Conference stringclasses 6 values | Year int64 1.99k 2.03k | Title stringlengths 8 187 | DOI stringlengths 16 32 | Abstract stringlengths 128 7.15k ⌀ | Accessible bool 2 classes | Early bool 2 classes | AuthorNames-Deduped listlengths 1 24 | Award listlengths 0 2 | Resources listlengths 0 5 | ResourceLinks listlengths 0 10 |
|---|---|---|---|---|---|---|---|---|---|---|
InfoVis | 2,020 | Chemicals in the Creek: designing a situated data physicalization of open government data with the community | 10.1109/TVCG.2020.3030472 | Over the last decade growing amounts of government data have been made available in an attempt to increase transparency and civic participation, but it is unclear if this data serves non-expert communities due to gaps in access and the technical knowledge needed to interpret this “open” data. We conducted a two-year design study focused on the creation of a community-based data display using the United States Environmental Protection Agency data on water permit violations by oil storage facilities on the Chelsea Creek in Massachusetts to explore whether situated data physicalization and Participatory Action Research could support meaningful engagement with open data. We selected this data as it is of interest to local groups and available online, yet remains largely invisible and inaccessible to the Chelsea community. The resulting installation, Chemicals in the Creek, responds to the call for community-engaged visualization processes and provides an application of situated methods of data representation. It proposes event-centered and power-aware modes of engagement using contextual and embodied data representations. The design of Chemicals in the Creek is grounded in interactive workshops and we analyze it through event observation, interviews, and community outcomes. We reflect on the role of community engaged research in the Information Visualization community relative to recent conversations on new approaches to design studies and evaluation. | false | false | [
"Laura J. Perovich",
"Sara Ann Wylie",
"Roseann Bongiovanni"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.06155v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Communicative Visualizations as a Learning Problem | 10.1109/TVCG.2020.3030375 | Significant research has provided robust task and evaluation languages for the analysis of exploratory visualizations. Unfortunately, these taxonomies fail when applied to communicative visualizations. Instead, designers often resort to evaluating communicative visualizations from the cognitive efficiency perspective: “can the recipient accurately decode my message/insight?” However, designers are unlikely to be satisfied if the message went ‘in one ear and out the other.’ The consequence of this inconsistency is that it is difficult to design or select between competing options in a principled way. The problem we address is the fundamental mismatch between how designers want to describe their intent, and the language they have. We argue that visualization designers can address this limitation through a learning lens: that the recipient is a student and the designer a teacher. By using learning objectives, designers can better define, assess, and compare communicative visualizations. We illustrate how the learning-based approach provides a framework for understanding a wide array of communicative goals. To understand how the framework can be applied (and its limitations), we surveyed and interviewed members of the Data Visualization Society using their own visualizations as a probe. Through this study we identified the broad range of objectives in communicative visualizations and the prevalence of certain objective types. | false | false | [
"Eytan Adar",
"Elsie Lee-Robbins"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.07095v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Comparative Layouts Revisited: Design Space, Guidelines, and Future Directions | 10.1109/TVCG.2020.3030419 | We present a systematic review on three comparative layouts-juxtaposition, superposition, and explicit-encoding-which are information visualization (InfoVis) layouts designed to support comparison tasks. For the last decade, these layouts have served as fundamental idioms in designing many visualization systems. However, we found that the layouts have been used with inconsistent terms and confusion, and the lessons from previous studies are fragmented. The goal of our research is to distill the results from previous studies into a consistent and reusable framework. We review 127 research papers, including 15 papers with quantitative user studies, which employed comparative layouts. We first alleviate the ambiguous boundaries in the design space of comparative layouts by suggesting lucid terminology (e.g., chart-wise and item-wise juxtaposition). We then identify the diverse aspects of comparative layouts, such as the advantages and concerns of using each layout in the real-world scenarios and researchers' approaches to overcome the concerns. Building our knowledge on top of the initial insights gained from the Gleicher et al.'s survey [19], we elaborate on relevant empirical evidence that we distilled from our survey (e.g., the actual effectiveness of the layouts in different study settings) and identify novel facets that the original work did not cover (e.g., the familiarity of the layouts to people). Finally, we show the consistent and contradictory results on the performance of comparative layouts and offer practical implications for using the layouts by suggesting trade-offs and seven actionable guidelines. | false | false | [
"Sehi L'Yi",
"Jaemin Jo",
"Jinwook Seo"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.00192v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/nFY0g_pqGUg",
"icon": "video"
}
] |
InfoVis | 2,020 | Composition and Configuration Patterns in Multiple-View Visualizations | 10.1109/TVCG.2020.3030338 | Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there; and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design. | false | false | [
"Xi Chen",
"Wei Zeng 0004",
"Yanna Lin",
"Hayder Al-Maneea",
"Jonathan Roberts 0002",
"Remco Chang"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2007.15407v2",
"icon": "paper"
}
] |
