39-Issue 3

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https://diglib.eg.org/handle/10.2312/2632882

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VisuaLint: Sketchy In Situ Annotations of Chart Construction Errors
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Hopkins, Aspen K.; Correll, Michael; Satyanarayan, Arvind; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Chart construction errors, such as truncated axes or inexpressive visual encodings, can hinder reading a visualization, or worse, imply misleading facts about the underlying data. These errors can be caught by critical readings of visualizations, but readers must have a high level of data and design literacy and must be paying close attention. To address this issue, we introduce VisuaLint: a technique for surfacing chart construction errors in situ. Inspired by the ubiquitous red wavy underline that indicates spelling mistakes, visualization elements that contain errors (e.g., axes and legends) are sketchily rendered and accompanied by a concise annotation. VisuaLint is unobtrusive-it does not interfere with reading a visualization-and its direct display establishes a close mapping between erroneous elements and the expression of error. We demonstrate five examples of VisualLint and present the results of a crowdsourced evaluation (N = 62) of its efficacy. These results contribute an empirical baseline proficiency for recognizing chart construction errors, and indicate near-universal difficulty in error identification. We find that people more reliably identify chart construction errors after being shown examples of VisuaLint, and prefer more verbose explanations for unfamiliar or less obvious flaws.
Feature Driven Combination of Animated Vector Field Visualizations
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lobo, María Jesús; Telea, Alexandru; Hurter, Christophe; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Animated visualizations are one of the methods for finding and understanding complex structures of time-dependent vector fields. Many visualization designs can be used to this end, such as streamlines, vector glyphs, and image-based techniques. While all such designs can depict any vector field, their effectiveness in highlighting particular field aspects has not been fully explored. To fill this gap, we compare three animated vector field visualization techniques, OLIC, IBFV, and particles, for a critical point detection-and-classification task through a user study. Our results show that the effectiveness of the studied techniques depends on the nature of the critical points. We use these results to design a new flow visualization technique that combines all studied techniques in a single view by locally using the most effective technique for the patterns present in the flow data at that location. A second user study shows that our technique is more efficient and less error prone than the three other techniques used individually for the critical point detection task.
v-plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Blumenschein, Michael; Debbeler, Luka J.; Lages, Nadine C.; Renner, Britta; Keim, Daniel A.; El-Assady, Mennatallah; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
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 userguiding 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
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Storm-Furru, Syver; Bruckner, Stefan; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
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 of fish that can be harvested, and regulate commercial activities based on licensing schemes. As the world's second largest exporter of fish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving sciencebased 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.
Infomages: Embedding Data into Thematic Images
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Coelho, Darius; Mueller, Klaus; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Recent studies have indicated that visually embellished charts such as infographics have the ability to engage viewers and positively affect memorability. Fueled by these findings, researchers have proposed a variety of infographic design tools. However, these tools do not cover the entire design space. In this work, we identify a subset of infographics that we call infomages. Infomages are casual visuals of data in which a data chart is embedded into a thematic image such that the content of the image reflects the subject and the designer's interpretation of the data. Creating an effective infomage, however, can require a fair amount of design expertise and is thus out of reach for most people. In order to also afford non-artists with the means to design convincing infomages, we first study the principled design of existing infomages and identify a set of key chart embedding techniques. Informed by these findings we build a design tool that links web-scale image search with a set of interactive image processing tools to empower novice users with the ability to design a wide variety of infomages. As the embedding process might introduce some amount of visual distortion of the data our tool also aids users to gauge the amount of this distortion, if any. We experimentally demonstrate the usability of our tool and conclude with a discussion of infomages and our design tool.
Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bäuerle, Alex; Neumann, Heiko; Ropinski, Timo; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introduce errors, which compromise valuable training data, and lead to suboptimal training results. We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set. To systematically investigate training data, we propose a categorization of labeling errors into three different types, based on an analysis of potential pitfalls in label acquisition processes. For each of these types, we present approaches to detect, reason about, and resolve error candidates, as we propose measures and visual guidance techniques to support machine learning users. Our approach has been used to spot errors in well-known machine learning benchmark data sets, and we tested its usability during a user evaluation. While initially developed for images, the techniques presented in this paper are independent of the classification algorithm, and can also be extended to many other types of training data.
Boxer: Interactive Comparison of Classifier Results
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Gleicher, Michael; Barve, Aditya; Yu, Xinyi; Heimerl, Florian; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.
PAVED: Pareto Front Visualization for Engineering Design
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Cibulski, Lena; Mitterhofer, Hubert; May, Thorsten; Kohlhammer, Jörn; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto-optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most-preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED, an interactive parallel coordinates visualization for exploration of multi-criteria alternatives. We reflect on our user-centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real-world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi-criteria optimization problems in engineering design or alternative domains.
SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Xia, Meng; Xu, Min; Lin, Chuan-en; Cheng, Ta Ying; Qu, Huamin; Ma, Xiaojuan; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
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.
Fiber Surfaces for many Variables
