DevLan/vispubs huggingface dataset:
UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization
Authors: Nikolaus Piccolotto, Markus Wallinger, Silvia Miksch, Markus Bögl
Published at Vis, 2024

In various scientific and industrial domains, analyzing multivariate spatial data, i.e., vectors associated with spatial locations, is common practice. To analyze those datasets, analysts may turn to methods such as Spatial Blind Source Separation (SBSS). Designed explicitly for spatial data analysis, SBSS finds latent components in the dataset and is superior to popular non-spatial methods, like PCA. However, when analysts try different tuning parameter settings, the amount of latent components complicates analytical tasks. Based on our years-long collaboration with SBSS researchers, we propose a visualization approach to tackle this challenge. The main component is UnDRground Tubes (UT), a general-purpose idiom combining ideas from set visualization and multidimensional projections. We describe the UT visualization pipeline and integrate UT into an interactive multiple-view system. We demonstrate its effectiveness through interviews with SBSS experts, a qualitative evaluation with visualization experts, and computational experiments. SBSS experts were excited about our approach. They saw many benefits for their work and potential applications for geostatistical data analysis more generally. UT was also well received by visualization experts. Our benchmarks show that UT projections and its heuristics are appropriate.
Unmasking Dunning-Kruger Effect in Visual Reasoning & Judgment
Authors: Mengyu Chen, Yijun Liu, Emily Wall 0001
Published at Vis, 2024

The Dunning-Kruger Effect (DKE) is a metacognitive phenomenon where low-skilled individuals tend to overestimate their competence while high-skilled individuals tend to underestimate their competence. This effect has been observed in a number of domains including humor, grammar, and logic. In this paper, we explore if and how DKE manifests in visual reasoning and judgment tasks. Across two online user studies involving (1) a sliding puzzle game and (2) a scatterplot-based categorization task, we demonstrate that individuals are susceptible to DKE in visual reasoning and judgment tasks: those who performed best underestimated their performance, while bottom performers overestimated their performance. In addition, we contribute novel analyses that correlate susceptibility of DKE with personality traits and user interactions. Our findings pave the way for novel modes of bias detection via interaction patterns and establish promising directions towards interventions tailored to an individual's personality traits. All materials and analyses are in supplemental materials: https://github.com/CAV-Lab/DKE_supplemental.git.
Unveiling How Examples Shape Visualization Design Outcomes
Authors: Hannah K. Bako, Xinyi Liu 0001, Grace Ko, Hyemi Song, Leilani Battle, Zhicheng Liu 0001
Published at Vis, 2024

Visualization designers (e.g., journalists or data analysts) often rely on examples to explore the space of possible designs, yet we have little insight into how examples shape data visualization design outcomes. While the effects of examples have been studied in other disciplines, such as web design or engineering, the results are not readily applicable to visualization due to inconsistencies in findings and challenges unique to visualization design. Towards bridging this gap, we conduct an exploratory experiment involving 32 data visualization designers focusing on the influence of five factors (timing, quantity, diversity, data topic similarity, and data schema similarity) on objectively measurable design outcomes (e.g., numbers of designs and idea transfers). Our quantitative analysis shows that when examples are introduced after initial brainstorming, designers curate examples with topics less similar to the dataset they are working on and produce more designs with a high variation in visualization components. Also, designers copy more ideas from examples with higher data schema similarities. Our qualitative analysis of participants' thought processes provides insights into why designers incorporate examples into their designs, revealing potential factors that have not been previously investigated. Finally, we discuss how our results inform how designers may use examples during design ideation as well as future research on quantifying designs and supporting example-based visualization design. All supplemental materials are available in our OSF repo.
User Experience of Visualizations in Motion: A Case Study and Design Considerations
Authors: Lijie Yao, Federica Bucchieri, Victoria McArthur, Anastasia Bezerianos, Petra Isenberg
Published at Vis, 2024

