EuroVisShort2024

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https://diglib.eg.org/handle/10.2312/3606997

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Comparative Analysis of Timeline-based Visualizations for Dynamic Overlapping Sets
(The Eurographics Association, 2024) Pron, Mariana; Agarwal, Shivam; Poddar, Madhav; Beck, Fabian; Tominski, Christian; Waldner, Manuela; Wang, Bei
Timeline-based set visualizations provide an overview of how overlapping categorical data evolves. We study three different visualization techniques of such type and made minor modifications to visualize the same data in a two-fold comparison. First, we contrast their encodings and interactions through a conceptual analysis. Second, in a user study with 28 participants, we evaluate their performance regarding different analysis tasks for dynamic sets and record user feedback along various dimensions.
Data-Driven Computation of Probabilistic Marching Cubes for Efficient Visualization of Level-Set Uncertainty
(The Eurographics Association, 2024) Athawale, Tushar M.; Wang, Zhe; Johnson, Chris R.; Pugmire, David; Tominski, Christian; Waldner, Manuela; Wang, Bei
Uncertainty visualization is an important emerging research area. Being able to visualize data uncertainty can help scientists improve trust in analysis and decision-making. However, visualizing uncertainty can add computational overhead, which can hinder the efficiency of analysis. In this paper, we propose novel data-driven techniques to reduce the computational requirements of the probabilistic marching cubes (PMC) algorithm. PMC is an uncertainty visualization technique that studies how uncertainty in data affects level-set positions. However, the algorithm relies on expensive Monte Carlo (MC) sampling for the multivariate Gaussian uncertainty model because no closed-form solution exists for the integration of multivariate Gaussian. In this work, we propose the eigenvalue decomposition and adaptive probability model techniques that reduce the amount of MC sampling in the original PMC algorithm and hence speed up the computations. Our proposed methods produce results that show negligible differences compared with the original PMC algorithm demonstrated through metrics, including root mean squared error, maximum error, and difference images. We demonstrate the performance and accuracy evaluations of our data-driven methods through experiments on synthetic and real datasets.
Embedded Temporal Data Visualizations in an Urban Environment for Casual Exploration
(The Eurographics Association, 2024) Nagel, Till; Huber, Christoph; Petzold, Ekkehard; Humbert, Sophie; Tominski, Christian; Waldner, Manuela; Wang, Bei
We investigate situated and embedded visualizations to enhance casual urban data engagement. Presenting a design concept for embedding temporal data visualizations onto flat surfaces, we explore features that integrate these visualizations within their physical contexts. Through a mobile application utilizing location-based augmented reality to visualize traffic, we demonstrate the feasibility of our designs in real-world settings. This approach not only aims to improve understanding of urban phenomena but also to enrich user experiences, offering a novel method for urban data visualization that emphasizes user engagement.
EuroVis 2024 Short Papers: Frontmatter
(The Eurographics Association, 2024) Tominski, Christian; Waldner, Manuela; Wang, Bei; Tominski, Christian; Waldner, Manuela; Wang, Bei
Exploring Electron Density Evolution using Merge Tree Mappings
(The Eurographics Association, 2024) Wetzels, Florian; Masood, Talha Bin; List, Nanna Holmgaard; Hotz, Ingrid; Garth, Christoph; Tominski, Christian; Waldner, Manuela; Wang, Bei
This paper presents a prototypical visualization for the analysis of light-induced dynamics in molecules. It utilizes topological distances to find temporal patterns in scalar fields representing the electronic structure of such molecules and to illustrate the evolution of their features. It also provides a means to correlate these findings to the geometric evolution of the molecules.
FR-glyphs for Multidimensional Categorical Data
(The Eurographics Association, 2024) Canlon, Delorean C.; Paulovich, Fernando; Tennekes, Martijn; Tominski, Christian; Waldner, Manuela; Wang, Bei
Multivariate categorical data analysis is challenging, especially when geographical information is present. Despite the widespread existence of such datasets, the current visualization solutions only typically represent frequencies of attributes, which can be misleading if uncorrelated attributes exist. We present the frequency-relation-glyphs, or FR-glyphs, as an alternative solution for these issues. FR-glyphs can (1) show deviations in the attribute's frequencies and (2) relations between combined sets of attributes. Furthermore, they can be added to geographical maps to compare multiple regions, such as provinces. We used the Bestand geRegistreerde Ongevallen in Nederland (BRON) dataset, which includes bicycle incidents, to show the usefulness of the FR-glyphs and evaluate them with stakeholders.