InfoVis | 2,020 | Context-aware Sampling of Large Networks via Graph Representation Learning | 10.1109/TVCG.2020.3030440 | Numerous sampling strategies have been proposed to simplify large-scale networks for highly readable visualizations. It is of great challenge to preserve contextual structures formed by nodes and edges with tight relationships in a sampled graph, because they are easily overlooked during the process of sampling due to their irregular distribution and immunity to scale. In this paper, a new graph sampling method is proposed oriented to the preservation of contextual structures. We first utilize a graph representation learning (GRL) model to transform nodes into vectors so that the contextual structures in a network can be effectively extracted and organized. Then, we propose a multi-objective blue noise sampling model to select a subset of nodes in the vectorized space to preserve contextual structures with the retention of relative data and cluster densities in addition to those features of significance, such as bridging nodes and graph connections. We also design a set of visual interfaces enabling users to interactively conduct context-aware sampling, visually compare results with various sampling strategies, and deeply explore large networks. Case studies and quantitative comparisons based on real-world datasets have demonstrated the effectiveness of our method in the abstraction and exploration of large networks. | false | false | [
"Zhiguang Zhou",
"Chen Shi",
"Xilong Shen",
"Lihong Cai",
"Haoxuan Wang",
"Yuhua Liu",
"Ying Zhao 0001",
"Wei Chen 0001"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/ZvQ5-LcZV7w",
"icon": "video"
}
] |
InfoVis | 2,020 | Data Comics for Reporting Controlled User Studies in Human-Computer Interaction | 10.1109/TVCG.2020.3030433 | Inspired by data comics, this paper introduces a novel format for reporting controlled studies in the domain of human-computer interaction (HCI). While many studies in HCI follow similar steps in explaining hypotheses, laying out a study design, and reporting results, many of these decisions are buried in blocks of dense scientific text. We propose leveraging data comics as study reports to provide an open and glanceable view of studies by tightly integrating text and images, illustrating design decisions and key insights visually, resulting in visual narratives that can be compelling to non-scientists and researchers alike. Use cases of data comics study reports range from illustrations for non-scientific audiences to graphical abstracts, study summaries, technical talks, textbooks, teaching, blogs, supplementary submission material, and inclusion in scientific articles. This paper provides examples of data comics study reports alongside a graphical repertoire of examples, embedded in a framework of guidelines for creating comics reports which was iterated upon and evaluated through a series of collaborative design sessions. | false | false | [
"Zezhong Wang 0001",
"Jacob Ritchie",
"Jingtao Zhou",
"Fanny Chevalier",
"Benjamin Bach"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "https://osf.io/unmyj",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/rscjCYCdEzk",
"icon": "video"
}
] |
InfoVis | 2,020 | Data Visceralization: Enabling Deeper Understanding of Data Using Virtual Reality | 10.1109/TVCG.2020.3030435 | A fundamental part of data visualization is transforming data to map abstract information onto visual attributes. While this abstraction is a powerful basis for data visualization, the connection between the representation and the original underlying data (i.e., what the quantities and measurements actually correspond with in reality) can be lost. On the other hand, virtual reality (VR) is being increasingly used to represent real and abstract models as natural experiences to users. In this work, we explore the potential of using VR to help restore the basic understanding of units and measures that are often abstracted away in data visualization in an approach we call data visceralization. By building VR prototypes as design probes, we identify key themes and factors for data visceralization. We do this first through a critical reflection by the authors, then by involving external participants. We find that data visceralization is an engaging way of understanding the qualitative aspects of physical measures and their real-life form, which complements analytical and quantitative understanding commonly gained from data visualization. However, data visceralization is most effective when there is a one-to-one mapping between data and representation, with transformations such as scaling affecting this understanding. We conclude with a discussion of future directions for data visceralization. | false | false | [
"Benjamin Lee",
"Dave Brown",
"Bongshin Lee",
"Christophe Hurter",
"Steven Mark Drucker",
"Tim Dwyer"
] | [
"HM"
] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.00059v2",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/C3q5XhdanXk",
"icon": "video"
}
] |
InfoVis | 2,020 | Designing Narrative-Focused Role-Playing Games for Visualization Literacy in Young Children | 10.1109/TVCG.2020.3030464 | Building on game design and education research, this paper introduces narrative-focused role-playing games as a way to promote visualization literacy in young children. Visualization literacy skills are vital in understanding the world around us and constructing meaningful visualizations, yet, how to better develop these skills at an early age remains largely overlooked and understudied. Only recently has the visualization community started to fill this gap, resulting in preliminary studies and development of educational tools for use in early education. We add to these efforts through the exploration of gamification to support learning, and identify an opportunity to apply role-playing game-based designs by leveraging the presence of narratives in data-related problems involving visualizations. We study the effects of including narrative elements on learning through a technology probe, grounded in a set of design considerations stemming from visualization, game design and education science. We create two versions of a game - one with narrative elements and one without - and evaluate our instances on 33 child participants between 11- to 13-years old using a between-subjects study design. Despite participants requiring double the amount of time to complete their game due to additional narrative elements, the inclusion of such elements were found to improve engagement without sacrificing learning; our results indicate no significant differences in development of graph-reading skills, but significant differences in engagement and overall enjoyment of the game. We report observations and qualitative feedback collected, and note areas for improvement and room for future work. | false | false | [
"Elaine Huynh",
"Angela Nyhout",
"Patricia Ganea",
"Fanny Chevalier"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.13749v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/VWl3KkLSmuQ",
"icon": "video"
}
] |
