42-Issue 3

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633338

Browse

Now showing 1 - 20 of 37

Data Stories of Water: Studying the Communicative Role of Data Visualizations within Long-form Journalism
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Garreton, Manuela; Morini, Francesca; Moyano, Daniela Paz; Grün, Gianna-Carina; Parra, Denis; Dörk, Marian; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
We present a methodology for making sense of the communicative role of data visualizations in journalistic storytelling and share findings from surveying water-related data stories. Data stories are a genre of long-form journalism that integrate text, data visualization, and other visual expressions (e.g., photographs, illustrations, videos) for the purpose of data-driven storytelling. In the last decade, a considerable number of data stories about a wide range of topics have been published worldwide. Authors use a variety of techniques to make complex phenomena comprehensible and use visualizations as communicative devices that shape the understanding of a given topic. Despite the popularity of data stories, we, as scholars, still lack a methodological framework for assessing the communicative role of visualizations in data stories. To this extent, we draw from data journalism, visual culture, and multimodality studies to propose an interpretative framework in six stages. The process begins with the analysis of content blocks and framing elements and ends with the identification of dimensions, patterns, and relationships between textual and visual elements. The framework is put to the test by analyzing 17 data stories about water-related issues. Our observations from the survey illustrate how data visualizations can shape the framing of complex topics.
Belief Decay or Persistence? A Mixed-method Study on Belief Movement Over Time
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Gupta, Shrey; Karduni, Alireza; Wall, Emily; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critical movement in an individual's beliefs when elicited immediately after data is shown v. after a temporal delay (e.g., due to forgetfulness or weak incorporation of the data). Our paper investigates the hypothesis that posterior beliefs elicited after a time interval will ''decay'' back towards the prior beliefs compared to the posterior beliefs elicited immediately after new data is presented. In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief distractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants' reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual's belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one's belief system.
Teru Teru Bozu: Defensive Raincloud Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Correll, Michael; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Univariate visualizations like histograms, rug plots, or box plots provide concise visual summaries of distributions. However, each individual visualization may fail to robustly distinguish important features of a distribution, or provide sufficient information for all of the relevant tasks involved in summarizing univariate data. One solution is to juxtapose or superimpose multiple univariate visualizations in the same chart, as in Allen et al.'s [APW*19] ''raincloud plots.'' In this paper I examine the design space of raincloud plots, and, through a series of simulation studies, explore designs where the component visualizations mutually ''defend'' against situations where important distribution features are missed or trivial features are given undue prominence. I suggest a class of ''defensive'' raincloud plot designs that provide good mutual coverage for surfacing distributional features of interest.
Process and Pitfalls of Online Teaching and Learning with Design Study ''Lite'' Methodology: A Retrospective Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Syeda, Uzma Haque; Dunne, Cody; Borkin, Michelle A.; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Design studies are an integral method of visualization research with hundreds of instances in the literature. Although taught as a theory, the practical implementation of design studies is often excluded from visualization pedagogy due to the lengthy time commitments associated with such studies. Recent research has addressed this challenge and developed an expedited design study framework, the Design Study ''Lite'' Methodology (DSLM), which can implement design studies with novice students within just 14 weeks. The framework was developed and evaluated based on five semesters of in-person data visualization courses with 30 students or less and was implemented in conjunction with Service-Learning (S-L).With the growth and popularity of the data visualization field-and the teaching environment created by the COVID-19 pandemic-more academic institutions are offering visualization courses online. Therefore, in this paper, we strengthen and validate the epistemological foundations of the DSLM framework by testing its (1) adaptability to online learning environments and conditions and (2) scalability to larger classes with up to 57 students. We present two online implementations of the DSLM framework, with and without Service-Learning (S-L), to test the adaptability and scalability of the framework. We further demonstrate that the framework can be applied effectively without the S-L component. We reflect on our experience with the online DSLM implementations and contribute a detailed retrospective analysis using thematic analysis and grounded theory methods to draw valuable recommendations and guidelines for future applications of the framework. This work verifies that DSLM can be used successfully in online classes to teach design study methodology. Finally, we contribute novel additions to the DSLM framework to further enhance it for teaching and learning design studies in the classroom.
Unfolding Edges: Adding Context to Edges in Multivariate Graph Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Bludau, Mark-Jan; Dörk, Marian; Tominski, Christian; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Existing work on visualizing multivariate graphs is primarily concerned with representing the attributes of nodes. Even though edges are the constitutive elements of networks, there have been only few attempts to visualize attributes of edges. In this work, we focus on the critical importance of edge attributes for interpreting network visualizations and building trust in the underlying data. We propose 'unfolding of edges' as an interactive approach to integrate multivariate edge attributes dynamically into existing node-link diagrams. Unfolding edges is an in-situ approach that gradually transforms basic links into detailed representations of the associated edge attributes. This approach extends focus+context, semantic zoom, and animated transitions for network visualizations to accommodate edge details on-demand without cluttering the overall graph layout. We explore the design space for the unfolding of edges, which covers aspects of making space for the unfolding, of actually representing the edge context, and of navigating between edges. To demonstrate the utility of our approach, we present two case studies in the context of historical network analysis and computational social science. For these, web-based prototypes were implemented based on which we conducted interviews with domain experts. The experts' feedback suggests that the proposed unfolding of edges is a useful tool for exploring rich edge information of multivariate graphs.
Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Pathmanathan, Nelusa; Öney, Seyda; Becher, Michael; Sedlmair, Michael; Weiskopf, Daniel; Kurzhals, Kuno; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
The distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in realworld environments. Our method is based on a space-time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think-aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.
ParaDime: A Framework for Parametric Dimensionality Reduction
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Hinterreiter, Andreas; Humer, Christina; Kainz, Bernhard; Streit, Marc; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high-dimensional data items in a low-dimensional space while minimizing an objective function. ParaDime builds on the idea that the objective functions of several modern DR techniques result from transformed inter-item relationships. It provides a common interface for specifying these relations and transformations and for defining how they are used within the losses that govern the training process. Through this interface, ParaDime unifies parametric versions of DR techniques such as metric MDS, t-SNE, and UMAP. It allows users to fully customize all aspects of the DR process.We show how this ease of customization makes ParaDime suitable for experimenting with interesting techniques such as hybrid classification/embedding models and supervised DR. This way, ParaDime opens up new possibilities for visualizing high-dimensional data.
visMOP - A Visual Analytics Approach for Multi-omics Pathways
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Brich, Nicolas; Schacherer, Nadine; Hoene, Miriam; Weigert, Cora; Lehmann, Rainer; Krone, Michael; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
We present an approach for the visual analysis of multi-omics data obtained using high-throughput methods. The term ''omics'' denotes measurements of different types of biologically relevant molecules, like the products of gene transcription (transcriptomics) or the abundance of proteins (proteomics). Current popular visualization approaches often only support analyzing each of these omics separately. This, however, disregards the interconnectedness of different biologically relevant molecules and processes. Consequently, it describes the actual events in the organism suboptimally or only partially. Our visual analytics approach for multi-omics data provides a comprehensive overview and details-on-demand by integrating the different omics types in multiple linked views. To give an overview, we map the measurements to known biological pathways and use a combination of a clustered network visualization, glyphs, and interactive filtering. To ensure the effectiveness and utility of our approach, we designed it in close collaboration with domain experts and assessed it using an exemplary workflow with real-world transcriptomics, proteomics, and lipidomics measurements from mice.
DASS Good: Explainable Data Mining of Spatial Cohort Data
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Wentzel, Andrew; Floricel, Carla; Canahuate, Guadalupe; Naser, Mohamed A.; Mohamed, Abdallah S.; Fuller, Clifton David; Dijk, Lisanne van; Marai, G. Elisabeta; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.
Exploring Interpersonal Relationships in Historical Voting Records
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Cantareira, Gabriel Dias; Xing, Yiwen; Cole, Nicholas; Borgo, Rita; Abdul-Rahman, Alfie; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Historical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person's activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.
RectEuler: Visualizing Intersecting Sets using Rectangles
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Paetzold, Patrick; Kehlbeck, Rebecca; Strobelt, Hendrik; Xue, Yumeng; Storandt, Sabine; Deussen, Oliver; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Euler diagrams are a popular technique to visualize set-typed data. However, creating diagrams using simple shapes remains a challenging problem for many complex, real-life datasets. To solve this, we propose RectEuler: a flexible, fully-automatic method using rectangles to create Euler-like diagrams. We use an efficient mixed-integer optimization scheme to place set labels and element representatives (e.g., text or images) in conjunction with rectangles describing the sets. By defining appropriate constraints, we adhere to well-formedness properties and aesthetic considerations. If a dataset cannot be created within a reasonable time or at all, we iteratively split the diagram into multiple components until a drawable solution is found. Redundant encoding of the set membership using dots and set lines improves the readability of the diagram. Our web tool lets users see how the layout changes throughout the optimization process and provides interactive explanations. For evaluation, we perform quantitative and qualitative analysis across different datasets and compare our method to state-of-the-art Euler diagram generation methods.
Human-Computer Collaboration for Visual Analytics: an Agent-based Framework
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Monadjemi, Shayan; Guo, Mengtian; Gotz, David; Garnett, Roman; Ottley, Alvitta; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever-expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent-based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed-initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively.
Evaluating View Management for Situated Visualization in Web-based Handheld AR
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Batch, Andrea; Shin, Sungbok; Liu, Julia; Butcher, Peter W. S.; Ritsos, Panagiotis D.; Elmqvist, Niklas; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
As visualization makes the leap to mobile and situated settings, where data is increasingly integrated with the physical world using mixed reality, there is a corresponding need for effectively managing the immersed user's view of situated visualizations. In this paper we present an analysis of view management techniques for situated 3D visualizations in handheld augmented reality: a shadowbox, a world-in-miniature metaphor, and an interactive tour. We validate these view management solutions through a concrete implementation of all techniques within a situated visualization framework built using a web-based augmented reality visualization toolkit, and present results from a user study in augmented reality accessed using handheld mobile devices.