Gridded-glyphmaps for supporting Geographic Multicriteria Decision Analysis
(The Eurographics Association, 2024) Laksono, Dany; Slingsby, Aidan; Jianu, Radu; Tominski, Christian; Waldner, Manuela; Wang, Bei
Integrating human intuition into data-driven decisions is challenging. Multicriteria Decision Analysis (MCDA) provides a structured framework for evaluating multiple criteria but often fails to capture the nuanced preferences of decision-makers. Geovisualisation tools can help understand data, but representing intricate relationships within MCDA models, especially with multivariate data, remains difficult. This study proposes a solution by combining MCDA and geovisualisation strengths using gridded-glyphmaps. This approach enables interactive exploration of multivariate geospatial data, allowing decision-makers to adjust parameter weights in real-time and dynamically assess decision alternatives. We demonstrate this approach's effectiveness through decarbonisation planning scenarios in Cambridge, UK. Our glyphs represent multiple variables' interplay, allowing for flexible criteria weight refinement. Discretising the data into grids reveals patterns and relationships missed by traditional representations like choropleth maps. Our approach demonstrates how gridded-glyphmap visualisation within an MCDA model fosters insights and transparency in decarbonisation planning scenarios.
Highways and Tunnels: Force Feedback Guidance for Visualisations
(The Eurographics Association, 2024) Alrøe, Sarah Fjelsted; Hoggan, Eve; Schulz, Hans-Jörg; Tominski, Christian; Waldner, Manuela; Wang, Bei
Non-visual methods of user guidance in visualisations are still relatively underexplored. This paper aims to address this, by establishing a foundation for appropriately using haptic force feedback in a pointing device to provide guidance, with a focus on pulling and constraining. To explore these guidance methods, a force feedback enabled mouse was constructed, along with a force feedback enabled data visualisation. A user study was conducted, subjecting the participants to different degrees of pulling and constraining guidance, helping them solve navigation tasks. The study found significant quantitative and qualitative changes in behaviour and experience across conditions. We conclude that these two modes of feedback can be used for directing and prescribing guidance situations, provided they are used with restraint.
Interaction Techniques for Exploratory Data Visualization on Mobile Devices
(The Eurographics Association, 2024) Snyder, Luke S.; Rossi, Ryan A.; Koh, Eunyee; Heer, Jeffrey; Hoffswell, Jane; Tominski, Christian; Waldner, Manuela; Wang, Bei
The ubiquity and on-the-go availability of mobile devices makes them central to many tasks such as interpersonal communication and media consumption. However, despite the potential of mobile devices for on-demand exploratory data visualization, existing mobile interactions are difficult, often using highly custom interactions, complex gestures, or multi-modal input. We synthesize limitations from the literature and outline four motivating principles for improved mobile interaction: leverage ubiquitous modalities, prioritize discoverability, enable rapid in-context data exploration, and promote graceful recovery. We then contribute thirteen interaction candidates and conduct a formative study with twelve participants who experienced our interactions in a testbed prototype. Based on these interviews, we discuss design considerations and tradeoffs from four main themes: precise and rapid inspection, focused navigation, single-touch and fixed orientation interaction, and judicious use of motion.
Mapping the Avantgarde: Visualizing Modern Artists' Exhibition Activity
(The Eurographics Association, 2024) Tuscher, Michaela; Filipov, Velitchko; Kamencek, Teresa; Rosenberg, Raphael; Miksch, Silvia; Tominski, Christian; Waldner, Manuela; Wang, Bei
In this paper, we address a crucial challenge for art historians by proposing a visual analytics approach consisting of multiple views designed to facilitate exploration and comparative analysis of artists and their exhibitions. Existing tools to support art-historical research are scarce and lack analytical means to navigate and analyze artists' exhibition activities. Our approach addresses this gap by supporting the identification of geospatial and temporal patterns and offering insights into the multifaceted exhibition behavior of artists in the early 20th century. To demonstrate the efficacy and validate our approach, we present a case study conducted by an art historian in the form of an expert interview. The discussion presents details about insights that were obtained and valuable feedback about the utility of the visual encodings and interactions. By integrating geospatial and temporal facets along with features to perform comparative analysis our approach emerges as a valuable asset for art historians providing a comprehensive look into artists' exhibition histories.