InfoVis | 2,020 | DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction | 10.1109/TVCG.2020.3030447 | Efficient layout of large-scale graphs remains a challenging problem: the force-directed and dimensionality reduction-based methods suffer from high overhead for graph distance and gradient computation. In this paper, we present a new graph layout algorithm, called DRGraph, that enhances the nonlinear dimensionality reduction process with three schemes: approximating graph distances by means of a sparse distance matrix, estimating the gradient by using the negative sampling technique, and accelerating the optimization process through a multi-level layout scheme. DRGraph achieves a linear complexity for the computation and memory consumption, and scales up to large-scale graphs with millions of nodes. Experimental results and comparisons with state-of-the-art graph layout methods demonstrate that DRGraph can generate visually comparable layouts with a faster running time and a lower memory requirement. | false | false | [
"Min-Feng Zhu",
"Wei Chen 0001",
"Yuanzhe Hu",
"Yuxuan Hou",
"Liangjun Liu",
"Kaiyuan Zhang 0002"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.07799v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Embodied Navigation in Immersive Abstract Data Visualization: Is Overview+Detail or Zooming Better for 3D Scatterplots? | 10.1109/TVCG.2020.3030427 | Abstract data has no natural scale and so interactive data visualizations must provide techniques to allow the user to choose their viewpoint and scale. Such techniques are well established in desktop visualization tools. The two most common techniques are zoom+pan and overview+detail. However, how best to enable the analyst to navigate and view abstract data at different levels of scale in immersive environments has not previously been studied. We report the findings of the first systematic study of immersive navigation techniques for 3D scatterplots. We tested four conditions that represent our best attempt to adapt standard 2D navigation techniques to data visualization in an immersive environment while still providing standard immersive navigation techniques through physical movement and teleportation. We compared room-sized visualization versus a zooming interface, each with and without an overview. We find significant differences in participants' response times and accuracy for a number of standard visual analysis tasks. Both zoom and overview provide benefits over standard locomotion support alone (i.e., physical movement and pointer teleportation). However, which variation is superior, depends on the task. We obtain a more nuanced understanding of the results by analyzing them in terms of a time-cost model for the different components of navigation: way-finding, travel, number of travel steps, and context switching. | false | false | [
"Yalong Yang 0001",
"Maxime Cordeil",
"Johanna Beyer",
"Tim Dwyer",
"Kim Marriott",
"Hanspeter Pfister"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.09941v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/LnC0YWbz7SI",
"icon": "video"
}
] |
InfoVis | 2,020 | Exemplar-based Layout Fine-tuning for Node-link Diagrams | 10.1109/TVCG.2020.3030393 | We design and evaluate a novel layout fine-tuning technique for node-link diagrams that facilitates exemplar-based adjustment of a group of substructures in batching mode. The key idea is to transfer user modifications on a local substructure to other substructures in the entire graph that are topologically similar to the exemplar. We first precompute a canonical representation for each substructure with node embedding techniques and then use it for on-the-fly substructure retrieval. We design and develop a light-weight interactive system to enable intuitive adjustment, modification transfer, and visual graph exploration. We also report some results of quantitative comparisons, three case studies, and a within-participant user study. | false | false | [
"Jiacheng Pan",
"Wei Chen 0001",
"Xiaodong Zhao",
"Shuyue Zhou",
"Wei Zeng 0004",
"Min-Feng Zhu",
"Jian Chen 0006",
"Siwei Fu",
"Yingcai Wu"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/FdYxjdApGAM",
"icon": "video"
}
] |
InfoVis | 2,020 | Gemini: A Grammar and Recommender System for Animated Transitions in Statistical Graphics | 10.1109/TVCG.2020.3030360 | Animated transitions help viewers follow changes between related visualizations. Specifying effective animations demands significant effort: authors must select the elements and properties to animate, provide transition parameters, and coordinate the timing of stages. To facilitate this process, we present Gemini, a declarative grammar and recommendation system for animated transitions between single-view statistical graphics. Gemini specifications define transition “steps” in terms of high-level visual components (marks, axes, legends) and composition rules to synchronize and concatenate steps. With this grammar, Gemini can recommend animation designs to augment and accelerate designers' work. Gemini enumerates staged animation designs for given start and end states, and ranks those designs using a cost function informed by prior perceptual studies. To evaluate Gemini, we conduct both a formative study on Mechanical Turk to assess and tune our ranking function, and a summative study in which 8 experienced visualization developers implement animations in D3 that we then compare to Gemini's suggestions. We find that most designs (9/11) are exactly replicable in Gemini, with many (8/11) achievable via edits to suggestions, and that Gemini suggestions avoid multiple participant errors. | false | false | [
"Younghoon Kim",
"Jeffrey Heer"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.01429v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Guidelines For Pursuing and Revealing Data Abstractions | 10.1109/TVCG.2020.3030355 | Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration. | false | false | [
"Alex Bigelow",
"Katy Williams",
"Katherine E. Isaacs"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2005.04058v2",
"icon": "paper"
}
] |
InfoVis | 2,020 | Humane Visual AI: Telling the Stories Behind a Medical Condition | 10.1109/TVCG.2020.3030391 | A biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations. | false | false | [
"Wonyoung So",
"Edyta Paulina Bogucka",
"Sanja Scepanovic",
"Sagar Joglekar 0001",
"Ke Zhou 0003",
"Daniele Quercia"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2010.06296v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Implicit Multidimensional Projection of Local Subspaces | 10.1109/TVCG.2020.3030368 | We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases. | false | false | [
"Rongzheng Bian",
"Yumeng Xue",
"Liang Zhou 0001",
"Jian Zhang 0070",
"Baoquan Chen",
"Daniel Weiskopf",
"Yunhai Wang"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.03259v2",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/QZsMyOMHJmY",
"icon": "video"
}
] |
