DevLan/vispubs huggingface dataset:
Optimizing Grid Layouts for Level-of-Detail Exploration of Large Data Collections
Authors: Steffen Frey
Published at EuroVis, 2022

This paper introduces an optimization approach for generating grid layouts from large data collections such that they are amenable to level‐of‐detail presentation and exploration. Classic (flat) grid layouts visually do not scale to large collections, yielding overwhelming numbers of tiny member representations. The proposed local search‐based progressive optimization scheme generates hierarchical grids: leaves correspond to one grid cell and represent one member, while inner nodes cover a quadratic range of cells and convey an aggregate of contained members. The scheme is solely based on pairwise distances and jointly optimizes for homogeneity within inner nodes and across grid neighbors. The generated grids allow to present and flexibly explore the whole data collection with arbitrary local granularity. Diverse use cases featuring large data collections exemplify the application: stock market predictions from a Black‐Scholes model, channel structures in soil from Markov chain Monte Carlo, and image collections with feature vectors from neural network classification models. The paper presents feedback by a domain scientist, compares against previous approaches, and demonstrates visual and computational scalability to a million members, surpassing classic grid layout techniques by orders of magnitude.
Reusing Interactive Analysis Workflows
Authors: Kiran Gadhave, Zach Cutler, Alexander Lex
Published at EuroVis, 2022

Interactive visual analysis has many advantages, but an important disadvantage is that analysis processes and workflows cannot be easily stored and reused. This is in contrast to code‐based analysis workflows, which can simply be run on updated datasets, and adapted when necessary. In this paper, we introduce methods to capture workflows in interactive visualization systems for different interactions such as selections, filters, categorizing/grouping, labeling, and aggregation. These workflows can then be applied to updated datasets, making interactive visualization sessions reusable. We demonstrate this specification using an interactive visualization system that tracks interaction provenance, and allows generating workflows from the recorded actions. The system can then be used to compare different versions of datasets and apply workflows to them. Finally, we introduce a Python library that can load workflows and apply it to updated datasets directly in a computational notebook, providing a seamless bridge between computational workflows and interactive visualization tools.
Rich Screen Reader Experiences for Accessible Data Visualization
Authors: Jonathan Zong, Crystal Lee, Alan Lundgard, JiWoong Jang, Daniel Hajas, Arvind Satyanarayan
Published at EuroVis, 2022

Current web accessibility guidelines ask visualization designers to support screen readers via basic non‐visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity—from fine‐grained datum‐by‐datum reading to skimming and surfacing high‐level trends. In response to the lack of comparable non‐visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co‐design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse; navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description, or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen‐reader‐accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed‐methods study with 13 blind and visually impaired readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process. An accessible HTML version of this paper is available at: http://vis.csail.mit.edu/pubs/rich-screen-reader-vis-experiences.
Seeing Through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments
Authors: Ruobin Wang, Crescentia Jung, Yea-Seul Kim
Published at EuroVis, 2022

Sonification can be an effective medium for people with visual impairments to understand data in visualizations. However, there are no universal design principles that apply to various charts that encode different data types. Towards generalizable principles, we conducted an exploratory experiment to assess how different auditory channels (e.g., pitch, volume) impact the data and visualization perception among people with visual impairments. In our experiment, participants evaluated the intuitiveness and accuracy of the mapping of auditory channels on different data and chart types. We found that participants rated pitch to be the most intuitive, while the number of tappings and the length of sounds yielded the most accurate perception in decoding data. We study how audio channels can intuitively represent different charts and demonstrate that data‐level perception might not directly transfer to chart‐level perception as participants reflect on visual aspects of the charts while listening to audio. We conclude by how future experiments can be designed to establish a robust ranking for creating audio charts.
SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio-temporal Ensembles
Authors: Karim Huesmann, Lars Linsen
Published at EuroVis, 2022

Latent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio‐temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio‐temporal ensemble data without re‐training. The latent‐space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet, we are able to perform similarity analyses on large‐scale spatio‐temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in‐depth basic understanding of the underlying temporal evolution of a given ensemble.
Six methods for transforming layered hypergraphs to apply layered graph layout algorithms
Authors: Sara Di Bartolomeo, Alexis Pister, Paolo Buono, Catherine Plaisant, Cody Dunne, Jean-Daniel Fekete
Published at EuroVis, 2022

