40-Issue 3

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https://diglib.eg.org/handle/10.2312/2633046

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Optimal Axes for Data Value Estimation in Star Coordinates and Radial Axes Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Rubio-Sánchez, Manuel; Lehmann, Dirk J.; Sanchez, Alberto; Rojo-Álvarez, Jose Luis; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Radial axes plots are projection methods that represent high-dimensional data samples as points on a two-dimensional plane. These techniques define mappings through a set of axis vectors, each associated with a data variable, which users can manipulate interactively to create different plots and analyze data from multiple points of view. However, updating the direction and length of an axis vector is far from trivial. Users must consider the data analysis task, domain knowledge, the directions in which values should increase, the relative importance of each variable, or the correlations between variables, among other factors. Another issue is the difficulty to approximate high-dimensional data values in the two-dimensional visualizations, which can hamper searching for data with particular characteristics, analyzing the most common data values in clusters, inspecting outliers, etc. In this paper we present and analyze several optimization approaches for enhancing radial axes plots regarding their ability to represent high-dimensional data values. The techniques can be used not only to approximate data values with greater accuracy, but also to guide users when updating axis vectors or extending visualizations with new variables, since they can reveal poor choices of axis vectors. The optimal axes can also be included in nonlinear plots. In particular, we show how they can be used within RadViz to assess the quality of a variable ordering. The in-depth analysis carried out is useful for visualization designers developing radial axes techniques, or planning to incorporate axes into other visualization methods.
Color Nameability Predicts Inference Accuracy in Spatial Visualizations
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Reda, Khairi; Salvi, Amey A.; Gray, Jack; Papka, Michael E.; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Color encoding is foundational to visualizing quantitative data. Guidelines for colormap design have traditionally emphasized perceptual principles, such as order and uniformity. However, colors also evoke cognitive and linguistic associations whose role in data interpretation remains underexplored. We study how two linguistic factors, name salience and name variation, affect people's ability to draw inferences from spatial visualizations. In two experiments, we found that participants are better at interpreting visualizations when viewing colors with more salient names (e.g., prototypical 'blue', 'yellow', and 'red' over 'teal', 'beige', and 'maroon'). The effect was robust across four visualization types, but was more pronounced in continuous (e.g., smooth geographical maps) than in similar discrete representations (e.g., choropleths). Participants' accuracy also improved as the number of nameable colors increased, although the latter had a less robust effect. Our findings suggest that color nameability is an important design consideration for quantitative colormaps, and may even outweigh traditional perceptual metrics. In particular, we found that the linguistic associations of color are a better predictor of performance than the perceptual properties of those colors. We discuss the implications and outline research opportunities. The data and materials for this study are available at https://osf.io/asb7n
TourVis: Narrative Visualization of Multi-Stage Bicycle Races
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Díaz, Jose; Fort, Marta; Vázquez, Pere-Pau; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
There are many multiple-stage racing competitions in various sports such as swimming, running, or cycling. The wide availability of affordable tracking devices facilitates monitoring the position along with the race of all participants, even for non-professional contests. Getting real-time information of contenders is useful but also unleashes the possibility of creating more complex visualization systems that ease the understanding of the behavior of all participants during a simple stage or throughout the whole competition. In this paper we focus on bicycle races, which are highly popular, especially in Europe, being the Tour de France its greatest exponent. Current visualizations from TV broadcasting or real-time tracking websites are useful to understand the current stage status, up to a certain extent. Unfortunately, still no current system exists that visualizes a whole multi-stage contest in such a way that users can interactively explore the relevant events of a single stage (e.g. breakaways, groups, virtual leadership: : :), as well as the full competition. In this paper, we present an interactive system that is useful both for aficionados and professionals to visually analyze the development of multi-stage cycling competitions.
