EuroVisShort2020

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https://diglib.eg.org/handle/10.2312/2632897

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The Effect of Graph Layout on the Perception of Graph Properties
(The Eurographics Association, 2020) Kypridemou, Elektra; Zito, Michele; Bertamini, Marco; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
The way in which a graph is described visually is crucial for the understanding and analysis of its structure. In this study we explore how different drawing layouts affect our perception of the graph's properties. We study the perception of connectedness, tree-ness and density using four different layouts: the Circular, Grid, Planar and Spring layouts. Results show that some layouts are better than others when we need to decide whether a graph is a tree or is connected. More sophisticated algorithms, like Planar and Spring, facilitate our perception, while Circular and Grid layouts lead to performance not better than chance. However, when perceiving the density of a graph, no layout was found to be better than the others.
ARGUS: Interactive Visual Analytics Framework for the Discovery of Disruptions in Bio-Behavioral Rhythms
(The Eurographics Association, 2020) Mansoor, Hamid; Gerych, Walter; Buquicchio, Luke; Alajaji, Abdulaziz; Chandrasekaran, Kavin; Agu, Emmanuel; Rundensteiner, Elke; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Human Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles and their regularity have important health ramifications. Smartphones can sense HBRs by gathering and analyzing data from built-in sensors, which provide behavioral clues. The multichannel nature (multiple sensor streams) of such data makes it challenging to pin-point the causes of disruptions in HBRs. Prior work has utilized machine learning for HBR classification but has not facilitated deeper understanding or reasoning about the potential disruption causes. In this paper, we propose ARGUS, an interactive visual analytics framework to discover and understand HBR disruptions and causes. The foundation of ARGUS is a Rhythm Deviation Score (RDS) that extracts a user's underlying 24-hour rhythm from their smartphone sensor data and quantifies its irregularity. ARGUS then visualizes the RDS using a glyph to easily recognize disruptions in HBRs, along with multiple linked panes that overlay sensor information and user-provided or smartphone-inferred ground truth as supporting context. This framework visually captures a comprehensive picture of HBRs and their disruptions. ARGUS was designed by an expert lead goal-and-task analysis. To demonstrate its generalizability, two different smartphone-sensed datasets were visualized using ARGUS in conjunction with expert feedback.
Exploring Design Opportunities for Visually Congruent Proxemics in Information Visualization: A Design Space
(The Eurographics Association, 2020) Chulpongsatorn, Neil; Yu, Jackie; Knudsen, Søren; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
We explore design opportunities for varying visual complexity of information visualizations based on distance. Through considering visual congruency and proxemics interaction, we describe a design space that considers potential transitions between visualizations in relation to distance. Our design space is based on exploring prototyping and design possibilities. It describes three properties (boundedness, connectedness, and cardinality) and five design patterns (subdivision, particalization, peculiarization, multiplication, and nesting) that might be considered in design. We describe our design ideas and prototypes, as well as reflect on their usefulness. Finally, we discuss limitations and implications of our work.
Progressive Uniform Manifold Approximation and Projection
(The Eurographics Association, 2020) Ko, Hyung-Kwon; Jo, Jaemin; Seo, Jinwook; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
We present a progressive algorithm for the Uniform Manifold Approximation and Projection (UMAP), called the Progressive UMAP. Based on the theory of Riemannian geometry and algebraic topology, UMAP is an emerging dimensionality reduction technique that offers better versatility and stability than t-SNE. Although UMAP is also more efficient than t-SNE, it still suffers from an initial delay of a few minutes to produce the first projection, which limits its use in interactive data exploration. To tackle this problem, we improve the sequential computations in UMAP by making them progressive, which allows people to incrementally append a batch of data points into the projection at the desired pace. In our experiment with the Fashion MNIST dataset, we found that Progressive UMAP could generate the first approximate projection within a few seconds while also sufficiently capturing the important structures of the high-dimensional dataset.