We present a systematic review, an empirical study, and a first set of considerations for designing visualizations in motion, derived from a concrete scenario in which these visualizations were used to support a primary task. In practice, when viewers are confronted with embedded visualizations, they often have to focus on a primary task and can only quickly glance at a visualization showing rich, often dynamically updated, information. As such, the visualizations must be designed so as not to distract from the primary task, while at the same time being readable and useful for aiding the primary task. For example, in games, players who are engaged in a battle have to look at their enemies but also read the remaining health of their own game character from the health bar over their character's head. Many trade-offs are possible in the design of embedded visualizations in such dynamic scenarios, which we explore in-depth in this paper with a focus on user experience. We use video games as an example of an application context with a rich existing set of visualizations in motion. We begin our work with a systematic review of in-game visualizations in motion. Next, we conduct an empirical user study to investigate how different embedded visualizations in motion designs impact user experience. We conclude with a set of considerations and trade-offs for designing visualizations in motion more broadly as derived from what we learned about video games. All supplemental materials of this paper are available at osf.io/3v8wm/.
VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information-Seeking
Authors: Rui Qiu, Yamei Tu, Po-Yin Yen, Han-Wei Shen
Published at Vis, 2024

In the biomedical domain, visualizing the document embeddings of an extensive corpus has been widely used in informationseeking tasks. However, three key challenges with existing visualizations make it difficult for clinicians to find information efficiently. First, the document embeddings used in these visualizations are generated statically by pretrained language models, which cannot adapt to the user's evolving interest. Second, existing document visualization techniques cannot effectively display how the documents are relevant to users' interest, making it difficult for users to identify the most pertinent information. Third, existing embedding generation and visualization processes suffer from a lack of interpretability, making it difficult to understand, trust and use the result for decision-making. In this paper, we present a novel visual analytics pipeline for user-driven document representation and iterative information seeking (VADIS). VADIS introduces a prompt-based attention model (PAM) that generates dynamic document embedding and document relevance adjusted to the user's query. To effectively visualize these two pieces of information, we design a new document map that leverages a circular grid layout to display documents based on both their relevance to the query and the semantic similarity. Additionally, to improve the interpretability, we introduce a corpus-level attention visualization method to improve the user's understanding of the model focus and to enable the users to identify potential oversight. This visualization, in turn, empowers users to refine, update and introduce new queries, thereby facilitating a dynamic and iterative information-seeking experience. We evaluated VADIS quantitatively and qualitatively on a real-world dataset of biomedical research papers to demonstrate its effectiveness.
VisEval: A Benchmark for Data Visualization in the Era of Large Language Models
Authors: Nan Chen, Yuge Zhang, Jiahang Xu, Kan Ren, Yuqing Yang 0001
Published at Vis, 2024

Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implementations, such as natural language processing and visualization design. Recent advancements in pre-trained large language models (LLMs) are opening new avenues for generating visualizations from natural language. However, the lack of a comprehensive and reliable benchmark hinders our understanding of LLMs' capabilities in visualization generation. In this paper, we address this gap by proposing a new NL2VIS benchmark called VisEval. Firstly, we introduce a high-quality and large-scale dataset. This dataset includes 2,524 representative queries covering 146 databases, paired with accurately labeled ground truths. Secondly, we advocate for a comprehensive automated evaluation methodology covering multiple dimensions, including validity, legality, and readability. By systematically scanning for potential issues with a number of heterogeneous checkers, VisEval provides reliable and trustworthy evaluation outcomes. We run VisEval on a series of state-of-the-art LLMs. Our evaluation reveals prevalent challenges and delivers essential insights for future advancements.
Visual Analysis of Multi-outcome Causal Graphs
Authors: Mengjie Fan, Jinlu Yu, Daniel Weiskopf, Nan Cao 0001, Huai-Yu Wang, Liang Zhou 0001
Published at Vis, 2024