Implicit Modeling of Patient-Specific Aortic Dissections with Elliptic Fourier Descriptors
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Mistelbauer, Gabriel; Rössl, Christian; Bäumler, Kathrin; Preim, Bernhard; Fleischmann, Dominik; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Aortic dissection is a life-threatening vascular disease characterized by abrupt formation of a new flow channel (false lumen) within the aortic wall. Survivors of the acute phase remain at high risk for late complications, such as aneurysm formation, rupture, and death. Morphologic features of aortic dissection determine not only treatment strategies in the acute phase (surgical vs. endovascular vs. medical), but also modulate the hemodynamics in the false lumen, ultimately responsible for late complications. Accurate description of the true and false lumen, any communications across the dissection membrane separating the two lumina, and blood supply from each lumen to aortic branch vessels is critical for risk prediction. Patient-specific surface representations are also a prerequisite for hemodynamic simulations, but currently require time-consuming manual segmentation of CT data. We present an aortic dissection cross-sectional model that captures the varying aortic anatomy, allowing for reliable measurements and creation of high-quality surface representations. In contrast to the traditional spline-based cross-sectional model, we employ elliptic Fourier descriptors, which allows users to control the accuracy of the cross-sectional contour of a flow channel. We demonstrate (i) how our approach can solve the requirements for generating surface and wall representations of the flow channels, (ii) how any number of communications between flow channels can be specified in a consistent manner, and (iii) how well branches connected to the respective flow channels are handled. Finally, we discuss how our approach is a step forward to an automated generation of surface models for aortic dissections from raw 3D imaging segmentation masks.
Texture Browser: Feature-based Texture Exploration
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Luo, Xuejiao; Scandolo, Leonardo; Eisemann, Elmar; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Texture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult. Contrary to most photo collections, for which object recognition has proven quite useful, syntactic descriptions of texture characteristics is not straightforward, and even creating appropriate metadata is a very difficult task. In this paper, we propose a system to help explore large unlabeled collections of texture images. The key insight is that spatially grouping textures sharing similar features can simplify navigation. Our system uses a pre-trained convolutional neural network to extract high-level semantic image features, which are then mapped to a 2-dimensional location using an adaptation of t-SNE, a dimensionality-reduction technique. We describe an interface to visualize and explore the resulting distribution and provide a series of enhanced navigation tools, our prioritized t-SNE, scalable clustering, and multi-resolution embedding, to further facilitate exploration and retrieval tasks. Finally, we also present the results of a user evaluation that demonstrates the effectiveness of our solution.
VICE: Visual Identification and Correction of Neural Circuit Errors
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Gonda, Felix; Wang, Xueying; Beyer, Johanna; Hadwiger, Markus; Lichtman, Jeff W.; Pfister, Hanspeter; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time-consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity-related errors. We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework's utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies.
Hornero: Thunderstorms Characterization using Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Diehl, Alexandra; Pelorosso, Rodrigo; Ruiz, Juan; Pajarola, Renato; Gröller, M. Eduard; Bruckner, Stefan; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Analyzing the evolution of thunderstorms is critical in determining the potential for the development of severe weather events. Existing visualization systems for short-term weather forecasting (nowcasting) allow for basic analysis and prediction of storm developments. However, they lack advanced visual features for efficient decision-making. We developed a visual analytics tool for the detection of hazardous thunderstorms and their characterization, using a visual design centered on a reformulated expert task workflow that includes visual features to overview storms and quickly identify high-impact weather events, a novel storm graph visualization to inspect and analyze the storm structure, as well as a set of interactive views for efficient identification of similar storm cells (known as analogs) in historical data and their use for nowcasting. Our tool was designed with and evaluated by meteorologists and expert forecasters working in short-term operational weather forecasting of severe weather events. Results show that our solution suits the forecasters' workflow. Our visual design is expressive, easy to use, and effective for prompt analysis and quick decision-making in the context of short-range operational weather forecasting.
Local Extraction of 3D Time-Dependent Vector Field Topology
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Hofmann, Lutz; Sadlo, Filip; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
We present an approach to local extraction of 3D time-dependent vector field topology. In this concept, Lagrangian coherent structures, which represent the separating manifolds in time-dependent transport, correspond to generalized streak manifolds seeded along hyperbolic path surfaces (HPSs). Instead of expensive and numerically challenging direct computation of the HPSs by intersection of ridges in the forward and backward finite-time Lyapunov exponent (FTLE) fields, our approach employs local extraction of respective candidates in the four-dimensional space-time domain. These candidates are subsequently refined toward the hyperbolic path surfaces, which provides unsteady equivalents of saddle-type critical points, periodic orbits, and bifurcation lines from steady, traditional vector field topology. In contrast to FTLE-based methods, we obtain an explicit geometric representation of the topological skeleton of the flow, which for steady flows coincides with the hyperbolic invariant manifolds of vector field topology. We evaluate our approach on analytical flows, as well as data from computational fluid dynamics, using the FTLE as a ground truth superset, i.e., we also show that FTLE ridges exhibit several types of false positives.