Joint-Sphere: Intuitive and Detailed Human Joint Motion Representation
(The Eurographics Association, 2020) Kim, Seonghun; Balasubramanyam, Adithya; Kim, Dubeom; Chai, Young Ho; Patil, Ashok Kumar; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
A motion comparison method using images allows for the motions to be easily recognized and to see differences between each action. However, when using images, orientation differences between similar motions cannot be quantified. Although many studies have been conducted on methods to represent the data and apply detailed motion comparisons, these representations are difficult to understand because the relationship between the motion and the representation is not clear. This paper introduces a novel motion representation method called the Joint-Sphere that enables detailed motion comparisons and an intuitive understanding of each joint movement. In each Joint-Sphere, the movement of a specific joint part is represented. Several Joint- Spheres can be used to represent a full-body motion. The results from a dance motion pattern show that each joint movement can be compared accurately even when several joints are moving quickly.
EuroVis 2020 Short Papers: Frontmatter
(The Eurographics Association, 2020) Kerren, Andreas; Garth, Christoph; Marai, G. Elisabeta; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
SAMBAVis: Design Study of a Visual Analytics Tool for the Music Industry Powered by YouTube Comments
(The Eurographics Association, 2020) Oliveira, Victor Adriel de Jesus; Stoiber, Christina; Grüblbauer, Johanna; Musik, Christoph; Ringot, Alexis; Gebesmair, Andreas; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Data from comments on social media platforms offer valuable information about trends and market changes. Aiming at the music industry, we propose SAMBAVis: a visual analytics tool to handle user-generated content from comments left on YouTube music videos. SAMBAVis displays main key performance indexes, video lifecycle, and engagement with comments. It also performs sentiment analysis and extracts the main keywords from the comments, expanding YouTube capabilities. In this paper, we contribute with a design study, explaining the development of SAMBAVis and the rationale of our design. We present a usage scenario and reflect on our methods and results when creating a visualization tool for experts in the music business.
Examining Design-Centric Test Participants in Graphical Perception Experiments
(The Eurographics Association, 2020) Guo, Grace; Dy, Bianchi; Ibrahim, Nazim; Joyce, Sam Conrad; Poorthuis, Ate; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
In this paper, we replicate a foundational study in graphical perception, and compare our findings from using design-centric participants with that of previous studies. We also assess the visual accuracy of two groups, students and professionals, both with design backgrounds, to identify the potential effects of participants' backgrounds on their ability to accurately read charts. Our findings demonstrate that results for reading accuracy for different chart types of previous empirical studies [CM84,HB10] are applicable to participants of design backgrounds. We also demonstrate that besides significant differences in response time, there are no significant differences in reading accuracy between the student and professional groups in our study. This indicates that, despite bias in research participants for visualization research, previous conclusions about graphical perception are likely applicable across different populations and possibly work fields.
Interactive Creation of Perceptually Uniform Color Maps
(The Eurographics Association, 2020) Lambers, Martin; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
A large number of design rules have been identified for color maps used in Scientific Visualization. One of the most important of these is perceptual uniformity, which at the same time is one of the hardest to guarantee when color maps are created from user input. In this paper, we propose parameterized color map models for a variety of application areas. To allow interactive creation of color maps, these models are based on few intuitive parameters, and at the same time guarantee approximate perceptual uniformity.
Sketchy Rendering to Aid the Recollection of Regular Visualizations
(The Eurographics Association, 2020) Larsen, Michael Reidun Engelbrecht; Han, Wenkai; Schulz, Hans-Jörg; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Some visualizations have a more regular visual appearance than others. For example, while stream graphs or force-directed network layouts feature a unique, almost organic 'look and feel', matrices or unit treemaps can become rather bland, grid-like visualizations in which one data item is hard to tell apart from the next. In this paper, we investigate the use of sketchy rendering for such grid-like visualizations to give them a slightly more unique 'look and feel' themselves. We evaluate our approach in a lab study (N = 16) where participants were asked to re-find a given grid cell in regular and sketchy grids. We find that users who make conscious use of the sketchy features can benefit from certain forms of sketchy rendering in terms of task completion times.
