DevLan/vispubs huggingface dataset:
Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis
Authors: Jianping Kelvin Li, Shenyu Xu, Yecong (Chris) Ye, Kwan-Liu Ma
Published at EuroVis, 2020

Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.
SEEVis: A Smart Emergency Evacuation Plan Visualization System with Data-Driven Shot Designs
Authors: Quan Li, Yingjie Liu, Li Chen, Xingchao Yang, Yi Peng, Xiaoru Yuan, M. L. L. Wijerathne
Published at EuroVis, 2020

Despite the significance of tracking human mobility dynamics in a large‐scale earthquake evacuation for an effective first response and disaster relief, the general understanding of evacuation behaviors remains limited. Numerous individual movement trajectories, disaster damages of civil engineering, associated heterogeneous data attributes, as well as complex urban environment all obscure disaster evacuation analysis. Although visualization methods have demonstrated promising performance in emergency evacuation analysis, they cannot effectively identify and deliver the major features like speed or density, as well as the resulting evacuation events like congestion or turn‐back. In this study, we propose a shot design approach to generate customized and narrative animations to track different evacuation features with different exploration purposes of users. Particularly, an intuitive scene feature graph that identifies the most dominating evacuation events is first constructed based on user‐specific regions or their tracking purposes on a certain feature. An optimal camera route, i.e., a storyboard is then calculated based on the previous user‐specific regions or features. For different evacuation events along this route, we employ the corresponding shot design to reveal the underlying feature evolution and its correlation with the environment. Several case studies confirm the efficacy of our system. The feedback from experts and users with different backgrounds suggests that our approach indeed helps them better embrace a comprehensive understanding of the earthquake evacuation.
SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics
Authors: Meng Xia, Min Xu, Chuan-En Lin, Ta Ying Cheng, Huamin Qu, Xiaojuan Ma
Published at EuroVis, 2020

Problem‐solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non‐cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self‐regulation patterns (an example of non‐cognitive traits and behaviors) based on the problem‐solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem‐solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students ‘problem‐solving dynamics from both cognitive and non‐cognitive perspectives. The system visualizes the chronological sequence of learners’ problem‐solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem‐solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real‐world dataset which consists of thousands of problem‐solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts’ understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.
Set Streams: Visual Exploration of Dynamic Overlapping Sets
Authors: Shivam Agarwal, Fabian Beck 0001
Published at EuroVis, 2020

In many applications, membership of set elements changes over time. Since each element can be present in multiple sets, the sets also overlap. As a result, it becomes challenging to visualize the temporal change in set membership of elements across several timesteps while showing individual set intersections. We propose Set Streams, a visualization technique that represents changing set structures on a timeline as branching and merging streams. The streams encode the changing membership of elements with set intersections. A query‐based selection mechanism supports a flexible comparison of selected groups of elements across the temporal evolution. The main timeline view is complemented with additional panels to provide details about the elements. While the proposed visualization is an application‐independent visualization technique for dynamic sets, we demonstrate its effectiveness and applicability through three diverse application examples and expert feedback.
Short-Contact Touch-Manipulation of Scatterplot Matrices on Wall Displays
Authors: Patrick Riehmann, Gabriela Molina León, Joshua Reibert, Florian Echtler, Bernd Fröhlich 0001
Published at EuroVis, 2020

This paper presents a short‐contact multitouch vocabulary for interacting with scatterplot matrices (SPLOMs) on wall‐sized displays. Fling‐based gestures overcome central interaction challenges of such large displays by avoiding long swipes on the typically blunt surfaces, frequent physical navigation by walking for accessing screen areas beyond arm's reach in the horizontal direction and uncomfortable postures for accessing screen areas in the vertical direction. Furthermore, we make use of the display's high resolution and large size by supporting the efficient specification of two‐tiered focus + context regions which are consistently propagated across the SPLOM. These techniques are complemented by axis‐centered and lasso‐based selection techniques for specifying subsets of the data. An expert review as well as a user study confirmed the potential and general usability of our seamlessly integrated multitouch interaction techniques for SPLOMs on large vertical displays.
State of the Art in Time-Dependent Flow Topology: Interpreting Physical Meaningfulness Through Mathematical Properties
Authors: Roxana Bujack, Lin Yan, Ingrid Hotz, Christoph Garth, Bei Wang 0001
Published at EuroVis, 2020

