DevLan/vispubs huggingface dataset:
An Exploratory User Study of Visual Causality Analysis
Authors: Chi-Hsien (Eric) Yen, Aditya G. Parameswaran, Wai-Tat Fu
Published at EuroVis, 2019

Interactive visualization tools are being used by an increasing number of members of the general public; however, little is known about how, and how well, people use visualizations to infer causality. Adapted from the mediation causal model, we designed an analytic framework to systematically evaluate human performance, strategies, and pitfalls in a visual causal reasoning task. We recruited 24 participants and asked them to identify the mediators in a fictitious dataset using bar charts and scatter plots within our visualization interface. The results showed that the accuracy of their responses as to whether a variable is a mediator significantly decreased when a confounding variable directly influenced the variable being analyzed. Further analysis demonstrated how individual visualization exploration strategies and interfaces might influence reasoning performance. We also identified common strategies and pitfalls in their causal reasoning processes. Design implications for how future visual analytics tools can be designed to better support causal inference are discussed.
An Interactive Visualization System for Large Sets of Phase Space Trajectories
Authors: Tyson Neuroth, Franz Sauer, Kwan-Liu Ma
Published at EuroVis, 2019

We introduce a visual analysis system with GPU acceleration techniques for large sets of trajectories from complex dynamical systems. The approach is based on an interactive Boolean combination of subsets into a Focus+Context phase‐space visualization. We achieve high performance through efficient bitwise algorithms utilizing runtime generated GPU shaders and kernels. This enables a higher level of interactivity for visualizing the large multivariate trajectory data. We explain how our design meets a set of carefully considered analysis requirements, provide performance results, and demonstrate utility through case studies with many‐particle simulation data from two application areas.
An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems
Authors: Min Chen 0001, David S. Ebert
Published at EuroVis, 2019

Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipating secondary effects of the solutions. The methodology is built on the theoretical premise that designing a VA workflow is an optimization of the cost‐benefit ratio of the processes in the workflow. It makes uses three fundamental measures to group and connect “symptoms”, “causes”, “remedies”, and “side‐effects”, and guide the search for potential solutions to the problems. In terms of requirement analysis and system design, the proposed methodology can enable system designers to explore the decision space in a structured manner. In terms of evaluation, the proposed methodology is time‐efficient and complementary to various forms of empirical studies, such as user surveys, controlled experiments, observational studies, focus group discussions, and so on. In general, it reduces the amount of trial‐and‐error in the lifecycle of VA system development.
Analysis of Decadal Climate Predictions with User-guided Hierarchical Ensemble Clustering
Authors: Christopher P. Kappe, Michael Böttinger, Heike Leitte
Published at EuroVis, 2019

In order to gain probabilistic results, ensemble simulation techniques are increasingly applied in the weather and climate sciences (as well as in various other scientific disciplines). In many cases, however, only mean results or other abstracted quantities such as percentiles are used for further analyses and dissemination of the data. In this work, we aim at a more detailed visualization of the temporal development of the whole ensemble that takes the variability of all single members into account. We propose a visual analytics tool that allows an effective analysis process based on a hierarchical clustering of the time‐dependent scalar fields. The system includes a flow chart that shows the ensemble members' cluster affiliation over time, reflecting the whole cluster hierarchy. The latter one can be dynamically explored using a visualization derived from a dendrogram. As an aid in linking the different views, we have developed an adaptive coloring scheme that takes into account cluster similarity and the containment relationships. Finally, standard visualizations of the involved field data (cluster means, ground truth data, etc.) are also incorporated. We include results of our work on real‐world datasets to showcase the utility of our approach.
Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization
Authors: Jan Byska, Thomas Trautner, Sérgio M. Marques, Jirí Damborský, Barbora Kozlíková, Manuela Waldner
Published at EuroVis, 2019

Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio‐temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.
Augmenting Tactile 3D Data Navigation With Pressure Sensing
Authors: Xiyao Wang, Lonni Besançon, Mehdi Ammi, Tobias Isenberg 0001
Published at EuroVis, 2019