InfoVis | 2,020 | Insights From Experiments With Rigor in an EvoBio Design Study | 10.1109/TVCG.2020.3030405 | Design study is an established approach of conducting problem-driven visualization research. The academic visualization community has produced a large body of work for reporting on design studies, informed by a handful of theoretical frameworks, and applied to a broad range of application areas. The result is an abundance of reported insights into visualization design, with an emphasis on novel visualization techniques and systems as the primary contribution of these studies. In recent work we proposed a new, interpretivist perspective on design study and six companion criteria for rigor that highlight the opportunities for researchers to contribute knowledge that extends beyond visualization idioms and software. In this work we conducted a year-long collaboration with evolutionary biologists to develop an interactive tool for visual exploration of multivariate datasets and phylogenetic trees. During this design study we experimented with methods to support three of the rigor criteria: ABUNDANT, REFLEXIVE, and TRANSPARENT. As a result we contribute two novel visualization techniques for the analysis of multivariate phylogenetic datasets, three methodological recommendations for conducting design studies drawn from reflections over our process of experimentation, and two writing devices for reporting interpretivist design study. We offer this work as an example for implementing the rigor criteria to produce a diverse range of knowledge contributions. | false | false | [
"Jennifer Rogers",
"Austin H. Patton",
"Luke Harmon",
"Alexander Lex",
"Miriah D. Meyer"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.11564v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Introducing Layers of Meaning (LoM): A Framework to Reduce Semantic Distance of Visualization In Humanistic Research | 10.1109/TVCG.2020.3030426 | Information visualization (infovis) is a powerful tool for exploring rich datasets. Within humanistic research, rich qualitative data and domain culture make traditional infovis approaches appear reductive and disconnected, leading to low adoption. In this paper, we use a multi-step approach to scrutinize the relationship between infovis and the humanities and suggest new directions for it. We first look into infovis from the humanistic perspective by exploring the humanistic literature around infovis. We validate and expand those findings though a co-design workshop with humanist and infovis experts. Then, we translate our findings into guidelines for designers and conduct a design critique exercise to explore their effect on the perception of humanist researchers. Based on these steps, we introduce Layers of Meaning, a framework to reduce the semantic distance between humanist researchers and visualizations of their research material, by grounding infovis tools in time and space, physicality, terminology, nuance, and provenance. | false | false | [
"Houda Lamqaddam",
"Andrew Vande Moere",
"Vero Vanden Abeele",
"Koenraad Brosens",
"Katrien Verbert"
] | [] | [] | [] |
InfoVis | 2,020 | Investigating Visual Analysis of Differentially Private Data | 10.1109/TVCG.2020.3030369 | Differential Privacy is an emerging privacy model with increasing popularity in many domains. It functions by adding carefully calibrated noise to data that blurs information about individuals while preserving overall statistics about the population. Theoretically, it is possible to produce robust privacy-preserving visualizations by plotting differentially private data. However, noise-induced data perturbations can alter visual patterns and impact the utility of a private visualization. We still know little about the challenges and opportunities for visual data exploration and analysis using private visualizations. As a first step towards filling this gap, we conducted a crowdsourced experiment, measuring participants' performance under three levels of privacy (high, low, non-private) for combinations of eight analysis tasks and four visualization types (bar chart, pie chart, line chart, scatter plot). Our findings show that for participants' accuracy for summary tasks (e.g., find clusters in data) was higher that value tasks (e.g., retrieve a certain value). We also found that under DP, pie chart and line chart offer similar or better accuracy than bar chart. In this work, we contribute the results of our empirical study, investigating the task-based effectiveness of basic private visualizations, a dichotomous model for defining and measuring user success in performing visual analysis tasks under DP, and a set of distribution metrics for tuning the injection to improve the utility of private visualizations. | false | false | [
"Dan Zhang",
"Ali Sarvghad",
"Gerome Miklau"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/bI2dRiKFxck",
"icon": "video"
}
] |
InfoVis | 2,020 | Kyrix-S: Authoring Scalable Scatterplot Visualizations of Big Data | 10.1109/TVCG.2020.3030372 | Static scatterplots often suffer from the overdraw problem on big datasets where object overlap causes undesirable visual clutter. The use of zooming in scatterplots can help alleviate this problem. With multiple zoom levels, more screen real estate is available, allowing objects to be placed in a less crowded way. We call this type of visualization scalable scatterplot visualizations, or SSV for short. Despite the potential of SSVs, existing systems and toolkits fall short in supporting the authoring of SSVs due to three limitations. First, many systems have limited scalability, assuming that data fits in the memory of one computer. Second, too much developer work, e.g., using custom code to generate mark layouts or render objects, is required. Third, many systems focus on only a small subset of the SSV design space (e.g. supporting a specific type of visual marks). To address these limitations, we have developed Kyrix-S, a system for easy authoring of SSVs at scale. Kyrix-S derives a declarative grammar that enables specification of a variety of SSVs in a few tens of lines of code, based on an existing survey of scatterplot tasks and designs. The declarative grammar is supported by a distributed layout algorithm which automatically places visual marks onto zoom levels. We store data in a multi-node database and use multi-node spatial indexes to achieve interactive browsing of large SSVs. Extensive experiments show that 1) Kyrix-S enables interactive browsing of SSVs of billions of objects, with response times under 500ms and 2) Kyrix-S achieves 4X-9X reduction in specification compared to a state-of-the-art authoring system. | false | false | [
"Wenbo Tao",
"Xinli Hou",
"Adam Sah",
"Leilani Battle",
"Remco Chang",
"Michael Stonebraker"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2007.15904v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/LF6MYsdWn9c",
"icon": "video"
}
] |
