Excel to HTML
THURSDAY 5 MARCH 2026
Data Visualization
Session Chair: Yi-Jen Chiang, NYU
08:30 - 10:30
Grand Peninsula F
08:30VDA-348
Urban data for everybody: Visual comparison & multi-attribute ranking for casual users, Joshua Pola, TU Wien and Halo GmbH (Austria); Kresimir Matkovic, VRVis Research Center (Austria) [view abstract]
Urban data is increasingly available to the public, but making it accessible and useful for non-expert users remains challenging. Interactive visualization offers a powerful means to explore such data, particularly for tasks like comparing districts within one or between two cities, a common scenario for students relocating, tourists planning a trip, or citizens considering moving. Due to the varied interests and priorities of individuals, developing a universally applicable solution is demanding.In this work, we propose an approach that enables visual, weighted comparison of city attributes. We first abstract analysis tasks and derive corresponding design requirements, and then present a coordinated multiple views system that allows users to compare different weighted attributes across districts of two cities. The system integrates linked views that support selection, weighting, ranking, and detailed exploration of urban data. We demonstrate the usefulness of our approach through a comparison of Vienna, Austria, and Berlin, Germany, and validate it with a pilot study, which received very positive feedback. Our results indicate that the proposed approach supports casual users in exploring urban data effectively while allowing flexible, personalized comparisons.
08:50VDA-349
Easy interpretation of image classification results with feature-level visualization, Deepshikha Bhati, Kent State University (US); Ye Zhao, Kent State University (US); Tsung-Heng Wu, Kent State University (US); Md Amiruzzaman, West Chester University; Jing Yang, University of North Carolina (US) [view abstract]
Given a picture classified as a Persian cat by an AI model, users may ask questions such as, What are the contributions of the eyes and ears to the classification result? or Which features contribute the most? While existing post-hoc XAI methods effectively explain model predictions at the pixel or patch level, they are limited in directly quantifying the contributions of human-interpretable semantic features. In this paper, we propose a visual analytics approach for feature-level interpretation of image classification results. Our contributions are twofold. First, we introduce a semantic contribution quantification method that builds upon existing pixel-level attribution techniques (e.g., Layer-wise Relevance Propagation, Grad-CAM). Specifically, we aggregate and normalize pixel-level relevance scores over predefined semantic regions (such as eyes, ears, and body) to compute comparable contribution scores for each semantic feature within an image. Second, we present an interactive visual interface that leverages these quantified semantic feature contributions to support exploration, comparison, and analysis of AI outputs across image collections. Through illustrative scenarios and expert feedback, we demonstrate that our approach provides an intuitive, scalable, and semantically meaningful means to interpret image classification explanations.
09:10VDA-350
Distributed 3D Gaussian splatting for high-resolution isosurface visualization, Mengjiao Han, Argonne National Laboratory (US); Andres Sewell, Utah State University (US); Joseph Insley, Argonne National Laboratory (US); Janet Knowles, Argonne National Laboratory (US); Victor Mateevitsi, Argonne National Laboratory and University of Illinois Chicago (US); Michael Papka, Argonne National Laboratory and University of Illinois Chicago; Steve Petruzza, Utah State University (US); Silvio Rizzi, Argonne National Laboratory (US) [view abstract]
3D Gaussian Splatting (3D-GS) has recently emerged as a powerful technique for real-time, photorealistic rendering by optimizing anisotropic Gaussian primitives from view-dependent images. Unlike neural implicit methods such as NeRF, 3D-GS avoids the need for a neural network forward pass at inference, making it significantly faster while maintaining high visual fidelity. While 3D-GS has been extended to scientific visualization, prior work remains limited to single-GPU settings, restricting scalability for large scientific datasets on high-performance computing (HPC) systems. In this study, we present a distributed 3D-GS pipeline tailored for scientific data on HPC. Our approach partitions data across nodes, trains Gaussian splats in parallel using multi-nodes and multi-GPUs, and merges splats for global rendering. To eliminate artifacts, we add ghost cells at partition boundaries and apply background masks to remove irrelevant pixels. Benchmarks on the Richtmyer Meshkov datasets (about 106.7M Gaussians) show up to 3X speedup across 8 nodes on Polaris while preserving image quality. These results demonstrate that distributed 3D-GS enables scalable visualization of large-scale scientific data and provides a foundation for future in situ applications.
09:30VDA-351
Efficient selection of salient timesteps in scientific simulations, Roxana Bujack, Los Alamos National Laboratory (US); Jesus Pulido, Los Alamos National Laboratory (US); Manish Bhattarai, Los Alamos National Laboratory (US); David Rogers, Los Alamos National Laboratory (US) [view abstract]
Identifying key timesteps in spatio-temporal datasets is essential for shaping the story a simulation tells. The selected timesteps act as anchors for visualization, guiding parameter choices for rendering, animation, and analysis. While many sophisticated methods have been proposed for this task, we show that the field has often leaned toward unnecessary complexity. In this work, we provide a survey of existing timestep selection strategies, illustrating their limited ability to balance quality and efficiency. Building on these insights, we introduce a deliberately simple approach based on greedy local search. Starting from uniformly spaced candidates, we iteratively shift selections to minimize reconstruction error under interpolation. Despite its simplicity, this method consistently yields high-quality subsets, enabling effective parameter tuning and exploratory visualization while achieving significantly lower computational cost than more elaborate techniques. Through quantitative comparisons across datasets and error metrics, we demonstrate that purposeful simplicity can provide a better trade-off between quality and runtime than existing, more complex alternatives.
09:50VDA-352
JPI-first-2025-006: Modeling the Wundt curve with clarity and novelty: A psychophysical study of the hedonic value of flyer designs, Natsuko Minegishi, Konica Minolta, Inc. (Japan); Asaya Shimojyo, Independent Researcher [view abstract]
This study proposes a predictive framework for estimating observer-perceived likability of graphic design stimuli that communicate information and impressions. The model reinterprets Berlyne s Wundt curve by defining arousal potential as the perceptual difference between a remembered image and a presented stimulus. We hypothesize that this difference influences perceived clarity and novelty, and that their interaction determines hedonic value. Our review found no prior study defining arousal potential purely through visual differences and modeling likability as the interaction of clarity and novelty. To test this hypothesis, we quantified the perceptual difference, termed difference from memory, by extracting visual features using EfficientNet-B0 and calculating feature distances. We also conducted a subjective evaluation using flyer designs, with stimuli systematically varied from a prototypical market design. The results supported the hypothesized relationships, demonstrating the potential of the proposed framework to predict affective responses to graphic design based on quantifiable visual features.