Hypergraphs are a generalization of graphs in which edges (hyperedges) can connect more than two vertices—as opposed to ordinary graphs where edges involve only two vertices. Hypergraphs are a fairly common data structure but there is little consensus on how to visualize them. To optimize a hypergraph drawing for readability, we need a layout algorithm. Common graph layout algorithms only consider ordinary graphs and do not take hyperedges into account. We focus on layered hypergraphs, a particular class of hypergraphs that, like layered graphs, assigns every vertex to a layer, and the vertices in a layer are drawn aligned on a linear axis with the axes arranged in parallel. In this paper, we propose a general method to apply layered graph layout algorithms to layered hypergraphs. We introduce six different transformations for layered hypergraphs. The choice of transformation affects the subsequent graph layout algorithm in terms of computational performance and readability of the results. Thus, we perform a comparative evaluation of these transformations in terms of number of crossings, edge length, and impact on performance. We also provide two case studies showing how our transformations can be applied to real‐life use cases. A copy of this paper with all appendices and supplemental material is available at osf.io/grvwu.
Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data
Authors: Mengxi Wu, Yi-Jen Chiang, Christopher Musco
Published at EuroVis, 2022

Key time steps selection, i.e., selecting a subset of most representative time steps, is essential for effective and efficient scientific visualization of large time‐varying volume data. In particular, as computer simulations continue to grow in size and complexity, they often generate output that exceeds both the available storage capacity and bandwidth for transferring results to storage, making it indispensable to save only a subset of time steps. At the same time, this subset must be chosen so that it is highly representative, to facilitate post‐processing and reconstruction with high fidelity. The key time steps selection problem is especially challenging in the in situ setting, where we can only process data in one pass in an online streaming fashion, using a small amount of main memory and fast computation. In this paper, we formulate the problem as that of optimal piece‐wise linear interpolation. We first apply a method from numerical linear algebra to compute linear interpolation solutions and their errors in an online streaming fashion. Using that method as a building block, we can obtain a global optimal solution for the piece‐wise linear interpolation problem via a standard dynamic programming (DP) algorithm. However, this approach needs to process the time steps in multiple passes and is too slow for the in situ setting. To address this issue, we introduce a novel approximation algorithm, which processes time steps in one pass in an online streaming fashion, with very efficient computing time and main memory space both in theory and in practice. The algorithm is suitable for the in situ setting. Moreover, we prove that our algorithm, which is based on a greedy update rule, has strong theoretical guarantees on the approximation quality and the number of time steps stored. To the best of our knowledge, this is the first algorithm suitable for in situ key time steps selection with such theoretical guarantees, and is the main contribution of this paper. Experiments demonstrate the efficacy of our new techniques.
SurfNet: Learning Surface Representations via Graph Convolutional Network
Authors: Jun Han 0010, Chaoli Wang 0001
Published at EuroVis, 2022

For scientific visualization applications, understanding the structure of a single surface (e.g., stream surface, isosurface) and selecting representative surfaces play a crucial role. In response, we propose SurfNet, a graph‐based deep learning approach for representing a surface locally at the node level and globally at the surface level. By treating surfaces as graphs, we leverage a graph convolutional network to learn node embedding on a surface. To make the learned embedding effective, we consider various pieces of information (e.g., position, normal, velocity) for network input and investigate multiple losses. Furthermore, we apply dimensionality reduction to transform the learned embeddings into 2D space for understanding and exploration. To demonstrate the effectiveness of SurfNet, we evaluate the embeddings in node clustering (node‐level) and surface selection (surface‐level) tasks. We compare SurfNet against state‐of‐the‐art node embedding approaches and surface selection methods. We also demonstrate the superiority of SurfNet by comparing it against a spectral‐based mesh segmentation approach. The results show that SurfNet can learn better representations at the node and surface levels with less training time and fewer training samples while generating comparable or better clustering and selection results.
Trends & Opportunities in Visualization for Physiology: A Multiscale Overview
Authors: Laura A. Garrison, Ivan Kolesár, Ivan Viola, Helwig Hauser, Stefan Bruckner
Published at EuroVis, 2022

Combining elements of biology, chemistry, physics, and medicine, the science of human physiology is complex and multifaceted. In this report, we offer a broad and multiscale perspective on key developments and challenges in visualization for physiology. Our literature search process combined standard methods with a state‐of‐the‐art visual analysis search tool to identify surveys and representative individual approaches for physiology. Our resulting taxonomy sorts literature on two levels. The first level categorizes literature according to organizational complexity and ranges from molecule to organ. A second level identifies any of three high‐level visualization tasks within a given work: exploration, analysis, and communication. The findings of this report may be used by visualization researchers to understand the overarching trends, challenges, and opportunities in visualization for physiology and to provide a foundation for discussion and future research directions in this area.
Urban Rhapsody: Large-scale exploration of urban soundscapes
Authors: João Rulff, Fabio Miranda 0001, Maryam Hosseini, Marcos Lage, Mark Cartwright, Graham Dove, Juan Pablo Bello, Cláudio T. Silva
Published at EuroVis, 2022