Visualizing Carotid Blood Flow Simulations for Stroke Prevention
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Eulzer, Pepe; Meuschke, Monique; Klingner, Carsten M.; Lawonn, Kai; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
In this work, we investigate how concepts from medical flow visualization can be applied to enhance stroke prevention diagnostics. Our focus lies on carotid stenoses, i.e., local narrowings of the major brain-supplying arteries, which are a frequent cause of stroke. Carotid surgery can reduce the stroke risk associated with stenoses, however, the procedure entails risks itself. Therefore, a thorough assessment of each case is necessary. In routine diagnostics, the morphology and hemodynamics of an afflicted vessel are separately analyzed using angiography and sonography, respectively. Blood flow simulations based on computational fluid dynamics could enable the visual integration of hemodynamic and morphological information and provide a higher resolution on relevant parameters. We identify and abstract the tasks involved in the assessment of stenoses and investigate how clinicians could derive relevant insights from carotid blood flow simulations. We adapt and refine a combination of techniques to facilitate this purpose, integrating spatiotemporal navigation, dimensional reduction, and contextual embedding. We evaluated and discussed our approach with an interdisciplinary group of medical practitioners, fluid simulation and flow visualization researchers. Our initial findings indicate that visualization techniques could promote usage of carotid blood flow simulations in practice.
Design Patterns and Trade-Offs in Responsive Visualization for Communication
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Kim, Hyeok; Moritz, Dominik; Hullman, Jessica; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Increased access to mobile devices motivates the need to design communicative visualizations that are responsive to varying screen sizes. However, relatively little design guidance or tooling is currently available to authors. We contribute a detailed characterization of responsive visualization strategies in communication-oriented visualizations, identifying 76 total strategies by analyzing 378 pairs of large screen (LS) and small screen (SS) visualizations from online articles and reports. Our analysis distinguishes between the Targets of responsive visualization, referring to what elements of a design are changed and Actions representing how targets are changed. We identify key trade-offs related to authors' need to maintain graphical density, referring to the amount of information per pixel, while also maintaining the ''message'' or intended takeaways for users of a visualization. We discuss implications of our findings for future visualization tool design to support responsive transformation of visualization designs, including requirements for automated recommenders for communication-oriented responsive visualizations.
Parameterized Splitting of Summed Volume Tables
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Reinbold, Christian; Westermann, Rüdiger; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Summed Volume Tables (SVTs) allow one to compute integrals over the data values in any cubical area of a three-dimensional orthogonal grid in constant time, and they are especially interesting for building spatial search structures for sparse volumes. However, SVTs become extremely memory consuming due to the large values they need to store; for a dataset of n values an SVT requires O(nlogn) bits. The 3D Fenwick tree allows recovering the integral values in O(log3 n) time, at a memory consumption ofO(n) bits.We propose an algorithm that generates SVT representations that can flexibly trade speed for memory: From similar characteristics as SVTs, over equal memory consumption as 3D Fenwick trees at significantly lower computational complexity, to even further reduced memory consumption at the cost of raising computational complexity. For a 641x9601x9601 binary dataset, the algorithm can generate an SVT representation that requires 27.0 GB and 46 . 8 data fetch operations to retrieve an integral value, compared to 27.5 GB and 1521 . 8 fetches by 3D Fenwick trees, a decrease in fetches of 97%. A full SVT requires 247.6GB and 8 fetches per integral value. We present a novel hierarchical approach to compute and store intermediate prefix sums of SVTs, so that any prescribed memory consumption between O(n) bits and O(nlogn) bits is achieved. We evaluate the performance of the proposed algorithm in a number of examples considering large volume data, and we perform comparisons to existing alternatives.
