44-Issue 3

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https://diglib.eg.org/handle/10.2312/3607144

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VIZTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Liangwei; Wang, Zhan; Xiao, Shishi; Liu, Le; Tsung, Fugee; Zeng, Wei; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when interpreting distributional visualizations that depict statistical uncertainty. Advancements in LLM-based conversational interfaces show promise in promoting visualization comprehension. However, they fail to provide contextual explanations at fine-grained granularity, and chart readers are still required to mentally bridge visual information and textual explanations during conversations. Our formative study highlights the expectations for both lexical and visual feedback, as well as the importance of explicitly linking these two modalities throughout the conversation. The findings motivate the design of VIZTA, a visualization teaching assistant that leverages the fusion of visual and lexical feedback to help readers better comprehend visualization. VIZTA features a semantic-aware conversational agent capable of explaining contextual information within visualizations and employs a visual-lexical fusion design to facilitate chart-centered conversation. A between-subject study with 24 participants demonstrates the effectiveness of VIZTA in supporting the understanding and reasoning tasks of distributional visualization across multiple scenarios.
NODKANT: Exploring Constructive Network Physicalization
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Pahr, Daniel; Bartolomeo, Sara Di; Ehlers, Henry; Filipov, Velitchko Andreev; Stoiber, Christina; Aigner, Wolfgang; Wu, Hsiang-Yun; Raidou, Renata Georgia; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit-NODKANT-for constructing modular node-link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed-methods between-subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low-level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step-by-step instructions to create a predefined layout, and the third group received a pre-constructed representation. While free construction proved on average more time-consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step-by-step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10-14 days compared to those who were given a pre-constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/.
Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Patel, Tark; Athawale, Tushar M.; Ouermi, Timbwaoga A. J.; Johnson, Chris R.; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state-of-the-art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state-of-the-art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human-brain HARDI datasets.
EuroVis 2025 CGF 44-3: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Player-Centric Shot Maps in Table Tennis
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Erades, Aymeric; Vuillemot, Romain; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Shot maps are popular in many sports as they typically plot events and player positions in the way they are collected, using a pitch or a table as an absolute coordinate system. We introduce a variation of a table tennis shot map that shifts the point of view from the table to the player. This results in a new reference system to plot incoming balls relative to the player's position rather than on the table. This approach aligns with how table tennis tactical analysis is conducted, focusing on identifying empty spaces and weak spots around the players. We describe the motivation behind this work, built through close collaboration with two table tennis experts, and demonstrate how this approach aligns with the way they analyze games to reveal key tactical aspects. We also present the design rationale and the computer vision pipeline used to accurately collect data from broadcast videos. Our findings show that the technique enables capturing insights that were not visible with the absolute coordinate system, particularly in understanding regions that are reachable and those close to the pivot area of the player.
SurpriseExplora: Tuning and Contextualizing Model-derived Maps with Interactive Visualizations
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Ndlovu, Akim; Shrestha, Hilson; Peck, Evan; Harrison, Lane; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
People craft choropleth maps to monitor, analyze, and understand spatially distributed data. Recent visualization work has addressed several known biases in choropleth maps by developing new model- and metrics- based approaches (e.g. Bayesian surprise). However, effective use of these techniques requires extensive parameter setting and tuning, making them difficult or impossible for users without substantial technical skills. In this paper we describe SurpriseExplora, which addresses this gap through direct manipulation techniques for re-targeting a Bayesian surprise model's scope and parameters. We present three use cases to illustrate the capabilities of SurpriseExplora, showing for example how models calculated at a national level can obscure key findings that can be revealed through interaction sequences common to map visualizations (e.g. zooming), and how augmenting funnel-plot visualizations with interactions that adjust underlying models can account for outliers or skews in spatial datasets. We evaluate SurpriseExplora through an expert review with visualization researchers and practitioners. We conclude by discussing how SurpriseExplora uncovers new opportunities for sense-making within the broader ecosystem of map visualizations, as well as potential empirical studies with non-expert populations. Code and demo video available at https://osf.io/7m89w/
SUPQA: LLM-based Geo-Visualization for Subjective Urban Performance Question-Answering
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Huang, Haiwen; Chen, Juntong; Wang, Changbo; Li, Chenhui; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
As urbanization accelerates, urban performance has become a growing concern, impacting every aspect of residents' lives. However, urban performance exploration is a tedious and highly subjective process for users. Users need to manually collect and integrate various information, or spend a large amount of time and effort due to the steep learning curves of existing specialized tools. To address these challenges, we introduce SUPQA, a novel approach for urban performance exploration using natural language as input and interactive geographic visualizations as output. Our approach leverages Large Language Models (LLMs) to effectively interpret user intents and quantify various urban performance measures. We integrate progressive navigation and multi-geographic scale analysis in our visualization system, explaining the reasoning process and streamlining users' decision-making workflow. Two usage scenarios and evaluations demonstrate the effectiveness of SUPQA in helping residents and planners acquire desired information more efficiently and enhancing the quality of decision-making.