Matrix Snap&Go: Visualization of Paths on Matrices
(The Eurographics Association, 2024) Huang, Zeyang; Archambault, Daniel; Borgo, Rita; Kerren, Andreas; Tominski, Christian; Waldner, Manuela; Wang, Bei
Matrix representations can be effective for visualizing networks. However, it is very difficult to follow or explore specific paths in a matrix representation. In this paper, we introduce an interactive method for exploring paths on a matrix, called Matrix Snap&Go. Our visualization approach relies heavily on interactive exploration, bringing in the local neighborhood of selected nodes and tracing the path progression through the matrix. We demonstrate the utility of our approach by performing and analyzing test runs with synthetic input graphs of various node/edge densities as well as by discussing a use case based on the exploration of citation networks.
Mixing Modes: Active and Passive Integration of Speech, Text, and Visualization for Communicating Data Uncertainty
(The Eurographics Association, 2024) Stokes, Chase; Sanker, Chelsea; Cogley, Bridget; Setlur, Vidya; Tominski, Christian; Waldner, Manuela; Wang, Bei
Interpreting uncertain data can be difficult, particularly if the data presentation is complex. We investigate the efficacy of different modalities for representing data and how to combine the strengths of each modality to facilitate the communication of data uncertainty. We implemented two multimodal prototypes to explore the design space of integrating speech, text, and visualization elements. A preliminary evaluation with 20 participants from academic and industry communities demonstrates that there exists no one-size-fits-all approach for uncertainty communication strategies; rather, the effectiveness of conveying uncertain data is intertwined with user preferences and situational context, necessitating a more refined, multimodal strategy for future interface design. Materials for this paper can be found on OSF.
Model-invariant Weight Distribution Descriptors for Visual Exploration of Neural Networks en Masse
(The Eurographics Association, 2024) Eilertsen, Gabriel; Jönsson, Daniel; Unger, Jonas; Ynnerman, Anders; Tominski, Christian; Waldner, Manuela; Wang, Bei
We present a neural network representation which can be used for visually analyzing the similarities and differences in a large corpus of trained neural networks. The focus is on architecture-invariant comparisons based on network weights, estimating similarities of the statistical footprints encoded by the training setups and stochastic optimization procedures. To make this possible, we propose a novel visual descriptor of neural network weights. The visual descriptor considers local weight statistics in a model-agnostic manner by encoding the distribution of weights over different model depths. We show how such a representation can extract descriptive information, is robust to different parameterizations of a model, and is applicable to different architecture specifications. The descriptor is used to create a model atlas by projecting a model library to a 2D representation, where clusters can be found based on similar weight properties. A cluster analysis strategy makes it possible to understand the weight properties of clusters and how these connect to the different datasets and hyper-parameters used to train the models.
Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes
(The Eurographics Association, 2024) Tseng, Chin; Wang, Arran Zeyu; Quadri, Ghulam Jilani; Szafir, Danielle Albers; Tominski, Christian; Waldner, Manuela; Wang, Bei
Existing guidelines for categorical color selection are heuristic, often grounded in intuition rather than empirical studies of readers' abilities. While design conventions recommend palettes maximize hue differences, more recent exploratory findings indicate other factors, such as lightness, may play a role in effective categorical palette design. We conducted a crowdsourced experiment on mean value judgments in multi-class scatterplots using five color palette families-single-hue sequential, multihue sequential, perceptually-uniform multi-hue sequential, diverging, and multi-hue categorical-that differ in how they manipulate hue and lightness. Participants estimated relative mean positions in scatterplots containing 2 to 10 categories using 20 colormaps. Our results confirm heuristic guidance that hue-based categorical palettes are most effective. However, they also provide additional evidence that scalable categorical encoding relies on more than hue variance.
Revisiting PAVED: Studying Tool Adoption After Four Years
(The Eurographics Association, 2024) Cibulski, Lena; May, Thorsten; Tominski, Christian; Waldner, Manuela; Wang, Bei
Design studies create visualizations that provide lasting solutions to real-world problems. Yet, they rarely validate this goal. Validation of domain usefulness typically stops shortly after the end of a project. Following up on the long-term acceptance, however, can provide important indications of how well a tool addresses the true needs of target users. For an existing decision support tool, we close this gap by revisiting its adoption in the target domain after four years. Our survey reveals a small number of power-users and helps carve out factors that influence whether and how a tool is adopted in the intended work environment.