InfoVis | 2,020 | Lyra 2: Designing Interactive Visualizations by Demonstration | 10.1109/TVCG.2020.3030367 | Recent graphical interfaces offer direct manipulation mechanisms for authoring visualizations, but are largely restricted to static output. To author interactive visualizations, users must instead turn to textual specification, but such approaches impose a higher technical burden. To bridge this gap, we introduce Lyra 2, a system that extends a prior visualization design environment with novel methods for authoring interaction techniques by demonstration. Users perform an interaction (e.g., button clicks, drags, or key presses) directly on the visualization they are editing. The system interprets this performance using a set of heuristics and enumerates suggestions of possible interaction designs. These heuristics account for the properties of the interaction (e.g., target and event type) as well as the visualization (e.g., mark and scale types, and multiple views). Interaction design suggestions are displayed as thumbnails; users can preview and test these suggestions, iteratively refine them through additional demonstrations, and finally apply and customize them via property inspectors. We evaluate our approach through a gallery of diverse examples, and evaluate its usability through a first-use study and via an analysis of its cognitive dimensions. We find that, in Lyra 2, interaction design by demonstration enables users to rapidly express a wide range of interactive visualizations. | false | false | [
"Jonathan Zong",
"Dhiraj Barnwal",
"Rupayan Neogy",
"Arvind Satyanarayan"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.09576v2",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/S7IAGCM33Fw",
"icon": "video"
}
] |
InfoVis | 2,020 | MetroSets: Visualizing Sets as Metro Maps | 10.1109/TVCG.2020.3030475 | We propose MetroSets, a new, flexible online tool for visualizing set systems using the metro map metaphor. We model a given set system as a hypergraph $\mathcal{H}=(V,\ \mathcal{S})$, consisting of a set $V$ of vertices and a set $\mathcal{S}$, which contains subsets of $V$ called hyperedges. Our system then computes a metro map representation of $\mathcal{H}$, where each hyperedge $E$ in $\mathcal{S}$ corresponds to a metro line and each vertex corresponds to a metro station. Vertices that appear in two or more hyperedges are drawn as interchanges in the metro map, connecting the different sets. MetroSets is based on a modular 4-step pipeline which constructs and optimizes a path-based hypergraph support, which is then drawn and schematized using metro map layout algorithms. We propose and implement multiple algorithms for each step of the MetroSet pipeline and provide a functional prototype with easy-to-use preset configurations. Furthermore, using several real-world datasets, we perform an extensive quantitative evaluation of the impact of different pipeline stages on desirable properties of the generated maps, such as octolinearity, monotonicity, and edge uniformity. | false | false | [
"Ben Jacobsen",
"Markus Wallinger",
"Stephen G. Kobourov",
"Martin Nöllenburg"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.09367v2",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/FN-xYlByE64",
"icon": "video"
}
] |
InfoVis | 2,020 | MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework | 10.1109/TVCG.2020.3030423 | We contribute MobileVisFixer, a new method to make visualizations more mobile-friendly. Although mobile devices have become the primary means of accessing information on the web, many existing visualizations are not optimized for small screens and can lead to a frustrating user experience. Currently, practitioners and researchers have to engage in a tedious and time-consuming process to ensure that their designs scale to screens of different sizes, and existing toolkits and libraries provide little support in diagnosing and repairing issues. To address this challenge, MobileVisFixer automates a mobile-friendly visualization re-design process with a novel reinforcement learning framework. To inform the design of MobileVisFixer, we first collected and analyzed SVG-based visualizations on the web, and identified five common mobile-friendly issues. MobileVisFixer addresses four of these issues on single-view Cartesian visualizations with linear or discrete scales by a Markov Decision Process model that is both generalizable across various visualizations and fully explainable. MobileVisFixer deconstructs charts into declarative formats, and uses a greedy heuristic based on Policy Gradient methods to find solutions to this difficult, multi-criteria optimization problem in reasonable time. In addition, MobileVisFixer can be easily extended with the incorporation of optimization algorithms for data visualizations. Quantitative evaluation on two real-world datasets demonstrates the effectiveness and generalizability of our method. | false | false | [
"Aoyu Wu",
"Wai Tong",
"Tim Dwyer",
"Bongshin Lee",
"Petra Isenberg",
"Huamin Qu"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.06678v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology | 10.1109/TVCG.2020.3030365 | Scatterplots are used for a variety of visual analytics tasks, including cluster identification, and the visual encodings used on a scatterplot play a deciding role on the level of visual separation of clusters. For visualization designers, optimizing the visual encodings is crucial to maximizing the clarity of data. This requires accurately modeling human perception of cluster separation, which remains challenging. We present a multi-stage user study focusing on four factors-distribution size of clusters, number of points, size of points, and opacity of points-that influence cluster identification in scatterplots. From these parameters, we have constructed two models, a distance-based model, and a density-based model, using the merge tree data structure from Topological Data Analysis. Our analysis demonstrates that these factors play an important role in the number of clusters perceived, and it verifies that the distance-based and density-based models can reasonably estimate the number of clusters a user observes. Finally, we demonstrate how these models can be used to optimize visual encodings on real-world data. | false | false | [
"Ghulam Jilani Quadri",
"Paul Rosen 0001"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2007.13872v2",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/hJ1P6fbtwsU",
"icon": "video"
}
] |