We present a pressure‐augmented tactile 3D data navigation technique, specifically designed for small devices, motivated by the need to support the interactive visualization beyond traditional workstations. While touch input has been studied extensively on large screens, current techniques do not scale to small and portable devices. We use phone‐based pressure sensing with a binary mapping to separate interaction degrees of freedom (DOF) and thus allow users to easily select different manipulation schemes (e. g., users first perform only rotation and then with a simple pressure input to switch to translation). We compare our technique to traditional 3D‐RST (rotation, scaling, translation) using a docking task in a controlled experiment. The results show that our technique increases the accuracy of interaction, with limited impact on speed. We discuss the implications for 3D interaction design and verify that our results extend to older devices with pseudo pressure and are valid in realistic phone usage scenarios.
Bird's-Eye - Large-Scale Visual Analytics of City Dynamics using Social Location Data
Authors: Robert Krüger, Qi Han 0006, Nikolay Ivanov, Sanae Mahtal, Dennis Thom, Hanspeter Pfister, Thomas Ertl
Published at EuroVis, 2019

The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large‐scale and up‐to‐date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up‐to‐date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal‐and‐task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back‐end and an interactive visual analytics interface. The monitoring component uses intelligent pre‐aggregation to allow dynamic queries in near real‐time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advanced analytical tools with visual interfaces is pivotal in making behavioral city dynamics accessible to practitioners. Our interviews also revealed areas for future research.
Bridging the Data Analysis Communication Gap Utilizing a Three-Component Summarized Line Graph
Authors: Calvin Yau, Morteza Karimzadeh, Chittayong Surakitbanharn, Niklas Elmqvist, David S. Ebert
Published at EuroVis, 2019

Communication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision‐makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time‐series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.
Capture & Analysis of Active Reading Behaviors for Interactive Articles on the Web
Authors: Matthew Conlen, Alex Kale, Jeffrey Heer
Published at EuroVis, 2019

Journalists, educators, and technical writers are increasingly publishing interactive content on the web. However, popular analytics tools provide only coarse information about how readers interact with individual pages, and laboratory studies often fail to capture the variability of a real‐world audience. We contribute extensions to the Idyll markup language to automate the detailed instrumentation of interactive articles and corresponding visual analysis tools for inspecting reader behavior at both micro‐ and macro‐levels. We present three case studies of interactive articles that were instrumented, posted online, and promoted via social media to reach broad audiences, and share data from over 50,000 reader sessions. We demonstrate the use of our tools to characterize article‐specific interaction patterns, compare behavior across desktop and mobile devices, and reveal reading patterns common across articles. Our contributed findings, tools, and corpus of behavioral data can help advance and inform more comprehensive studies of narrative visualization.
Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau
Authors: Leilani Battle, Jeffrey Heer
Published at EuroVis, 2019

Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and yet our understanding of the process is arguably vague and piecemeal. We contribute a consistent definition of EVA through review of the relevant literature, and an empirical evaluation of existing assumptions regarding how analysts perform EVA using Tableau, a popular visual analysis tool. We present the results of a study where 27 Tableau users answered various analysis questions across 3 datasets. We measure task performance, identify recurring patterns across participants' analyses, and assess variance from task specificity and dataset. We find striking differences between existing assumptions and the collected data. Participants successfully completed a variety of tasks, with over 80% accuracy across focused tasks with measurably correct answers. The observed cadence of analyses is surprisingly slow compared to popular assumptions from the database community. We find significant overlap in analyses across participants, showing that EVA behaviors can be predictable. Furthermore, we find few structural differences between behavior graphs for open‐ended and more focused exploration tasks.
ChronoCorrelator: Enriching Events with Time Series
Authors: M. A. M. M. van Dortmont, Stef van den Elzen, Jarke J. van Wijk
Published at EuroVis, 2019

Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.
ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns
Authors: Mostafa M. Abbas, Michaël Aupetit 0001, Michael Sedlmair, Halima Bensmail
Published at EuroVis, 2019

We propose ClustMe, a new visual quality measure to rank monochrome scatterplots based on cluster patterns. ClustMe is based on data collected from a human‐subjects study, in which 34 participants judged synthetically generated cluster patterns in 1000 scatterplots. We generated these patterns by carefully varying the free parameters of a simple Gaussian Mixture Model with two components, and asked the participants to count the number of clusters they could see (1 or more than 1). Based on the results, we form ClustMe by selecting the model that best predicts these human judgments among 7 different state‐of‐the‐art merging techniques (Demp). To quantitatively evaluate ClustMe, we conducted a second study, in which 31 human subjects ranked 435 pairs of scatterplots of real and synthetic data in terms of cluster patterns complexity. We use this data to compare ClustMe's performance to 4 other state‐of‐the‐art clustering measures, including the well‐known Clumpiness scagnostics. We found that of all measures, ClustMe is in strongest agreement with the human rankings.
CV3: Visual Exploration, Assessment, and Comparison of CVs
Authors: Velitchko Andreev Filipov, Alessio Arleo, Paolo Federico 0001, Silvia Miksch
Published at EuroVis, 2019

