39-Issue 3

Permanent URI for this collection

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

Browse

Now showing 1 - 20 of 49

Metro Maps on Octilinear Grid Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bast, Hannah; Brosi, Patrick; Storandt, Sabine; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Schematic transit maps (often called "metro maps" in the literature) are important to produce comprehensible visualizations of complex public transit networks. In this work, we investigate the problem of automatically drawing such maps on an octilinear grid with an arbitrary (but optimal) number of edge bends. Our approach can naturally deal with obstacles that should be respected in the final drawing (points of interest, rivers, coastlines) and can prefer grid edges near the real-world course of a line. This allows our drawings to be combined with existing maps, for example as overlays in map services. We formulate an integer linear program which can be used to solve the problem exactly. We also provide a fast approximation algorithm which greedily calculates shortest paths between node candidates on the underlying octilinear grid graph. Previous work used local search techniques to update node positions until a local optimum was found, but without guaranteeing octilinearity. We can thus calculate nearly optimal metro maps in a fraction of a second even for complex networks, enabling the interactive use of our method in map editors.
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.
Fuzzy Contour Trees: Alignment and Joint Layout of Multiple Contour Trees
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lohfink, Anna-Pia; Wetzels, Florian; Lukasczyk, Jonas; Weber, Gunther H.; Garth, Christoph; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
We describe a novel technique for the simultaneous visualization of multiple scalar fields, e.g. representing the members of an ensemble, based on their contour trees. Using tree alignments, a graph-theoretic concept similar to edit distance mappings, we identify commonalities across multiple contour trees and leverage these to obtain a layout that can represent all trees simultaneously in an easy-to-interpret, minimally-cluttered manner. We describe a heuristic algorithm to compute tree alignments for a given similarity metric, and give an algorithm to compute a joint layout of the resulting aligned contour trees. We apply our approach to the visualization of scalar field ensembles, discuss basic visualization and interaction possibilities, and demonstrate results on several analytic and real-world examples.
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.
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.
Quantitative Comparison of Time-Dependent Treemaps
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Vernier, Eduardo; Sondag, Max; Comba, João; Speckmann, Bettina; Telea, Alexandru; Verbeek, Kevin; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Rectangular treemaps are often the method of choice to visualize large hierarchical datasets. Nowadays such datasets are available over time, hence there is a need for (a) treemaps that can handle time-dependent data, and (b) corresponding quality criteria that cover both a treemap's visual quality and its stability over time. In recent years a wide variety of (stable) treemapping algorithms has been proposed, with various advantages and limitations. We aim to provide insights to researchers and practitioners to allow them to make an informed choice when selecting a treemapping algorithm for specific applications and data. To this end, we perform an extensive quantitative evaluation of rectangular treemaps for time-dependent data. As part of this evaluation we propose a novel classification scheme for time-dependent datasets. Specifically, we observe that the performance of treemapping algorithms depends on the characteristics of the datasets used. We identify four potential representative features that characterize time-dependent hierarchical datasets and classify all datasets used in our experiments accordingly. We experimentally test the validity of this classification on more than 2000 datasets, and analyze the relative performance of 14 state-of-the-art rectangular treemapping algorithms across varying features. Finally, we visually summarize our results with respect to both visual quality and stability to aid users in making an informed choice among treemapping algorithms. All datasets, metrics, and algorithms are openly available to facilitate reuse and further comparative studies.
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.
Short-Contact Touch-Manipulation of Scatterplot Matrices on Wall Displays
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Riehmann, Patrick; Molina León, Gabriela; Reibert, Joshua; Echtler, Florian; Froehlich, Bernd; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
This paper presents a short-contact multitouch vocabulary for interacting with scatterplot matrices (SPLOMs) on wall-sized displays. Fling-based gestures overcome central interaction challenges of such large displays by avoiding long swipes on the typically blunt surfaces, frequent physical navigation by walking for accessing screen areas beyond arm's reach in the horizontal direction and uncomfortable postures for accessing screen areas in the vertical direction. Furthermore, we make use of the display's high resolution and large size by supporting the efficient specification of two-tiered focus + context regions which are consistently propagated across the SPLOM. These techniques are complemented by axis-centered and lasso-based selection techniques for specifying subsets of the data. An expert review as well as a user study confirmed the potential and general usability of our seamlessly integrated multitouch interaction techniques for SPLOMs on large vertical displays.