InfoVis | 2,020 | Multi-Perspective, Simultaneous Embedding | 10.1109/TVCG.2020.3030373 | We describe MPSE: a Multi-Perspective Simultaneous Embedding method for visualizing high-dimensional data, based on multiple pairwise distances between the data points. Specifically, MPSE computes positions for the points in 3D and provides different views into the data by means of 2D projections (planes) that preserve each of the given distance matrices. We consider two versions of the problem: fixed projections and variable projections. MPSE with fixed projections takes as input a set of pairwise distance matrices defined on the data points, along with the same number of projections and embeds the points in 3D so that the pairwise distances are preserved in the given projections. MPSE with variable projections takes as input a set of pairwise distance matrices and embeds the points in 3D while also computing the appropriate projections that preserve the pairwise distances. The proposed approach can be useful in multiple scenarios: from creating simultaneous embedding of multiple graphs on the same set of vertices, to reconstructing a 3D object from multiple 2D snapshots, to analyzing data from multiple points of view. We provide a functional prototype of MPSE that is based on an adaptive and stochastic generalization of multi-dimensional scaling to multiple distances and multiple variable projections. We provide an extensive quantitative evaluation with datasets of different sizes and using different number of projections, as well as several examples that illustrate the quality of the resulting solutions. | false | false | [
"Md. Iqbal Hossain",
"Vahan Huroyan",
"Stephen G. Kobourov",
"Raymundo Navarrete"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/a6JWYd2rXwI",
"icon": "video"
}
] |
InfoVis | 2,020 | NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries | 10.1109/TVCG.2020.3030378 | Natural language interfaces (NLls) have shown great promise for visual data analysis, allowing people to flexibly specify and interact with visualizations. However, developing visualization NLIs remains a challenging task, requiring low-level implementation of natural language processing (NLP) techniques as well as knowledge of visual analytic tasks and visualization design. We present NL4DV, a toolkit for natural language-driven data visualization. NL4DV is a Python package that takes as input a tabular dataset and a natural language query about that dataset. In response, the toolkit returns an analytic specification modeled as a JSON object containing data attributes, analytic tasks, and a list of Vega-Lite specifications relevant to the input query. In doing so, NL4DV aids visualization developers who may not have a background in NLP, enabling them to create new visualization NLIs or incorporate natural language input within their existing systems. We demonstrate NL4DV's usage and capabilities through four examples: 1) rendering visualizations using natural language in a Jupyter notebook, 2) developing a NLI to specify and edit Vega-Lite charts, 3) recreating data ambiguity widgets from the DataTone system, and 4) incorporating speech input to create a multimodal visualization system. | false | false | [
"Arpit Narechania",
"Arjun Srinivasan",
"John T. Stasko"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.10723v3",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/mHsvQLw7xpQ",
"icon": "video"
}
] |
InfoVis | 2,020 | No mark is an island: Precision and category repulsion biases in data reproductions | 10.1109/TVCG.2020.3030345 | Data visualization is powerful in large part because it facilitates visual extraction of values. Yet, existing measures of perceptual precision for data channels (e.g., position, length, orientation, etc.) are based largely on verbal reports of ratio judgments between two values (e.g., [7]). Verbal report conflates multiple sources of error beyond actual visual precision, introducing a ratio computation between these values and a requirement to translate that ratio to a verbal number. Here we observe raw measures of precision by eliminating both ratio computations and verbal reports; we simply ask participants to reproduce marks (a single bar or dot) to match a previously seen one. We manipulated whether the mark was initially presented (and later drawn) alone, paired with a reference (e.g. a second ‘100%’ bar also present at test, or a y-axis for the dot), or integrated with the reference (merging that reference bar into a stacked bar graph, or placing the dot directly on the axis). Reproductions of smaller values were overestimated, and larger values were underestimated, suggesting systematic memory biases. Average reproduction error was around 10% of the actual value, regardless of whether the reproduction was done on a common baseline with the original. In the reference and (especially) the integrated conditions, responses were repulsed from an implicit midpoint of the reference mark, such that values above 50% were overestimated, and values below 50% were underestimated. This reproduction paradigm may serve within a new suite of more fundamental measures of the precision of graphical perception. | false | false | [
"Caitlyn M. McColeman",
"Lane Harrison",
"Mi Feng",
"Steven Franconeri"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/OItjd5sPxBg",
"icon": "video"
}
] |
InfoVis | 2,020 | Palettailor: Discriminable Colorization for Categorical Data | 10.1109/TVCG.2020.3030406 | We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process. | false | false | [
"Kecheng Lu",
"Mi Feng",
"Xin Chen",
"Michael Sedlmair",
"Oliver Deussen",
"Dani Lischinski",
"Zhanglin Cheng",
"Yunhai Wang"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.02969v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/L_8mE_xTf1c",
"icon": "video"
}
] |
InfoVis | 2,020 | Personal Augmented Reality for Information Visualization on Large Interactive Displays | 10.1109/TVCG.2020.3030460 | In this work we propose the combination of large interactive displays with personal head-mounted Augmented Reality (AR) for information visualization to facilitate data exploration and analysis. Even though large displays provide more display space, they are challenging with regard to perception, effective multi-user support, and managing data density and complexity. To address these issues and illustrate our proposed setup, we contribute an extensive design space comprising first, the spatial alignment of display, visualizations, and objects in AR space. Next, we discuss which parts of a visualization can be augmented. Finally, we analyze how AR can be used to display personal views in order to show additional information and to minimize the mutual disturbance of data analysts. Based on this conceptual foundation, we present a number of exemplary techniques for extending visualizations with AR and discuss their relation to our design space. We further describe how these techniques address typical visualization problems that we have identified during our literature research. To examine our concepts, we introduce a generic AR visualization framework as well as a prototype implementing several example techniques. In order to demonstrate their potential, we further present a use case walkthrough in which we analyze a movie data set. From these experiences, we conclude that the contributed techniques can be useful in exploring and understanding multivariate data. We are convinced that the extension of large displays with AR for information visualization has a great potential for data analysis and sense-making. | false | false | [