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Blecha, Christian; Raith, Felix; Präger, Arne Jonas; Nagel, Thomas; Kolditz, Olaf; Maßmann, Jobst; Röber, Niklas; Böttinger, Michael; Scheuermann, Gerik; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Scientific visualization deals with increasingly complex data consisting of multiple fields. Typical disciplines generating multivariate data are fluid dynamics, structural mechanics, geology, bioengineering, and climate research. Quite often, scientists are interested in the relation between some of these variables. A popular visualization technique for a single scalar field is the extraction and rendering of isosurfaces. With this technique, the domain can be split into two parts, i.e. a volume with higher values and one with lower values than the selected isovalue. Fiber surfaces generalize this concept to two or three scalar variables up to now. This article extends the notion further to potentially any finite number of scalar fields. We generalize the fiber surface extraction algorithm of Raith et al. [RBN*19] from 3 to d dimensions and demonstrate the technique using two examples from geology and climate research. The first application concerns a generic model of a nuclear waste repository and the second one an atmospheric simulation over central Europe. Both require complex simulations which involve multiple physical processes. In both cases, the new extended fiber surfaces helps us finding regions of interest like the nuclear waste repository or the power supply of a storm due to their characteristic properties.
Canis: A High-Level Language for Data-Driven Chart Animations
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Ge, Tong; Zhao, Yue; Lee, Bongshin; Ren, Donghao; Chen, Baoquan; Wang, Yunhai; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
In this paper, we introduce Canis, a high-level domain-specific language that enables declarative specifications of data-driven chart animations. By leveraging data-enriched SVG charts, its grammar of animations can be applied to the charts created by existing chart construction tools. With Canis, designers can select marks from the charts, partition the selected marks into mark units based on data attributes, and apply animation effects to the mark units, with the control of when the effects start. The Canis compiler automatically synthesizes the Lottie animation JSON files [Aira], which can be rendered natively across multiple platforms. To demonstrate Canis' expressiveness, we present a wide range of chart animations. We also evaluate its scalability by showing the effectiveness of our compiler in reducing the output specification size and comparing its performance on different platforms against D3.
GTMapLens: Interactive Lens for Geo-Text Data Browsing on Map
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Ma, Chao; Zhao, Ye; AL-Dohuki, Shamal; Yang, Jing; Ye, Xinyue; Kamw, Farah; Amiruzzaman, Md; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Data containing geospatial semantics, such as geotagged tweets, travel blogs, and crime reports, associates natural language texts with geographical locations. This paper presents a lens-based visual interaction technique, GTMapLens, to flexibly browse the geo-text data on a map. It allows users to perform dynamic focus+context exploration by using movable lenses to browse geographical regions, find locations of interest, and perform comparative and drill-down studies. Geo-text data is visualized in a way that users can easily perceive the underlying geospatial semantics along with lens moving. Based on a requirement analysis with a cohort of multidisciplinary domain experts, a set of lens interaction techniques are developed including keywords control, path management, context visualization, and snapshot anchors. They allow users to achieve a guided and controllable exploration of geo-text data. A hierarchical data model enables the interactive lens operations by accelerated data retrieval from a geo-text database. Evaluation with real-world datasets is presented to show the usability and effectiveness of GTMapLens.
QUESTO: Interactive Construction of Objective Functions for Classification Tasks
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Das, Subhajit; Xu, Shenyu; Gleicher, Michael; Chang, Remco; Endert, Alex; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta-information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain-specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.
Ocupado: Visualizing Location-Based Counts Over Time Across Buildings
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Oppermann, Michael; Munzner, Tamara; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Understanding how spaces in buildings are being used is vital for optimizing space utilization, for improving resource allocation, and for the design of new facilities. We present a multi-year design study that resulted in Ocupado, a set of visual decision-support tools centered around occupancy data for stakeholders in facilities management and planning. Ocupado uses WiFi devices as a proxy for human presence, capturing location-based counts that preserve privacy without trajectories. We contribute data and task abstractions for studying space utilization for combinations of data granularities in both space and time. In addition, we contribute generalizable design choices for visualizing location-based counts relating to indoor environments. We provide evidence of Ocupado's utility through multiple analysis scenarios with real-world data refined through extensive stakeholder feedback, and discussion of its take-up by our industry partner.
Understanding the Design Space and Authoring Paradigms for Animated Data Graphics
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Thompson, John R.; Liu, Zhicheng; Li, Wilmot; Stasko, John; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
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 & 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.
Sublinear Time Force Computation for Big Complex Network Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Meidiana, Amyra; Hong, Seok-Hee; Torkel, Marnijati; Cai, Shijun; Eades, Peter; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
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.
Orchard: Exploring Multivariate Heterogeneous Networks on Mobile Phones
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Eichmann, Philipp; Edge, Darren; Evans, Nathan; Lee, Bongshin; Brehmer, Matthew; White, Christopher; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
People are becoming increasingly sophisticated in their ability to navigate information spaces using search, hyperlinks, and visualization. But, mobile phones preclude the use of multiple coordinated views that have proven effective in the desktop environment (e.g., for business intelligence or visual analytics). In this work, we propose to model information as multivariate heterogeneous networks to enable greater analytic expression for a range of sensemaking tasks while suggesting a new, list-based paradigm with gestural navigation of structured information spaces on mobile phones. We also present a mobile application, called Orchard, which combines ideas from both faceted search and interactive network exploration in a visual query language to allow users to collect facets of interest during exploratory navigation. Our study showed that users could collect and combine these facets with Orchard, specifying network queries and projections that would only have been possible previously using complex data tools or custom data science.
Augmenting Node-Link Diagrams with Topographic Attribute Maps
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Preiner, Reinhold; Schmidt, Johanna; Krösl, Katharina; Schreck, Tobias; Mistelbauer, Gabriel; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
We propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute-based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force-directed node-link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph-related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.
Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Liem, Johannes; Perin, Charles; Wood, Jo; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
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.
PEAX: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lekschas, Fritz; Peterson, Brant; Haehn, Daniel; Ma, Eric; Gehlenborg, Nils; Pfister, Hanspeter; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
We present PEAX, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user's perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity. Using an active learning sampling strategy, PEAX collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate PEAX's features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of PEAX. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.

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