CPU Ray Tracing of Tree-Based Adaptive Mesh Refinement Data
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wang, Feng; Marshak, Nathan; Usher, Will; Burstedde, Carsten; Knoll, Aaron; Heister, Timo; Johnson, Chris R.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Adaptive mesh refinement (AMR) techniques allow for representing a simulation's computation domain in an adaptive fashion. Although these techniques have found widespread adoption in high-performance computing simulations, visualizing their data output interactively and without cracks or artifacts remains challenging. In this paper, we present an efficient solution for direct volume rendering and hybrid implicit isosurface ray tracing of tree-based AMR (TB-AMR) data. We propose a novel reconstruction strategy, Generalized Trilinear Interpolation (GTI), to interpolate across AMR level boundaries without cracks or discontinuities in the surface normal. We employ a general sparse octree structure supporting a wide range of AMR data, and use it to accelerate volume rendering, hybrid implicit isosurface rendering and value queries. We demonstrate that our approach achieves artifact-free isosurface and volume rendering and provides higher quality output images compared to existing methods at interactive rendering rates.
Co-creating Visualizations: A First Evaluation with Social Science Researchers
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Molina León, Gabriela; Breiter, Andreas; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Co-creation is a design method where designers and domain experts work together to develop a product. In this paper, we present and evaluate the use of co-creation to design a visual information system with social science researchers in order to explore and analyze their data. Co-creation proposes involving the future users in the design process to ensure that they play a critical role in the design, and to increase the chances of long-term adoption. We evaluated the co-creation process through surveys, interviews and a user study. According to the participants' feedback, they felt listened to through co-creation, and considered the methodology helpful to develop visualizations that support their research in the near future. However, participation was far from perfect, particularly early career researchers showed limited interest in participating because they did not see the process as beneficial for their research publication goals. We summarize benefits and limitations of co-creation, together with our recommendations, as lessons learned.
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Jaunet, Theo; Vuillemot, Romain; Wolf, Christian; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explain-ability in the field of visual analytics.
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.
Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Vernier, Eduardo Faccin; Garcia, Rafael; Silva, Iron Prando da; Comba, João L. D.; Telea, Alexandru C.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.
Representative Isovalue Detection and Isosurface Segmentation Using Novel Isosurface Measures
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wang, Cuilan; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Interval volume is the volume of the region between two isosurfaces. This paper proposes a novel measure, called VOA measure, that is computed based on interval volume and isosurface area. This measure represents the rate of change of distance between isosurfaces with respect to isovalue. It can be used to detect representative isovalues of the dataset since two isosurfaces near material boundaries tend to be much closer to each other than two isosurfaces in material interiors, assuming they have the same isovalue difference. For the same isosurface, some portion of it may pass through the boundary of two materials and some portion of it may pass through the interior of a material. To separate the portions of an isosurface that represent different features of the dataset, another novel isosurface measure is introduced. This measure is calculated based on the Euclidean distance of individual sample points on two isosurfaces. The effectiveness of the two new measures in detecting significant isovalues and segmenting isosurfaces are demonstrated in the paper.