"Patrick Reipschläger",
"Tamara Flemisch",
"Raimund Dachselt"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.03237v3",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/xVZ0h5fSBqg",
"icon": "video"
}
] |
InfoVis | 2,020 | PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning | 10.1109/TVCG.2020.3030467 | Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases. | false | false | [
"Tan Tang",
"Renzhong Li",
"Xinke Wu",
"Shuhan Liu",
"Johannes Knittel",
"Steffen Koch 0001",
"Lingyun Yu 0001",
"Peiran Ren",
"Thomas Ertl",
"Yingcai Wu"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.00249v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | QualDash: Adaptable Generation of Visualisation Dashboards for Healthcare Quality Improvement | 10.1109/TVCG.2020.3030424 | Adapting dashboard design to different contexts of use is an open question in visualisation research. Dashboard designers often seek to strike a balance between dashboard adaptability and ease-of-use, and in hospitals challenges arise from the vast diversity of key metrics, data models and users involved at different organizational levels. In this design study, we present QualDash, a dashboard generation engine that allows for the dynamic configuration and deployment of visualisation dashboards for healthcare quality improvement (QI). We present a rigorous task analysis based on interviews with healthcare professionals, a co-design workshop and a series of one-on-one meetings with front line analysts. From these activities we define a metric card metaphor as a unit of visual analysis in healthcare QI, using this concept as a building block for generating highly adaptable dashboards, and leading to the design of a Metric Specification Structure (MSS). Each MSS is a JSON structure which enables dashboard authors to concisely configure unit-specific variants of a metric card, while offloading common patterns that are shared across cards to be preset by the engine. We reflect on deploying and iterating the design of OualDash in cardiology wards and pediatric intensive care units of five NHS hospitals. Finally, we report evaluation results that demonstrate the adaptability, ease-of-use and usefulness of QualDash in a real-world scenario. | false | false | [
"Mai El-Shehaly",
"Rebecca Randell",
"Matthew Brehmer",
"Lynn McVey",
"Natasha Alvarado",
"Chris Gale",
"Roy A. Ruddle"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.03002v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference | 10.1109/TVCG.2020.3030439 | Color mapping is a foundational technique for visualizing scalar data. Prior literature offers guidelines for effective colormap design, such as emphasizing luminance variation while limiting changes in hue. However, empirical studies of color are largely focused on perceptual tasks. This narrow focus inhibits our understanding of how generalizable these guidelines are, particularly to tasks like visual inference that require synthesis and judgement across multiple percepts. Furthermore, the emphasis on traditional ramp designs (e.g., sequential or diverging) may sideline other key metrics or design strategies. We study how a cognitive metric-color name variation-impacts people's ability to make model-based judgments. In two graphical inference experiments, participants saw a series of color-coded scalar fields sampled from different models and assessed the relationships between these models. Contrary to conventional guidelines, participants were more accurate when viewing colormaps that cross a variety of uniquely nameable colors. We modeled participants' performance using this metric and found that it provides a better fit to the experimental data than do existing design principles. Our findings indicate cognitive advantages for colorful maps like rainbow, which exhibit high color categorization, despite their traditionally undesirable perceptual properties. We also found no evidence that color categorization would lead observers to infer false data features. Our results provide empirically grounded metrics for predicting a colormap's performance and suggest alternative guidelines for designing new quantitative colormaps to support inference. The data and materials for this paper are available at: https://osf.io/tck2r/ | false | false | [
"Khairi Reda",
"Danielle Albers Szafir"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "https://osf.io/avwrs",
"icon": "paper"
}
] |
InfoVis | 2,020 | Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs | 10.1109/TVCG.2020.3030371 | Matrix visualizations are a useful tool to provide a general overview of a graph's structure. For multivariate graphs, a remaining challenge is to cope with the attributes that are associated with nodes and edges. Addressing this challenge, we propose responsive matrix cells as a focus+context approach for embedding additional interactive views into a matrix. Responsive matrix cells are local zoomable regions of interest that provide auxiliary data exploration and editing facilities for multivariate graphs. They behave responsively by adapting their visual contents to the cell location, the available display space, and the user task. Responsive matrix cells enable users to reveal details about the graph, compare node and edge attributes, and edit data values directly in a matrix without resorting to external views or tools. We report the general design considerations for responsive matrix cells covering the visual and interactive means necessary to support a seamless data exploration and editing. Responsive matrix cells have been implemented in a web-based prototype based on which we demonstrate the utility of our approach. We describe a walk-through for the use case of analyzing a graph of soccer players and report on insights from a preliminary user feedback session. | false | false | [
"Tom Horak",
"Philip Berger",
"Heidrun Schumann",
"Raimund Dachselt",
"Christian Tominski"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.03385v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/4ibZT9_UE-o",
"icon": "video"
}
] |