Noise is one of the primary quality‐of‐life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low‐cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes. In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state‐of‐the‐art audio representation, machine learning and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high‐precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five‐year deployment of a one‐of‐a‐kind sensor network in New York City.
Vessel Maps: A Survey of Map-Like Visualizations of the Cardiovascular System
Authors: Pepe Eulzer, Monique Meuschke, Gabriel Mistelbauer, Kai Lawonn
Published at EuroVis, 2022

Map‐like visualizations of patient‐specific cardiovascular structures have been applied in numerous medical application contexts. The term map‐like alludes to the characteristics these depictions share with cartographic maps: they show the spatial relations of data attributes from a single perspective, they abstract the underlying data to inCrease legibility, and they facilitate tasks centered around overview, navigation, and comparison. A vast landscape of techniques exists to derive such maps from heterogeneous data spaces. Yet, they all target similar purposes within disease diagnostics, treatment, or research and they face coinciding challenges in mapping the spatial component of a treelike structure to a legible layout. In this report, we present a framing to unify these approaches. On the one hand, we provide a classification of the existing literature according to the data spaces such maps can be derived from. On the other hand, we view the approaches in light of the manifold requirements medical practitioners and researchers have in their efforts to combat the ever‐growing burden of cardiovascular disease. Based on these two perspectives, we offer recommendations for the design of map‐like visualizations of the cardiovascular system.
VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism
Authors: Shambhavi Mahajan, Bonnie Chen, Alireza Karduni, Yea-Seul Kim, Emily Wall
Published at EuroVis, 2022

The process of forming, expressing, and updating beliefs from data plays a critical role in data‐driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Recently, belief‐driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief‐driven visualization. In this paper, we synthesize a design space for belief‐driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief‐driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.
Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
Authors: Max Sondag, Cagatay Turkay, Kai Xu 0003, Louise Matthews, Sibylle Mohr, Daniel Archambault
Published at EuroVis, 2022

Epidemiologists use individual‐based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex ‘infection maps’ of time‐varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time‐varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns.
Visual Parameter Selection for Spatial Blind Source Separation
Authors: Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann, Klaus Nordhausen, Peter Filzmoser, Silvia Miksch
Published at EuroVis, 2022

Analysis of spatial multivariate data, i.e., measurements at irregularly‐spaced locations, is a challenging topic in visualization and statistics alike. Such data are inteGral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non‐expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data.
Where did my Lines go? Visualizing Missing Data in Parallel Coordinates
Authors: Alex Bäuerle, Christian van Onzenoodt, Simon der Kinderen, Jimmy Johansson Westberg, Daniel Jönsson, Timo Ropinski
Published at EuroVis, 2022

We evaluate visualization concepts to represent missing values in parallel coordinates. We focus on the trade‐off between the ability to perceive missing values and the concept's impact on common tasks. For this purpose, we identified three missing value representation concepts: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values. For the missing values axis and imputed values concepts, we additionally add downplay and highlight variations. We performed a crowd‐sourced, quantitative user study with 732 participants comparing the concepts and their variations using five real‐world datasets. Based on our findings, we provide suggestions regarding which visual encoding to employ depending on the task at focus.
"I See You!": A Design Framework for Interface Cues about Agent Visual Perception from a Thematic Analysis of Videogames
Authors: Matthew Rueben, Matthew Rodney Horrocks, Jennifer Eleanor Martinez, Michelle V. Cormier, Nicolas J. LaLone, Marlena R. Fraune, Z. Toups Dugas
Published at CHI, 2022

As artificial agents proliferate, there will be more and more situations in which they must communicate their capabilities to humans, including what they can “see.” Artificial agents have existed for decades in the form of computer-controlled agents in videogames. We analyze videogames in order to not only inspire the design of better agents, but to stop agent designers from replicating research that has already been theorized, designed, and tested in-depth. We present a qualitative thematic analysis of sight cues in videogames and develop a framework to support human-agent interaction design. The framework identifies the different locations and stimulus types – both visualizations and sonifications – available to designers and the types of information they can convey as sight cues. Insights from several other cue properties are also presented. We close with suggestions for implementing such cues with existing technologies to improve the safety, privacy, and efficiency of human-agent interactions.