Design of a Real Time Visual Analytics Support Tool for Conflict Detection and Resolution in Air Traffic Control
(The Eurographics Association, 2020) Zohrevandi, Elmira; Westin, Carl A. L.; Lundberg, Jonas; Ynnerman, Anders; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Air traffic control is a safety critical high-risk environment where operators need to analyse and interpret traffic dynamics of spatio-temporal data in real-time. To support the air traffic controller in safely separating traffic, earlier research has applied real-time visualisation techniques that explore the constraints and solution spaces of separation problems. Traditionally, situation displays for conflict detection and resolution have used visualisations that convey information about the relative horizontal position between aircraft. Although vertical solutions for solving conflicts are common, and often a preferred among controllers, visualisations typically provide limited information about the vertical relationship between aircraft. This paper presents a design study of an interactive conflict detection and resolution support tool and explores techniques for real-time visualisation of spatio-temporal data. The design evolution has incorporated several activities, including an initial work domain analysis, iterative rounds of programming, design, and evaluations with a domain expert, and an evaluation with eight active controllers. The heading-time-altitude visualisation system is developed based on formulating and solving aircraft movements in a relative coordinate system. A polar-graph visualisation technique is used to construct a view of conflicting aircraft vertical solution spaces in the temporal domain. Using composite glyphs, the final heading-time-altitude visualisation provides a graphical representation of both horizontal and vertical solution spaces for the traffic situation.
MODELAR: A MODular and EvaLuative framework to improve surgical Augmented Reality visualization
(The Eurographics Association, 2020) Hattab, Georges; Meyer, Felix; Albrecht, Remke Dirk; Speidel, Stefanie; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
The use of Augmented Reality (AR) for the visualization of 3D biomedical image data is possible thanks to a growing number of hardware and software solutions. Considerable efforts are made during surgery, where the visual information of the target structures can either be highlighted or dulled. However, as technical challenges and barriers to development decrease, it's increasingly important to take into account the specific capacities and constraints of the surgeon's perceptual and cognitive systems. To address this legitimate problem, we present a practical framework that evaluates the importance of visual encodings and renderings for surgical AR. By conducting a task-specific user study we observed a set of emerging visualization strategies. The given task is to make the kidney boundary visually salient and make the tumor and calyx distinguishable. After having recruited 23 participants, we found two preferred presets to tackle this task. With both presets, the usage of color, depth, and opacity improved the display of the organ bounds while contrasting the tumor and calyx. 19 participants successfully completed the task using MODELAR. Their preference was to either find a good preset where the organ bounds were visible then adjust the color of target objects or vice versa. MODELAR helped us better identify effective visualization that best fit the task requirements. Our evaluation results and the modular framework MODELAR is freely available and open source at https://github.com/ghattab/MODELAR.
Width-Scale Bar Charts for Data with Large Value Range
(The Eurographics Association, 2020) Höhn, Markus; Wunderlich, Marcel; Ballweg, Kathrin; Landesberger, Tatiana von; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Data sets with large value range are difficult to visualize with traditional linear bar charts. Usually, a logarithmic scale is used in these cases. However, the logarithmic scale suffers from non-linearity. Recently, scale-stack bar charts and magnitude markers, improve the readability of values. However, they have other disadvantages such as various scales or several objects for visualizing one value. We propose the width-scale bar chart that uses width, height and color to cover a large value range within one linear scale. A quantitative user study shows advantages of our design - especially for reading values.
Effective Visualization of Sparse Image-to-Image Correspondences
(The Eurographics Association, 2020) Andujar, Carlos; Chica, Antonio; Comino Trinidad, Marc; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Finding robust correspondences between images is a crucial step in photogrammetry applications. The traditional approach to visualize sparse matches between two images is to place them side-by-side and draw link segments connecting pixels with matching features. In this paper we present new visualization techniques for sparse correspondences between image pairs. Key ingredients of our techniques include (a) the clustering of consistent matches, (b) the optimization of the image layout to minimize occlusions due to the super-imposed links, (c) a color mapping to minimize color interference among links (d) a criterion for giving visibility priority to isolated links, (e) the bending of link segments to put apart nearby links, and (f) the use of glyphs to facilitate the identification of matching keypoints. We show that our technique substantially reduces the clutter in the final composite image and thus makes it easier to detect and inspect both inlier and outlier matches. Potential applications include the validation of image pairs in difficult setups and the visual comparison of feature detection / matching algorithms.