We present a state‐of‐the‐art report on time‐dependent flow topology. We survey representative papers in visualization and provide a taxonomy of existing approaches that generalize flow topology from time‐independent to time‐dependent settings. The approaches are classified based upon four categories: tracking of steady topology, reference frame adaption, pathline classification or clustering, and generalization of critical points. Our unique contributions include introducing a set of desirable mathematical properties to interpret physical meaningfulness for time‐dependent flow visualization, inferring mathematical properties associated with selective research papers, and utilizing such properties for classification. The five most important properties identified in the existing literature include coincidence with the steady case, induction of a partition within the domain, Lagrangian invariance, objectivity, and Galilean invariance.
Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude
Authors: Johannes Liem, Charles Perin, Jo Wood
Published at EuroVis, 2020

In the visualization community, it is often assumed that visual data storytelling increases memorability and engagement, making it more effective at communicating information. However, many assumptions about the efficacy of storytelling in visualization lack empirical evaluation. Contributing to an emerging body of work, we study whether selected techniques commonly used in visual data storytelling influence people's attitudes towards immigration. We compare (a) personal visual narratives designed to generate empathy; (b) structured visual narratives of aggregates of people; and (c) an exploratory visualization without narrative acting as a control condition. We conducted two crowdsourced between‐subject studies comparing the three conditions, each with 300 participants. To assess the differences in attitudes between conditions, we adopted established scales from the social sciences used in the European Social Survey (ESS). Although we found some differences between conditions, the effects on people's attitudes are smaller than we expected. Our findings suggest that we need to be more careful when it comes to our expectations about the effects visual data storytelling can have on attitudes. Additional material: https://flowstory.github.io/attitudes/.
Sublinear Time Force Computation for Big Complex Network Visualization
Authors: Amyra Meidiana, Seok-Hee Hong 0001, Marnijati Torkel, Shijun Cai, Peter Eades
Published at EuroVis, 2020

In this paper, we present a new framework for sublinear time force computation for visualization of big complex graphs. Our algorithm is based on the sampling of vertices for computing repulsion forces and edge sparsification for attraction force computation. More specifically, for vertex sampling, we present three types of sampling algorithms, including random sampling, geometric sampling, and combinatorial sampling, to reduce the repulsion force computation to sublinear in the number of vertices. We utilize a spectral sparsification approach to reduce the number of attraction force computations to sublinear in the number of edges for dense graphs. We also present a smart initialization method based on radial tree drawing of the BFS spanning tree rooted at the center.Experiments show that our new sublinear time force computation algorithms run quite fast, while producing good visualization of large and complex networks, with significant improvements in quality metrics such as shape‐based and edge crossing metrics.
Sunspot Plots: Model-based Structure Enhancement for Dense Scatter Plots
Authors: Thomas Trautner, Fabian Bolte, Sergej Stoppel, Stefan Bruckner
Published at EuroVis, 2020

Scatter plots are a powerful and well‐established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real‐world data sets and validate its effectiveness in a user study.
Survey on Individual Differences in Visualization
Authors: Zhengliang Liu, R. Jordan Crouser, Alvitta Ottley
Published at EuroVis, 2020

Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one‐size‐fits‐all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence‐based reviews, and spur future inquiry.
Survey on the Analysis of User Interactions and Visualization Provenance
Authors: Kai Xu 0003, Alvitta Ottley, Conny Walchshofer, Marc Streit, Remco Chang, John E. Wenskovitch
Published at EuroVis, 2020

There is fast‐growing literature on provenance‐related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence‐based guidelines and highlights concrete opportunities for future development in this emerging area. The survey and papers discussed can be explored online interactively at https://provenance-survey.caleydo.org.
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
Authors: Angelos Chatzimparmpas, Rafael Messias Martins, Ilir Jusufi, Kostiantyn Kucher, Fabrice Rossi, Andreas Kerren
Published at EuroVis, 2020

Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State‐of‐the‐Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web‐based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.
The State of the Art in Map-Like Visualization
Authors: Marius Hogräfer, Magnus Heitzler, Hans-Jörg Schulz
Published at EuroVis, 2020