A Visual Analytics Approach to Facilitate Crime Hotspot Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Neto, José F. de Queiroz; Santos, Emanuele; Vidal, Creto Augusto; Ebert, David S.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Computer-based technology has played a significant role in crime prevention over the past 30 years, especially with the popularization of spatial databases and crime mapping systems. Police departments frequently use hotspot analysis to identify regions that should be a priority in receiving preventive resources. Practitioners and researchers agree that tracking crime over time and identifying its geographic patterns are vital information for planning efficiently. Frequently, police departments have access to systems that are too complicated and excessively technical, leading to modest usage. By working closely together with domain experts from police agencies of two different countries, we identified and characterized five domain tasks inherent to the hotspot analysis problem and developed SHOC, a visualization tool that strives for simplicity and ease of use in helping users to perform all the domain tasks. SHOC is included in a visual analytics system that allows users without technical expertise to annotate, save, and share analyses. We also demonstrate that our system effectively supports the completion of the domain tasks in two different real-world case studies.
MotionGlyphs: Visual Abstraction of Spatio-Temporal Networks in Collective Animal Behavior
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Cakmak, Eren; Schäfer, Hanna; Buchmüller, Juri; Fuchs, Johannes; Schreck, Tobias; Jordan, Alex; Keim, Daniel A.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Domain experts for collective animal behavior analyze relationships between single animal movers and groups of animals over time and space to detect emergent group properties. A common way to interpret this type of data is to visualize it as a spatio-temporal network. Collective behavior data sets are often large, and may hence result in dense and highly connected node-link diagrams, resulting in issues of node-overlap and edge clutter. In this design study, in an iterative design process, we developed glyphs as a design for seamlessly encoding relationships and movement characteristics of a single mover or clusters of movers. Based on these glyph designs, we developed a visual exploration prototype, MotionGlyphs, that supports domain experts in interactively filtering, clustering, and animating spatio-temporal networks for collective animal behavior analysis. By means of an expert evaluation, we show how MotionGlyphs supports important tasks and analysis goals of our domain experts, and we give evidence of the usefulness for analyzing spatio-temporal networks of collective animal behavior.
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.
Phase Space Projection of Dynamical Systems
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bartolovic, Nemanja; Gross, Markus; Günther, Tobias; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Dynamical systems are commonly used to describe the state of time-dependent systems. In many engineering and control problems, the state space is high-dimensional making it difficult to analyze and visualize the behavior of the system for varying input conditions. We present a novel dimensionality reduction technique that is tailored to high-dimensional dynamical systems. In contrast to standard general purpose dimensionality reduction algorithms, we use energy minimization to preserve properties of the flow in the high-dimensional space. Once the projection operator is optimized, further high-dimensional trajectories are projected easily. Our 3D projection maintains a number of useful flow properties, such as critical points and flow maps, and is optimized to match geometric characteristics of the high-dimensional input, as well as optional user constraints. We apply our method to trajectories traced in the phase spaces of second-order dynamical systems, including finite-sized objects in fluids, the circular restricted three-body problem and a damped double pendulum. We compare the projections with standard visualization techniques, such as PCA, t-SNE and UMAP, and visualize the dynamical systems with multiple coordinated views interactively, featuring a spatial embedding, projection to subspaces, our dimensionality reduction and a seed point exploration tool.
Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Aurisano, Jillian; Kumar, Abhinav; Alsaiari, Abeer; Eugenio, Barbara Di; Johnson, Andrew E.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
This paper describes results from an observational, exploratory study of visual data exploration in a large, multi-view, flexible canvas environment. Participants were provided with a set of data exploration sub-tasks associated with a local crime dataset and were instructed to pose questions to a remote mediator who would respond by generating and organizing visualizations on the large display. We observed that participants frequently posed requests to cast a net around one or several subsets of the data or a set of data attributes. They accomplished this directly and by utilizing existing views in unique ways, including by requesting to copy and pivot a group of views collectively and posing a set of parallel requests on target views expressed in one command. These observed actions depart from multi-view flexible canvas environments that typically provide interfaces in support of generating one view at a time or actions that operate on one view at a time. We describe how participants used these 'cast-a-net' requests for tasks that spanned more than one view and describe design considerations for multi-view environments that would support the observed multi-view generation actions.
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.

Browse

Browsing 39-Issue 3 by Issue Date

Results Per Page

Sort Options