We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for understanding multimorbidity and comorbidity. To support the visual analysis, we collaborated with medical experts to devise two comparative visualization techniques at different stages of the analysis process. First, a progressive visualization method is proposed for comparing multiple state-of-the-art causal discovery algorithms. The method can handle mixed-type datasets comprising both continuous and categorical variables and assist in the creation of a fine-tuned causal graph of a single outcome. Second, a comparative graph layout technique and specialized visual encodings are devised for the quick comparison of multiple causal graphs. In our visual analysis approach, analysts start by building individual causal graphs for each outcome variable, and then, multi-outcome causal graphs are generated and visualized with our comparative technique for analyzing differences and commonalities of these causal graphs. Evaluation includes quantitative measurements on benchmark datasets, a case study with a medical expert, and expert user studies with real-world health research data.
Visual Support for the Loop Grafting Workflow on Proteins
Authors: Filip Opálený, Pavol Ulbrich, Joan Planas-Iglesias, Jan Byska, Jan Stourac, David Bednár, Katarína Furmanová, Barbora Kozlíková
Published at Vis, 2024

In understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on exploring regions in proteins called loops. Analyzing various characteristics of these regions helps the experts design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. We designed a set of interactive visualizations that provide experts with visual support through all the loop grafting pipeline steps. The workflow is divided into several phases, reflecting the steps of the pipeline. Each phase is supported by a specific set of abstracted 2D visual representations of proteins and their loops that are interactively linked with the 3D View of proteins. By sequentially passing through the individual phases, the user shapes the list of loops that are potential candidates for loop grafting. Finally, the actual in-silico insertion of the loop candidates from one protein to the other is performed, and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. We showcase the contribution of our visual support design on a real case scenario changing the enantiomer selectivity of the engineered enzyme. Moreover, we provide the readers with the experts' feedback.
Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration
Authors: Jinrui Wang, Xinhuan Shu, Benjamin Bach, Uta Hinrichs
Published at Vis, 2024

This paper defines, analyzes, and discusses the emerging genre of visualization atlases. We currently witness an increase in web-based, data-driven initiatives that call themselves "atlases" while explaining complex, contemporary issues through data and visualizations: climate change, sustainability, AI, or cultural discoveries. To understand this emerging genre and inform their design, study, and authoring support, we conducted a systematic analysis of 33 visualization atlases and semi-structured interviews with eight visualization atlas creators. Based on our results, we contribute (1) a definition of a visualization atlas as a compendium of (web) pages aimed at explaining and supporting exploration of data about a dedicated topic through data, visualizations and narration. (2) a set of design patterns of 8 design dimensions, (3) insights into the atlas creation from interviews and (4) the definition of 5 visualization atlas genres. We found that visualization atlases are unique in the way they combine i) exploratory visualization, ii) narrative elements from data-driven storytelling and iii) structured navigation mechanisms. They target a wide range of audiences with different levels of domain knowledge, acting as tools for study, communication, and discovery. We conclude with a discussion of current design practices and emerging questions around the ethics and potential real-world impact of visualization atlases, aimed to inform the design and study of visualization atlases.
Visualizing Temporal Topic Embeddings with a Compass
Authors: Daniel Palamarchuk, Lemara Williams, Brian Mayer, Thomas Danielson, Rebecca Faust, Larry M. Deschaine, Chris North 0001
Published at Vis, 2024

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time
VMC: A Grammar for Visualizing Statistical Model Checks
Authors: Ziyang Guo, Alex Kale, Matthew Kay 0001, Jessica Hullman
Published at Vis, 2024

Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: (1) samples of distributions of checkable quantities generated from the model, including predictive distributions for new data and distributions of model parameters; (2) transformations on observed data to facilitate comparison; (3) visual representations of distributions; and (4) layouts to facilitate comparing model samples and observed data. We contribute an implementation of VMC as an R package. We validate VMC by reproducing a set of canonical model check examples, and show how using VMC to generate model checks reduces the edit distance between visualizations relative to existing visualization toolkits. The findings of an interview study with three expert modelers who used VMC highlight challenges and opportunities for encouraging exploration of correct, effective model check visualizations
What Can Interactive Visualization do for Participatory Budgeting in Chicago?
Authors: Alex Kale, Danni Liu, Maria Gabriela Ayala, Harper Schwab, Andrew M. McNutt
Published at Vis, 2024