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Fischer, Maximilian T.; Seebacher, Daniel; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta-information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user-adaptable and problem-specific machine learning-based retrieval system. An interactive multi-level matrix-based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross-checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real-world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.
Exploring Multi-dimensional Data via Subset Embedding
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Xie, Peng; Tao, Wenyuan; Li, Jie; Huang, Wentao; Chen, Siming; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Multi-dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformlyformatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully-connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3-step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.
A Novel Approach for Exploring Annotated Data With Interactive Lenses
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Bettio, Fabio; Ahsan, Moonisa; Marton, Fabio; Gobbetti, Enrico; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
We introduce a novel approach for assisting users in exploring 2D data representations with an interactive lens. Focus-andcontext exploration is supported by translating user actions to the joint adjustments in camera and lens parameters that ensure a good placement and sizing of the lens within the view. This general approach, implemented using standard device mappings, overcomes the limitations of current solutions, which force users to continuously switch from lens positioning and scaling to view panning and zooming. Navigation is further assisted by exploiting data annotations. In addition to traditional visual markups and information links, we associate to each annotation a lens configuration that highlights the region of interest. During interaction, an assisting controller determines the next best lens in the database based on the current view and lens parameters and the navigation history. Then, the controller interactively guides the user's lens towards the selected target and displays its annotation markup. As only one annotation markup is displayed at a time, clutter is reduced. Moreover, in addition to guidance, the navigation can also be automated to create a tour through the data. While our methods are generally applicable to general 2D visualization, we have implemented them for the exploration of stratigraphic relightable models. The capabilities of our approach are demonstrated in cultural heritage use cases. A user study has been performed in order to validate our approach.
Automatic Improvement of Continuous Colormaps in Euclidean Colorspaces
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Nardini, Pascal; Chen, Min; Böttinger, Michael; Scheuermann, Gerik; Bujack, Roxana; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Colormapping is one of the simplest and most widely used data visualization methods within and outside the visualization community. Uniformity, order, discriminative power, and smoothness of continuous colormaps are the most important criteria for evaluating and potentially improving colormaps. We present a local and a global automatic optimization algorithm in Euclidean color spaces for each of these design rules in this work. As a foundation for our optimization algorithms, we used the CCC-Tool colormap specification (CMS); each algorithm has been implemented in this tool. In addition to synthetic examples that demonstrate each method's effect, we show the outcome of some of the methods applied to a typhoon simulation.
ClusterSets: Optimizing Planar Clusters in Categorical Point Data
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Geiger, Jakob; Cornelsen, Sabine; Haunert, Jan-Henrik; Kindermann, Philipp; Mchedlidze, Tamara; Nöllenburg, Martin; Okamoto, Yoshio; Wolff, Alexander; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
In geographic data analysis, one is often given point data of different categories (such as facilities of a university categorized by department). Drawing upon recent research on set visualization, we want to visualize category membership by connecting points of the same category with visual links. Existing approaches that follow this path usually insist on connecting all members of a category, which may lead to many crossings and visual clutter. We propose an approach that avoids crossings between connections of different categories completely. Instead of connecting all data points of the same category, we subdivide categories into smaller, local clusters where needed. We do a case study comparing the legibility of drawings produced by our approach and those by existing approaches. In our problem formulation, we are additionally given a graph G on the data points whose edges express some sort of proximity. Our aim is to find a subgraph G0 of G with the following properties: (i) edges connect only data points of the same category, (ii) no two edges cross, and (iii) the number of connected components (clusters) is minimized. We then visualize the clusters in G0. For arbitrary graphs, the resulting optimization problem, Cluster Minimization, is NP-hard (even to approximate). Therefore, we introduce two heuristics. We do an extensive benchmark test on real-world data. Comparisons with exact solutions indicate that our heuristics do astonishing well for certain relative-neighborhood graphs.