Cartographic maps have been shown to provide cognitive benefits when interpreting data in relation to a geographic location. In visualization, the term map‐like describes techniques that incorporate characteristics of cartographic maps in their representation of abstract data. However, the field of map‐like visualization is vast and currently lacks a clear classification of the existing techniques. Moreover, choosing the right technique to support a particular visualization task is further complicated, as techniques are scattered across different domains, with each considering different characteristics as map‐like. In this paper, we give an overview of the literature on map‐like visualization and provide a hierarchical classification of existing techniques along two general perspectives: imitation and schematization of cartographic maps. Each perspective is further divided into four principal categories that group common map‐like techniques along the visual primitives they affect. We further discuss this classification from a task‐centered view and highlight open research questions.
Understanding the Design Space and Authoring Paradigms for Animated Data Graphics
Authors: John Thompson 0002, Zhicheng Liu, Wilmot Li, John T. Stasko
Published at EuroVis, 2020

Creating expressive animated data graphics often requires designers to possess highly specialized programming skills. Alternatively, the use of direct manipulation tools is popular among animation designers, but these tools have limited support for generating graphics driven by data. Our goal is to inform the design of next‐generation animated data graphic authoring tools. To understand the composition of animated data graphics, we survey real‐world examples and contribute a description of the design space. We characterize animated transitions based on object, graphic, data, and timing dimensions. We synthesize the primitives from the object, graphic, and data dimensions as a set of 10 transition types, and describe how timing primitives compose broader pacing techniques. We then conduct an ideation study that uncovers how people approach animation creation with three authoring paradigms: keyframe animation, procedural animation, and presets &amp; templates. Our analysis shows that designers have an overall preference for keyframe animation. However, we find evidence that an authoring tool should combine these three paradigms as designers’ preferences depend on the characteristics of the animated transition design and the authoring task. Based on these findings, we contribute guidelines and design considerations for developing future animated data graphic authoring tools.
v-plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions
Authors: Michael Blumenschein, Luka J. Debbeler, Nadine C. Lages, Britta Renner, Daniel A. Keim, Mennatallah El-Assady
Published at EuroVis, 2020

Comparing data distributions is a core focus in descriptive statistics, and part of most data analysis processes across disciplines. In particular, comparing distributions entails numerous tasks, ranging from identifying global distribution properties, comparing aggregated statistics (e.g., mean values), to the local inspection of single cases. While various specialized visualizations have been proposed (e.g., box plots, histograms, or violin plots), they are not usually designed to support more than a few tasks, unless they are combined. In this paper, we present the v‐plot designer; a technique for authoring custom hybrid charts, combining mirrored bar charts, difference encodings, and violin‐style plots. v‐plots are customizable and enable the simultaneous comparison of data distributions on global, local, and aggregation levels. Our system design is grounded in an expert survey that compares and evaluates 20 common visualization techniques to derive guidelines for the task‐driven selection of appropriate visualizations. This knowledge externalization step allowed us to develop a guiding wizard that can tailor v‐plots to individual tasks and particular distribution properties. Finally, we confirm the usefulness of our system design and the user‐guiding process by measuring the fitness for purpose and applicability in a second study with four domain and statistic experts.
VA-TRAC: Geospatial Trajectory Analysis for Monitoring, Identification, and Verification in Fishing Vessel Operations
Authors: S. Storm-Furru, Stefan Bruckner
Published at EuroVis, 2020

In order to ensure sustainability, fishing operations are governed by many rules and regulations that restrict the use of certain techniques and equipment, specify the species and size offish that can be harvested, and regulate commercial activities based on licensing schemes. As the world's second largest exporter offish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving science‐based regulatory body. This paper introduces VA‐TRAC, a geovisual analytics application developed in collaboration with the Norwegian Directorate of Fisheries in order to address this complex task. Our approach uses automatic methods to identify possible catch operations based on fishing vessel trajectories, embedded in an interactive web‐based visual interface used to explore the results, compare them with licensing information, and incorporate the analysts’ domain knowledge into the decision making process. We present a data and task analysis based on a close collaboration with domain experts, and the design and implementation of VA‐TRAC to address the identified requirements.
Visual Analysis of the Finite-Time Lyapunov Exponent
Authors: Antoni Sagristà, Stefan Jordan, Filip Sadlo
Published at EuroVis, 2020

In this paper, we present an integrated visual analytics approach to support the parametrization and exploration of flow visualization based on the finite‐time Lyapunov exponent. Such visualization of time‐dependent flow faces various challenges, including the choice of appropriate advection times, temporal regions of interest, and spatial resolution. Our approach eases these challenges by providing the user with context by means of parametric aggregations, with support and guidance for a more directed exploration, and with a set of derived measures for better qualitative assessment. We demonstrate the utility of our approach with examples from computation fluid dynamics and time‐dependent dynamical systems.