, documenting chart findings remains a challenge due to its time-consuming and tedious nature. While existing automatic methods alleviate some of the burden on users, they often fail to cater to users' specific interests. In response to these limitations, we present InkSight, a mixed-initiative computational notebook plugin that generates finding documentation based on the user's intent. InkSight allows users to express their intent in specific data subsets through sketching atop visualizations intuitively. To facilitate this, we designed two types of sketches, i.e., open-path and closed-path sketch. Upon receiving a user's sketch, InkSight identifies the sketch type and corresponding selected data items. Subsequently, it filters data fact types based on the sketch and selected data items before employing existing automatic data fact recommendation algorithms to infer data facts. Using large language models (GPT-3.5), InkSight converts data facts into effective natural language documentation. Users can conveniently fine-tune the generated documentation within InkSight. A user study with 12 participants demonstrated the usability and effectiveness of InkSight in expressing user intent and facilitating chart finding documentation.
InnovationInsights: A Visual Analytics Approach for Understanding the Dual Frontiers of Science and Technology
Authors: Yifang Wang 0001, Yifan Qian, Xiaoyu Qi, Nan Cao 0001, Dashun Wang
Published at Vis, 2023

Science has long been viewed as a key driver of economic growth and rising standards of living. Knowledge about how scientific advances support marketplace inventions is therefore essential for understanding the role of science in propelling real-world applications and technological progress. The increasing availability of large-scale datasets tracing scientific publications and patented inventions and the complex interactions among them offers us new opportunities to explore the evolving dual frontiers of science and technology at an unprecedented level of scale and detail. However, we lack suitable visual analytics approaches to analyze such complex interactions effectively. Here we introduce InnovationInsights, an interactive visual analysis system for researchers, research institutions, and policymakers to explore the complex linkages between science and technology, and to identify critical innovations, inventors, and potential partners. The system first identifies important associations between scientific papers and patented inventions through a set of statistical measures introduced by our experts from the field of the Science of Science. A series of visualization views are then used to present these associations in the data context. In particular, we introduce the Interplay Graph to visualize patterns and insights derived from the data, helping users effectively navigate citation relationships between papers and patents. This visualization thereby helps them identify the origins of technical inventions and the impact of scientific research. We evaluate the system through two case studies with experts followed by expert interviews. We further engage a premier research institution to test-run the system, helping its institution leaders to extract new insights for innovation. Through both the case studies and the engagement project, we find that our system not only meets our original goals of design, allowing users to better identify the sources of technical inventions and to understand the broad impact of scientific research; it also goes beyond these purposes to enable an array of new applications for researchers and research institutions, ranging from identifying untapped innovation potential within an institution to forging new collaboration opportunities between science and industry.
Interactive Design and Optics-Based Visualization of Arbitrary Non-Euclidean Kaleidoscopic Orbifolds
Authors: Jinta Zheng, Eugene Zhang, Yue Zhang 0009
Published at Vis, 2023

Orbifolds are a modern mathematical concept that arises in the research of hyperbolic geometry with applications in computer graphics and visualization. In this paper, we make use of rooms with mirrors as the visual metaphor for orbifolds. Given any arbitrary two-dimensional kaleidoscopic orbifold, we provide an algorithm to construct a Euclidean, spherical, or hyperbolic polygon to match the orbifold. This polygon is then used to create a room for which the polygon serves as the floor and the ceiling. With our system that implements Möbius transformations, the user can interactively edit the scene and see the reflections of the edited objects. To correctly visualize non-Euclidean orbifolds, we adapt the rendering algorithms to account for the geodesics in these spaces, which light rays follow. Our interactive orbifold design system allows the user to create arbitrary two-dimensional kaleidoscopic orbifolds. In addition, our mirror-based orbifold visualization approach has the potential of helping our users gain insight on the orbifold, including its orbifold notation as well as its universal cover, which can also be the spherical space and the hyperbolic space.