Task-based Colormap Design Supporting Visual Comprehension in Process Tomography
(The Eurographics Association, 2020) Zhang, Yuchong; Fjeld, Morten; Said, Alan; Fratarcangeli, Marco; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Color coding is a fundamental technique for mapping data to visual representations, allowing people to carry out comprehension-based tasks. Process tomography is a rapidly developing non-invasive imaging technique used in various fields of science due to its effective flow monitoring and data acquisition [KLS*19]. To study how well colormaps can support visual comprehension of tomographic data, we conduct a feasibility evaluation of 11 widely-used color schemes. We employ the same segmentation tasks characterized by Microwave Tomography (MWT) on each individual chosen colormap, and then conduct a quantitative assessment of those schemes. Based on the insight gained, we conclude that autumn, viridis, and parula colormaps yield the best segmentation results. According to our findings, we propose a colormap design guideline for practitioners and researchers in the field of process tomography.
Designing Pairs of Colormaps for Visualizing Bivariate Scalar Fields
(The Eurographics Association, 2020) Ware, Colin; Samsel, Francesca; Rogers, David H.; Navratil, Paul; Mohammed, Ayat; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
In scientific visualization there is sometimes a requirement for two colormaps to be used to represent two co-registered scalar fields. One solution is to represent one of the fields as a continuous colormapped image, and the second field by means of a dense distribution of small glyphs overlaid on the background image and coded using a different colormap. This requires the design of pairs of colormaps which each can be easily read, but which minimally interfere with one another. Colormap pairs separated according to lightness, saturation and hue, were designed and evaluated using both a key accuracy task and a pattern identification task. The saturation separation pair (one colormap having high saturation and the other low saturation) was the best overall.
Evaluating Strategies of Exploratory Visual Data Analysis in Multi Device Environments
(The Eurographics Association, 2020) Alsaiari, Abeer; Aurisano, Jillian; Johnson, Andrew E.; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Supporting exploratory visual data analysis is essential when multiple analysts collaborate using multiple devices. Yet, we still have no full understanding of how the iterative process of analysis unfolds in complex settings. In this paper, we present the results from an exploratory study where six groups of three participants performed a collaborative visual data analysis task in a complex multi-user multi-device environment. We found that the course of the analysis happens at two levels. Within each level, we observed a set of exploration patterns. We present a categorization of the analysis structure in such a complex environment and discuss the implications of device affordances on this categorization. We also discuss this categorization in relation to the current structural assumptions of exploratory visual analysis.
Glyph-Based Visualization of Affective States
(The Eurographics Association, 2020) Kovacevic, Nikola; Wampfler, Rafael; Solenthaler, Barbara; Gross, Markus; Günther, Tobias; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Decades of research in psychology on the formal measurement of emotions led to the concept of affective states. Visualizing the measured affective state can be useful in education, as it allows teachers to adapt lessons based on the affective state of students. In the entertainment industry, game mechanics can be adapted based on the boredom and frustration levels of a player. Visualizing the affective state can also increase emotional self-awareness of the user whose state is being measured, which can have an impact on well-being. However, graphical user interfaces seldom visualize the user's affective state, but rather focus on the purely objective interaction between the system and the user. This paper proposes two graphical user interface widgets that visualize the user's affective state, ensuring a compact and unobtrusive visualization. In a user study with 644 participants, the widgets were evaluated in relation to a baseline widget and were tested on intuitiveness and understandability. Particularly in terms of understandability, the baseline was outperformed by our two widgets.
Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques
(The Eurographics Association, 2020) Bors, Christian; Eichner, Christian; Miksch, Silvia; Tominski, Christian; Schumann, Heidrun; Gschwandtner, Theresia; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Time series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation pipeline is composed of different steps involving several parameterizable algorithms. Existing Visual Analytics approaches can help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations, while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement. The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings of general validity.
TopoLines: Topological Smoothing for Line Charts
(The Eurographics Association, 2020) Rosen, Paul; Suh, Ashley; Salgado, Christopher; Hajij, Mustafa; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Line charts are commonly used to visualize a series of data values. When the data are noisy, smoothing is applied to make the signal more apparent. Conventional methods used to smooth line charts, e.g., using subsampling or filters, such as median, Gaussian, or low-pass, each optimize for different properties of the data. The properties generally do not include retaining peaks (i.e., local minima and maxima) in the data, which is an important feature for certain visual analytics tasks. We present TopoLines, a method for smoothing line charts using techniques from Topological Data Analysis. The design goal of TopoLines is to maintain prominent peaks in the data while minimizing any residual error. We evaluate TopoLines for 2 visual analytics tasks by comparing to 5 popular line smoothing methods with data from 4 application domains.

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