Robust Cut for Hierarchical Clustering and Merge Trees
(The Eurographics Association, 2024) Banesh, Divya; Ahrens, James; Bujack, Roxana; Tominski, Christian; Waldner, Manuela; Wang, Bei
Hierarchical clustering arrange multi-dimensional data into a tree-like structure, organizing the data by increasing levels of similarity. A cut of the tree divides data into clusters, where cluster members share a likeness. Most common cutting techniques identify a single line, either by a metric or with user input, cutting horizontally through the tree, separating root from leaves. We present a new approach that algorithmically identifies cuts at multiple levels of the tree based on a metric we call robustness. We identify levels to maximize overall robustness by maximizing the height of the shortest branch of the hierarchical tree we must cut through. This technique minimizes the variation within clusters while maximizing the distance between clusters. We apply the same approach to merge trees from computational topology to find the most robust number of connected components. We apply the multi-level robust cut to two datasets to highlight the advantages compared to a traditional, single-level cut.
Show Me Similar Nodes: The Similarity Lens for Multivariate Graphs
(The Eurographics Association, 2024) Tominski, Christian; Berger, Philip; Tominski, Christian; Waldner, Manuela; Wang, Bei
Node-link diagrams with topology-driven layouts are effective tools for visually exploring the structure of graphs. When exploring multivariate graphs, a frequent analytical question is to ask which graph nodes are similar in terms of their multivariate attribute values. Answering this question would usually involve switching to an attribute-driven layout or a different visual representation altogether. However, such context switches may ensue additional cognitive costs and hinder the fluent exploration of the graph. In this paper, we present an interactive lens technique, called the similarity lens. It avoids global view changes by dynamically injecting a local attribute-driven layout into an otherwise topology-driven layout. Given a focus node of interest in the center of the lens, dissimilar nodes are pushed out of the lens and similar nodes are pulled inward, with the most similar nodes closest to the focus node. This dynamic layout adaptation facilitates comparison tasks on a local level without disturbing the user's overall mental map of the graph topology too much. We demonstrate the utility of our approach by exploring a real-world multivariate graph of soccer players.
Visual Analysis of Wind Turbines in Denmark
(The Eurographics Association, 2024) Lamp, Frederik Jørgensen; Møller, Jakob Blaabjerg; Sørensen, Esben Bay; Walsh, Gareth; Kusnick, Jakob; Jänicke, Stefan; Tominski, Christian; Waldner, Manuela; Wang, Bei
To adequately prepare for Denmark's future green transition of the energy market, analysts require sophisticated tools to explore the historical development and current state of the power infrastructure, in particular the wind power network, which has become Denmark's most important energy source. Such analyses require identifying and assessing the performance of wind turbines in terms of size, age, location, and manufacturer for replacement, repair, and extension purposes. Visualization tools performing such trend analyses of wind turbines according to these parameters are scarce. Addressing this shortcoming, we present the Danish Wind Power Analytics Tool (DWPAT). It uses data from the Danish Energy Agency to offer interactive visual representations of the geospatial distribution of wind turbines in Denmark based on various criteria such as manufacturer, municipality, and installation type. DWPAT provides comprehensive insights into wind energy production and enables direct comparisons between specific wind turbines and numerous other analytical characteristics to support the decision-making process of stakeholders in industry, federal and state governments, and research.
Visual Analytics for Planning Left Atrial Appendage Occlusion: A Case Study on In-silico Hemodynamic Assessment
(The Eurographics Association, 2024) Nuhic, Jasna; Albors, Carlos; Mill, Jordi; Olivares, Andy L.; Camara, Oscar; Meuschke, Monique; Tominski, Christian; Waldner, Manuela; Wang, Bei
Clinicians are encouraged to integrate both structural and functional information when making medical decisions, but often lack the necessary visualization and analysis tools. For patients with atrial fibrillation undergoing left atrial appendage occluder (LAAO) implantation, interventional cardiologists must consider the variable anatomy of the left atria and blood flow patterns to select the optimal LAAO device settings. While commercial tools exist to assist with LAAO implantation, they only offer morphological information related to the left atria (LA). Conversely, advanced visual analytics tools have been developed to understand blood flow patterns in cerebral aneurysms. This preliminary study aims to investigate the potential of adapting these visual analytics tools to LAAO applica for the analysis of hemodynamics in the LA obtained from fluid simulations. The resulting platform, LAAOVis, enables the comparison of different LAAO device configurations to identify the most optimal ones for a specific patient. Domain experts have assessed the potential of LAAOVis and identified additional features that would enhance its suitability for planning LAAO device interventions.

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