Visually Assessing 1-D Orderings of Contiguous Spatial Polygons
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Rauscher, Julius; Dennig, Frederik L.; Schlegel, Udo; Keim, Daniel A.; Fuchs, Johannes; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
One-dimensional orderings of spatial entities have been researched in many contexts, e.g. spatial indexing structures or visualizations for spatiotemporal trend analysis. While plenty of studies have been conducted to evaluate orderings of point-based data, polygonal shapes, despite their different topological properties, have received less attention. Existing measures to quantify errors in projections or orderings suffer from generic neighborhood definitions and over-simplification of distances when applied to polygonal data. In this work, we address these shortcomings by introducing measures that adapt to a varying neighborhood size depending on the number of contiguous neighbors and thus, address the limitations of existing measures for polygonal shapes. To guide experts in determining a suitable ordering, we propose a user-steerable visual analytics prototype capable of locally and globally inspecting ordering errors, investigating the impact of geographic obstacles, and comparing ordering strategies using our measures.We demonstrate the effectiveness of our approach through a use case and conducted an expert study with 8 data scientists as a qualitative evaluation of our approach. Our results show that users are capable of identifying ordering errors, comparing ordering strategies on a global and local scale, as well as assessing the impact of semantically relevant geographic obstacles.
Random Access Segmentation Volume Compression for Interactive Volume Rendering
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Piochowiak, Max; Kurpicz, Florian; Dachsbacher, Carsten; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Segmentation volumes are voxel data sets often used in machine learning, connectomics, and natural sciences. Their large sizes make compression indispensable for storage and processing, including GPU video memory constrained real-time visualization. Fast Compressed Segmentation Volumes (CSGV) [PD24] provide strong brick-wise compression and random access at the brick level. Voxels within a brick, however, have to be decoded serially and thus rendering requires caching of visible full bricks, consuming extra memory. Without caching, accessing voxels can have a worst-case decoding overhead of up to a full brick (typically over 32.000 voxels). We present CSGV-R which provide true multi-resolution random access on a per-voxel level. We leverage Huffman-shaped Wavelet Trees for random accesses to variable bit-length encoding and their rank operation to query label palette offsets in bricks. Our real-time segmentation volume visualization removes decoding artifacts from CSGV and renders CSGV-R volumes without caching bricks at faster render times. CSGV-R has slightly lower compression rates than CSGV, but outperforms Neuroglancer, the state-of-the-art compression technique with true random access, with 2× to 4× smaller data sets at rates between 0.648% and 4.411% of the original volume sizes.
DataWeaver: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Fu, Yu; Bromley, Dennis; Setlur, Vidya; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Data-driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authoring framework and system, DATAWEAVER, that supports both visualization-to-text and text-to-visualization composition. DATAWEAVER enables users to create data narratives anchored to data facts derived from ''call-out'' interactions, i.e., user-initiated highlights of visualization elements that prompt relevant narrative content. In addition to this ''vis-to-text'' composition, DATAWEAVER also supports a ''text-initiated'' approach, generating relevant interactive visualizations from existing narratives. Key findings from an evaluation with 13 participants highlighted the utility and usability of DATAWEAVER and the effectiveness of its integrated authoring framework. The evaluation also revealed opportunities to enhance the framework by refining filtering mechanisms and visualization recommendations and better support authoring creativity by introducing advanced customization options.