Participatory budgeting (PB) is a democratic approach to allocating municipal spending that has been adopted in many places in recent years, including in Chicago. Current PB voting resembles a ballot where residents are asked which municipal projects, such as school improvements and road repairs, to fund with a limited budget. In this work, we ask how interactive visualization can benefit PB by conducting a design probe-based interview study (N = 13) with policy workers and academics with expertise in PB, urban planning, and civic HCI. Our probe explores how graphical elicitation of voter preferences and a dashboard of voting statistics can be incorporated into a realistic PB tool. Through qualitative analysis, we find that visualization creates opportunities for city government to set expectations about budget constraints while also granting their constituents greater freedom to articulate a wider range of preferences. However, using visualization to provide transparency about PB requires efforts to mitigate potential access barriers and mistrust. We call for more visualization professionals to help build civic capacity by working in and studying political systems
What University Students Learn In Visualization Classes
Authors: Maryam Hedayati, Matthew Kay 0001
Published at Vis, 2024

As a step towards improving visualization literacy, this work investigates how students approach reading visualizations differently after taking a university-level visualization course. We asked students to verbally walk through their process of making sense of unfamiliar visualizations, and conducted a qualitative analysis of these walkthroughs. Our qualitative analysis found that after taking a visualization course, students engaged with visualizations in more sophisticated ways: they were more likely to exhibit design empathy by thinking critically about the tradeoffs behind why a chart was designed in a particular way, and were better able to deconstruct a chart to make sense of it. We also gave students a quantitative assessment of visualization literacy and found no evidence of scores improving after the class, likely because the test we used focused on a different set of skills than those emphasized in visualization classes. While current measurement instruments for visualization literacy are useful, we propose developing standardized assessments for additional aspects of visualization literacy, such as deconstruction and design empathy. We also suggest that these additional aspects could be incorporated more explicitly in visualization courses
When Refreshable Tactile Displays Meet Conversational Agents: Investigating Accessible Data Presentation and Analysis with Touch and Speech
Authors: Samuel Reinders, Matthew Butler 0002, Ingrid Zukerman, Bongshin Lee, Lizhen Qu, Kim Marriott
Published at Vis, 2024

Despite the recent surge of research efforts to make data visualizations accessible to people who are blind or have low vision (BLV), how to support BLV people's data analysis remains an important and challenging question. As refreshable tactile displays (RTDs) become cheaper and conversational agents continue to improve, their combination provides a promising approach to support BLV people's interactive data exploration and analysis. To understand how BLV people would use and react to a system combining an RTD with a conversational agent, we conducted a Wizard-of-Oz study with 11 BLV participants, where they interacted with line charts, bar charts, and isarithmic maps. Our analysis of participants' interactions led to the identifcation of nine distinct patterns. We also learned that the choice of modalities depended on the type of task and prior experience with tactile graphics, and that participants strongly preferred the combination of RTD and speech to a single modality. In addition, participants with more tactile experience described how tactile images facilitated a deeper engagement with the data and supported independent interpretation. Our fndings will inform the design of interfaces for such interactive mixed-modality systems.
Who Let the Guards Out: Visual Support for Patrolling Games
Authors: Matej Lang, Adam J. Stepánek, Róbert Zvara, Vojtech Rehák, Barbora Kozlíková
Published at Vis, 2024

Effective security patrol management is critical for ensuring safety in diverse environments such as art galleries, airports, and factories. The behavior of patrols in these situations can be modeled by patrolling games. They simulate the behavior of the patrol and adversary in the building, which is modeled as a graph of interconnected nodes representing rooms. The designers of algorithms solving the game face the problem of analyzing complex graph layouts with temporal dependencies. Therefore, appropriate visual support is crucial for them to work effectively. In this paper, we present a novel tool that helps the designers of patrolling games explore the outcomes of the proposed algorithms and approaches, evaluate their success rate, and propose modifications that can improve their solutions. Our tool offers an intuitive and interactive interface, featuring a detailed exploration of patrol routes and probabilities of taking them, simulation of patrols, and other requested features. In close collaboration with experts in designing patrolling games, we conducted three case studies demonstrating the usage and usefulness of our tool. The prototype of the tool, along with exemplary datasets, is available at https://gitlab.fi.muni.cz/formela/strategy-vizualizer.