The Curriculum Vitae (CV, also referred to as “résumé”) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose “CV3”, an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.
Designing Animated Transitions to Convey Aggregate Operations
Authors: Younghoon Kim, Michael Correll, Jeffrey Heer
Published at EuroVis, 2019

Data can be aggregated in many ways before being visualized in charts, profoundly affecting what a chart conveys. Despite this importance, the type of aggregation is often communicated only via axis titles. In this paper, we investigate the use of animation to disambiguate different types of aggregation and communicate the meaning of aggregate operations. We present design rationales for animated transitions depicting aggregate operations and present the results of an experiment assessing the impact of these different transitions on identification tasks. We find that judiciously staged animated transitions can improve subjects' accuracy at identifying the aggregation performed, though sometimes with longer response times than with static transitions. Through an analysis of participants' rankings and qualitative responses, we find a consistent preference for animation over static transitions and highlight visual features subjects report relying on to make their judgments. We conclude by extending our animation designs to more complex charts of aggregated data such as box plots and bootstrapped confidence intervals.
DIVA: Exploration and Validation of Hypothesized Drug-Drug Interactions
Authors: Tabassum Kakar, Xiao Qin 0003, Elke A. Rundensteiner, Lane Harrison, Sanjay K. Sahoo, Suranjan De
Published at EuroVis, 2019

Adverse reactions caused by drug‐drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post‐marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DTVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain‐specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in‐depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case‐studies and interviews by drug analysts at the US Food and Drug Administration ‐ both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug‐drug interactions.
Efficient Optimal Overlap Removal: Algorithms and Experiments
Authors: Wouter Meulemans
Published at EuroVis, 2019

Motivated by visualizing spatial data using proportional symbols, we study the following problem: given a set of overlapping squares of varying sizes, minimally displace the squares as to remove the overlap while maintaining the orthogonal order on their centers. Though this problem is NP‐hard, we show that rotating the squares by 45 degrees into diamonds allows for a linear or convex quadratic program. It is thus efficiently solvable even for relatively large instances.This positive result and the flexibility offered by constraint programming allow us to study various trade‐offs for overlap removal. Specifically, we model and evaluate through computational experiments the relations between displacement, scale and order constraints for static data, and between displacement and temporal coherence for time‐varying data. Finally, we also explore the generalization of our methodology to other shapes.
Evaluating image quality measures to assess the impact of lossy data compression applied to climate simulation data
Authors: Allison H. Baker, Dorit Hammerling, Terece L. Turton
Published at EuroVis, 2019

Applying lossy data compression to climate model output is an attractive means of reducing the enormous volumes of data generated by climate models. However, because lossy data compression does not exactly preserve the original data, its application to scientific data must be done judiciously. To this end, a collection of measures is being developed to evaluate various aspects of lossy compression quality on climate model output. Given the importance of data visualization to climate scientists interacting with model output, any suite of measures must include a means of assessing whether images generated from the compressed model data are noticeably different from images based on the original model data. Therefore, in this work we conduct a forced‐choice visual evaluation study with climate model data that surveyed more than one hundred participants with domain relevant expertise. In addition to the images created from unaltered climate model data, study images are generated from model data that is subjected to two different types of lossy compression approaches and multiple levels (amounts) of compression. Study participants indicate whether a visual difference can be seen, with respect to the reference image, due to lossy compression effects. We assess the relationship between the perceptual scores from the user study to a number of common (full reference) image quality assessment (IQA) measures, and use statistical models to suggest appropriate measures and thresholds for evaluating lossily compressed climate data. We find the structural similarity index (SSIM) to perform the best, and our findings indicate that the threshold required for climate model data is much higher than previous findings in the literature.
Examining Implicit Discretization in Spectral Schemes
Authors: P. Samuel Quinan, Lace M. K. Padilla, Sarah H. Creem-Regehr, Miriah D. Meyer
Published at EuroVis, 2019

Two of the primary reasons rainbow color maps are considered ineffective trace back to the idea that they implicitly discretize encoded data into hue‐based bands, yet no research addresses what this discretization looks like or how consistent it is across individuals. This paper presents an exploratory study designed to empirically investigate the implicit discretization of common spectral schemes and explore whether the phenomenon can be modeled by variations in lightness, chroma, and hue. Our results suggest that three commonly used rainbow color maps are implicitly discretized with consistency across individuals. The results also indicate, however, that this implicit discretization varies across different datasets, in a way that suggests the visualization community's understanding of both rainbow color maps, and more generally effective color usage, remains incomplete.