Embedded and Situated Visualisation in Mixed Reality to Support Interval Running
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Li, Ang; Perin, Charles; Knibbe, Jarrod; Demartini, Gianluca; Viller, Stephen; Cordeil, Maxime; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We investigate the use of mixed reality visualisations to help pace tracking for interval running. We introduce three immersive visual designs to support pace tracking. Our designs leverage two properties afforded by mixed reality environments to display information: the space in front of the user and the physical environment to embed pace visualisation. In this paper, we report on the first design exploration and controlled study of mixed reality technology to support pacing tracking during interval running on an outdoor running track. Our results show that mixed reality and immersive visualisation designs for interval training offer a viable option to help runners (a) maintain regular pace, (b) maintain running flow, and (c) reduce mental task load.
Accessible Text Descriptions for UpSet Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2025) McNutt, Andrew; McCracken, Maggie K.; Eliza, Ishrat Jahan; Hajas, Daniel; Wagoner, Jake; Lanza, Nate; Wilburn, Jack; Creem-Regehr, Sarah; Lex, Alexander; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Data visualizations are typically not accessible to blind and low-vision (BLV) users. Automatically generating text descriptions offers an enticing mechanism for democratizing access to the information held in complex scientific charts, yet appropriate procedures for generating those texts remain elusive. Pursuing this issue, we study a single complex chart form: UpSet plots. UpSet Plots are a common way to analyze set data, an area largely unexplored by prior accessibility literature. By analyzing the patterns present in real-world examples, we develop a system for automatically captioning any UpSet plot. We evaluated the utility of our captions via semi-structured interviews with (N=11) BLV users and found that BLV users find them informative. In extensions, we find that sighted users can use our texts similarly to UpSet plots and that they are better than naive LLM usage.
FairSpace: An Interactive Visualization System for Constructing Fair Consensus from Many Rankings
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Shrestha, Hilson; Cachel, Kathleen; ALKHATHLAN, MALLAK; Rundensteiner, Elke; Harrison, Lane; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Decisions involving algorithmic rankings affect our lives in many ways, from product recommendations, receiving scholarships, to securing jobs. While tools have been developed for interactively constructing fair consensus rankings from a handful of rankings, addressing the more complex real-world scenario- where diverse opinions are represented by a larger collection of rankings- remains a challenge. In this paper, we address these challenges by reformulating the exploration of rankings as a dimension reduction problem in a system called FairSpace. FairSpace provides new views, including Fair Divergence View and Cluster Views, by juxtaposing fairness metrics of different local and alternative global consensus rankings to aid ranking analysis tasks.We illustrate the effectiveness of FairSpace through a series of use cases, demonstrating via interactive workflows that users are empowered to create local consensuses by grouping rankings similar in their fairness or utility properties, followed by hierarchically aggregating local consensuses into a global consensus through direct manipulation. We discuss how FairSpace opens the possibility for advances in dimension reduction visualization to benefit the research area of supporting fair decision-making in ranking based decision-making contexts. Code, datasets and demo video available at: osf.io/d7cwk
Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Eschner, Johannes; Labadie-Tamayo, Roberto; Zeppelzauer, Matthias; Waldner, Manuela; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Bias in generative Text-to-Image (T2I) models is a known issue, yet systematically analyzing such models' outputs to uncover it remains challenging. We introduce the Visual Bias Explorer (ViBEx) to interactively explore the output space of T2I models to support the discovery of visual bias. ViBEx introduces a novel flexible prompting tree interface in combination with zeroshot bias probing using CLIP for quick and approximate bias exploration. It additionally supports in-depth confirmatory bias analysis through visual inspection of forward, intersectional, and inverse bias queries. ViBEx is model-agnostic and publicly available. In four case study interviews, experts in AI and ethics were able to discover visual biases that have so far not been described in literature.
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Paulovich, Fernando V.; Arleo, Alessio; Elzen, Stef van den; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within network data, with specialized research investigating how such features could be presented to users to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math-grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well-defined stages, discuss how to match some of the DR state-of-the-art techniques to this framework, and present ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.