Thin-Volume Visualization on Curved Domains
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Herter, Felix; Hege, Hans-Christian; Hadwiger, Markus; Lepper, Verena; Baum, Daniel; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Thin, curved structures occur in many volumetric datasets. Their analysis using classical volume rendering is difficult because parts of such structures can bend away or hide behind occluding elements. This problem cannot be fully compensated by effective navigation alone, as structure-adapted navigation in the volume is cumbersome and only parts of the structure are visible in each view. We solve this problem by rendering a spatially transformed view of the volume so that an unobstructed visualization of the entire curved structure is obtained. As a result, simple and intuitive navigation becomes possible. The domain of the spatial transform is defined by a triangle mesh that is topologically equivalent to an open disc and that approximates the structure of interest. The rendering is based on ray-casting, in which the rays traverse the original volume. In order to carve out volumes of varying thicknesses, the lengths of the rays as well as the positions of the mesh vertices can be easily modified by interactive painting under view control. We describe a prototypical implementation and demonstrate the interactive visual inspection of complex structures from digital humanities, biology, medicine, and material sciences. The visual representation of the structure as a whole allows for easy inspection of interesting substructures in their original spatial context. Overall, we show that thin, curved structures in volumetric data can be excellently visualized using ray-casting-based volume rendering of transformed views defined by guiding surface meshes, supplemented by interactive, local modifications of ray lengths and vertex positions.
Animated Presentation of Static Infographics with InfoMotion
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Wang, Yun; Gao, Yi; Huang, Ray; Cui, Weiwei; Zhang, Haidong; Zhang, Dongmei; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
By displaying visual elements logically in temporal order, animated infographics can help readers better understand layers of information expressed in an infographic. While many techniques and tools target the quick generation of static infographics, few support animation designs. We propose InfoMotion that automatically generates animated presentations of static infographics. We first conduct a survey to explore the design space of animated infographics. Based on this survey, InfoMotion extracts graphical properties of an infographic to analyze the underlying information structures; then, animation effects are applied to the visual elements in the infographic in temporal order to present the infographic. The generated animations can be used in data videos or presentations. We demonstrate the utility of InfoMotion with two example applications, including mixed-initiative animation authoring and animation recommendation. To further understand the quality of the generated animations, we conduct a user study to gather subjective feedback on the animations generated by InfoMotion.
Visual Analysis of Electronic Densities and Transitions in Molecules
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Masood, Talha Bin; Thygesen, Signe Sidwall; Linares, Mathieu; Abrikosov, Alexei I.; Natarajan, Vijay; Hotz, Ingrid; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
The study of electronic transitions within a molecule connected to the absorption or emission of light is a common task in the process of the design of new materials. The transitions are complex quantum mechanical processes and a detailed analysis requires a breakdown of these processes into components that can be interpreted via characteristic chemical properties. We approach these tasks by providing a detailed analysis of the electron density field. This entails methods to quantify and visualize electron localization and transfer from molecular subgroups combining spatial and abstract representations. The core of our method uses geometric segmentation of the electronic density field coupled with a graph-theoretic formulation of charge transfer between molecular subgroups. The design of the methods has been guided by the goal of providing a generic and objective analysis following fundamental concepts. We illustrate the proposed approach using several case studies involving the study of electronic transitions in different molecular systems.
ParSetgnostics: Quality Metrics for Parallel Sets
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Dennig, Frederik L.; Fischer, Maximilian T.; Blumenschein, Michael; Fuchs, Johannes; Keim, Daniel A.; Dimara, Evanthia; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points - analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic order, the design of Parallel Sets is sensitive to visual clutter due to overlaps, crossings, and subdivision of ribbons hindering readability and pattern detection. In this paper, we propose a set of quality metrics, called ParSetgnostics (Parallel Sets diagnostics), which aim to improve Parallel Sets by reducing clutter. These quality metrics quantify important properties of Parallel Sets such as overlap, orthogonality, ribbon width variance, and mutual information to optimize the category and dimension ordering. By conducting a systematic correlation analysis between the individual metrics, we ensure their distinctiveness. Further, we evaluate the clutter reduction effect of ParSetgnostics by reconstructing six datasets from previous publications using Parallel Sets measuring and comparing their respective properties. Our results show that ParSetgostics facilitates multi-dimensional analysis of categorical data by automatically providing optimized Parallel Set designs with a clutter reduction of up to 81% compared to the originally proposed Parallel Sets visualizations.

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