EuroVis 2023 CGF 42-3: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Bujack, Roxana; Archambault, Daniel; Schreck, Tobias; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Visual Gaze Labeling for Augmented Reality Studies
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Öney, Seyda; Pathmanathan, Nelusa; Becher, Michael; Sedlmair, Michael; Weiskopf, Daniel; Kurzhals, Kuno; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Augmented Reality (AR) provides new ways for situated visualization and human-computer interaction in physical environments. Current evaluation procedures for AR applications rely primarily on questionnaires and interviews, providing qualitative means to assess usability and task solution strategies. Eye tracking extends these existing evaluation methodologies by providing indicators for visual attention to virtual and real elements in the environment. However, the analysis of viewing behavior, especially the comparison of multiple participants, is difficult to achieve in AR. Specifically, the definition of areas of interest (AOIs), which is often a prerequisite for such analysis, is cumbersome and tedious with existing approaches. To address this issue, we present a new visualization approach to define AOIs, label fixations, and investigate the resulting annotated scanpaths. Our approach utilizes automatic annotation of gaze on virtual objects and an image-based approach that also considers spatial context for the manual annotation of objects in the real world. Our results show, that with our approach, eye tracking data from AR scenes can be annotated and analyzed flexibly with respect to data aspects and annotation strategies.
Illustrative Motion Smoothing for Attention Guidance in Dynamic Visualizations
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Eschner, Johannes; Mindek, Peter; Waldner, Manuela; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators' goals are to convey a sense of the scene's overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers' attention through the story, such as highlighting, halos - or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high-frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of context items. We investigated the observers' ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users' ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations.
Beyond Alternative Text and Tables: Comparative Analysis of Visualization Tools and Accessibility Methods
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Kim, Nam Wook; Ataguba, Grace; Joyner, Shakila Cherise; Zhao, Chuangdian; Im, Hyejin; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Modern visualization software and programming libraries have made data visualization construction easier for everyone. However, the extent of accessibility design they support for blind and low-vision people is relatively unknown. It is also unclear how they can improve chart content accessibility beyond conventional alternative text and data tables. To address these issues, we examined the current accessibility features in popular visualization tools, revealing limited support for the standard accessibility methods and scarce support for chart content exploration. Next, we investigate two promising accessibility approaches that provide off-the-shelf solutions for chart content accessibility: structured navigation and conversational interaction. We present a comparative evaluation study and discuss what to consider when incorporating them into visualization tools.
Tac-Anticipator: Visual Analytics of Anticipation Behaviors in Table Tennis Matches
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Wang, Jiachen; Wu, Yihong; Zhang, Xiaolong; Zeng, Yixin; Zhou, Zheng; Zhang, Hui; Xie, Xiao; Wu, Yingcai; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in-lab behaviors, failed to capture the characteristics of in-situ anticipation behaviors in real matches. This research proposes a data-driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac-Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players' anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors.
WYTIWYR: A User Intent-Aware Framework with Multi-modal Inputs for Visualization Retrieval
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Xiao, Shishi; Hou, Yihan; Jin, Cheng; Zeng, Wei; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Retrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations. The retrieved results are expected to conform to both explicit visual attributes (e.g., chart type, colormap) and implicit user intents (e.g., design style, context information) that vary upon application scenarios. However, existing examplebased chart retrieval methods are built upon non-decoupled and low-level visual features that are hard to interpret, while definition-based ones are constrained to pre-defined attributes that are hard to extend. In this work, we propose a new framework, namely WYTIWYR (What-You-Think-Is-What-You-Retrieve), that integrates user intents into the chart retrieval process. The framework consists of two stages: first, the Annotation stage disentangles the visual attributes within the query chart; and second, the Retrieval stage embeds the user's intent with customized text prompt as well as bitmap query chart, to recall targeted retrieval result. We develop a prototype WYTIWYR system leveraging a contrastive language-image pre-training (CLIP) model to achieve zero-shot classification as well as multi-modal input encoding, and test the prototype on a large corpus with charts crawled from the Internet. Quantitative experiments, case studies, and qualitative interviews are conducted. The results demonstrate the usability and effectiveness of our proposed framework.
Ferret: Reviewing Tabular Datasets for Manipulation
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Lange, Devin; Sahai, Shaurya; Phillips, Jeff M.; Lex, Alexander; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
How do we ensure the veracity of science? The act of manipulating or fabricating scientifc data has led to many high-profle fraud cases and retractions. Detecting manipulated data, however, is a challenging and time-consuming endeavor. Automated detection methods are limited due to the diversity of data types and manipulation techniques. Furthermore, patterns automatically fagged as suspicious can have reasonable explanations. Instead, we propose a nuanced approach where experts analyze tabular datasets, e.g., as part of the peer-review process, using a guided, interactive visualization approach. In this paper, we present an analysis of how manipulated datasets are created and the artifacts these techniques generate. Based on these fndings, we propose a suite of visualization methods to surface potential irregularities. We have implemented these methods in Ferret, a visualization tool for data forensics work. Ferret makes potential data issues salient and provides guidance on spotting signs of tampering and differentiating them from truthful data.

Browse

Browsing 42-Issue 3 by Issue Date

Results Per Page

Sort Options