IntelliCircos: A Data-driven and AI-powered Authoring Tool for Circos Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Gu, Mingyang; Zhu, Jiamin; Wang, Qipeng; Wang, Fengjie; Wen, Xiaolin; Wang, Yong; Zhu, Min; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Genomics data is essential in biological and medical domains, and bioinformatics analysts often manually create circos plots to analyze the data and extract valuable insights. However, creating circos plots is complex, as it requires careful design for multiple track attributes and positional relationships between them. Typically, analysts often seek inspiration from existing circos plots, and they have to iteratively adjust and refine the plot to achieve a satisfactory final design, making the process both tedious and time-intensive. To address these challenges, we propose IntelliCircos, an AI-powered interactive authoring tool that streamlines the process from initial visual design to the final implementation of circos plots. Specifically, we build a new dataset containing 4396 circos plots with corresponding annotations and configurations, which are extracted and labeled from published papers. With the dataset, we further identify track combination patterns, and utilize Large Language Model (LLM) to provide domain-specific design recommendations and configuration references to navigate the design of circos plots. We conduct a user study with 8 bioinformatics analysts to evaluate IntelliCircos, and the results demonstrate its usability and effectiveness in authoring circos plots.
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Sevastjanova, Rita; Gerling, Robin; Spinner, Thilo; El-Assady, Mennatallah; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Large language models (LLMs) represent words through contextual word embeddings encoding different language properties like semantics and syntax. Understanding these properties is crucial, especially for researchers investigating language model capabilities, employing embeddings for tasks related to text similarity, or evaluating the reasons behind token importance as measured through attribution methods. Applications for embedding exploration frequently involve dimensionality reduction techniques, which reduce high-dimensional vectors to two dimensions used as coordinates in a scatterplot. This data transformation step introduces uncertainty that can be propagated to the visual representation and influence users' interpretation of the data. To communicate such uncertainties, we present LayerFlow - a visual analytics workspace that displays embeddings in an interlinked projection design and communicates the transformation, representation, and interpretation uncertainty. In particular, to hint at potential data distortions and uncertainties, the workspace includes several visual components, such as convex hulls showing 2D and HD clusters, data point pairwise distances, cluster summaries, and projection quality metrics. We show the usability of the presented workspace through replication and expert case studies that highlight the need to communicate uncertainty through multiple visual components and different data perspectives.
In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Zhe; Moreland, Kenneth; Larsen, Matthew; Kress, James; Childs, Hank; Li, Guan; Shan, Guihua; Pugmire, David; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Particle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization-Over-Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization-over-data algorithm. With tightly coupled workload estimation and load-balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round-robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.
HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Gadirov, Hamid; Wu, Qi; Bauer, David; Ma, Kwan-Liu; Roerdink, Jos B.T.M.; Frey, Steffen; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data. This work addresses the critical need to explicitly incorporate ensemble parameters into the learning process, as traditional methods often neglect these, limiting their ability to adapt to diverse simulation settings and provide meaningful insights into the data dynamics. HyperFLINT introduces a hypernetwork to account for simulation parameters, enabling it to generate accurate interpolations and flow fields for each timestep by dynamically adapting to varying conditions, thereby outperforming existing parameter-agnostic approaches. The architecture features modular neural blocks with convolutional and deconvolutional layers, supported by a hypernetwork that generates weights for the main network, allowing the model to better capture intricate simulation dynamics. A series of experiments demonstrates HyperFLINT's significantly improved performance in flow field estimation and temporal interpolation, as well as its potential in enabling parameter space exploration, offering valuable insights into complex scientific ensembles.
PrismBreak: Exploration of Multi-Dimensional Mixture Models
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Zahoransky, Brian; Günther, Tobias; Lawonn, Kai; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In data science, visual data exploration becomes increasingly more challenging due to the continued rapid increase of data dimensionality and data sizes. To manage complexity, two orthogonal approaches are commonly used in practice: First, data is frequently clustered in high-dimensional space by fitting mixture models composed of normal distributions or Student t-distributions. Second, dimensionality reduction is employed to embed high-dimensional point clouds in a two- or threedimensional space. Those algorithms determine the spatial arrangement in low-dimensional space without further user interaction. This leaves little room for a guided exploration and data analysis. In this paper, we propose a novel visualization system for the effective exploration and construction of potential subspaces onto which mixture models can be projected. The subspaces are spanned linearly via basis vectors, for which a vast number of basis vector combinations is theoretically imaginable. Our system guides the user step-by-step through the selection process by letting users choose one basis vector at a time. To guide the process, multiple choices are pre-visualized at once on a multi-faceted prism. In addition to the qualitative visualization of the distributions, multiple quantitative metrics are calculated by which subspaces can be compared and reordered, including variance, sparsity, and visibility. Further, a bookmarking tool lets users record and compare different basis vector combinations. The usability of the system is evaluated by data scientists and is tested on several high-dimensional data sets.

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