InfoVis | 2,020 | Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics | 10.1109/TVCG.2020.3030448 | Infographic is a data visualization technique which combines graphic and textual descriptions in an aesthetic and effective manner. Creating infographics is a difficult and time-consuming process which often requires significant attempts and adjustments even for experienced designers, not to mention novice users with limited design expertise. Recently, a few approaches have been proposed to automate the creation process by applying predefined blueprints to user information. However, predefined blueprints are often hard to create, hence limited in volume and diversity. In contrast, good infogrpahics have been created by professionals and accumulated on the Internet rapidly. These online examples often represent a wide variety of design styles, and serve as exemplars or inspiration to people who like to create their own infographics. Based on these observations, we propose to generate infographics by automatically imitating examples. We present a two-stage approach, namely retrieve-then-adapt. In the retrieval stage, we index online examples by their visual elements. For a given user information, we transform it to a concrete query by sampling from a learned distribution about visual elements, and then find appropriate examples in our example library based on the similarity between example indexes and the query. For a retrieved example, we generate an initial drafts by replacing its content with user information. However, in many cases, user information cannot be perfectly fitted to retrieved examples. Therefore, we further introduce an adaption stage. Specifically, we propose a MCMC-like approach and leverage recursive neural networks to help adjust the initial draft and improve its visual appearance iteratively, until a satisfactory result is obtained. We implement our approach on widely-used proportion-related infographics, and demonstrate its effectiveness by sample results and expert reviews. | false | false | [
"Chunyao Qian",
"Shizhao Sun",
"Weiwei Cui",
"Jian-Guang Lou",
"Haidong Zhang",
"Dongmei Zhang 0001"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.01177v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Revealing Perceptual Proxies with Adversarial Examples | 10.1109/TVCG.2020.3030429 | Data visualizations convert numbers into visual marks so that our visual system can extract data from an image instead of raw numbers. Clearly, the visual system does not compute these values as a computer would, as an arithmetic mean or a correlation. Instead, it extracts these patterns using perceptual proxies; heuristic shortcuts of the visual marks, such as a center of mass or a shape envelope. Understanding which proxies people use would lead to more effective visualizations. We present the results of a series of crowdsourced experiments that measure how powerfully a set of candidate proxies can explain human performance when comparing the mean and range of pairs of data series presented as bar charts. We generated datasets where the correct answer-the series with the larger arithmetic mean or range-was pitted against an “adversarial” series that should be seen as larger if the viewer uses a particular candidate proxy. We used both Bayesian logistic regression models and a robust Bayesian mixed-effects linear model to measure how strongly each adversarial proxy could drive viewers to answer incorrectly and whether different individuals may use different proxies. Finally, we attempt to construct adversarial datasets from scratch, using an iterative crowdsourcing procedure to perform black-box optimization. | false | false | [
"Brian D. Ondov",
"Fumeng Yang",
"Matthew Kay 0001",
"Niklas Elmqvist",
"Steven Franconeri"
] | [] | [] | [] |
InfoVis | 2,020 | Revisited: Comparison of Empirical Methods to Evaluate Visualizations Supporting Crafting and Assembly Purposes | 10.1109/TVCG.2020.3030400 | Ubiquitous, situated, and physical visualizations create entirely new possibilities for tasks contextualized in the real world, such as doctors inserting needles. During the development of situated visualizations, evaluating visualizations is a core requirement. However, performing such evaluations is intrinsically hard as the real scenarios are safety-critical or expensive to test. To overcome these issues, researchers and practitioners adapt classical approaches from ubiquitous computing and use surrogate empirical methods such as Augmented Reality (AR), Virtual Reality (VR) prototypes, or merely online demonstrations. This approach's primary assumption is that meaningful insights can also be gained from different, usually cheaper and less cumbersome empirical methods. Nevertheless, recent efforts in the Human-Computer Interaction (HCI) community have found evidence against this assumption, which would impede the use of surrogate empirical methods. Currently, these insights rely on a single investigation of four interactive objects. The goal of this work is to investigate if these prior findings also hold for situated visualizations. Therefore, we first created a scenario where situated visualizations support users in do-it-yourself (DIY) tasks such as crafting and assembly. We then set up five empirical study methods to evaluate the four tasks using an online survey, as well as VR, AR, laboratory, and in-situ studies. Using this study design, we conducted a new study with 60 participants. Our results show that the situated visualizations we investigated in this study are not prone to the same dependency on the empirical method, as found in previous work. Our study provides the first evidence that analyzing situated visualizations through different empirical (surrogate) methods might lead to comparable results. | false | false | [
"Maximilian Weiß",
"Katrin Angerbauer",
"Alexandra Voit",
"Magdalena Schwarzl",
"Michael Sedlmair",
"Sven Mayer"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/gs6BCWhwQpI",
"icon": "video"
}
] |
InfoVis | 2,020 | SafetyLens: Visual Data Analysis of Functional Safety of Vehicles | 10.1109/TVCG.2020.3030382 | Modern automobiles have evolved from just being mechanical machines to having full-fledged electronics systems that enhance vehicle dynamics and driver experience. However, these complex hardware and software systems, if not properly designed, can experience failures that can compromise the safety of the vehicle, its occupants, and the surrounding environment. For example, a system to activate the brakes to avoid a collision saves lives when it functions properly, but could lead to tragic outcomes if the brakes were applied in a way that's inconsistent with the design. Broadly speaking, the analysis performed to minimize such risks falls into a systems engineering domain called Functional Safety. In this paper, we present SafetyLens, a visual data analysis tool to assist engineers and analysts in analyzing automotive Functional Safety datasets. SafetyLens combines techniques including network exploration and visual comparison to help analysts perform domain-specific tasks. This paper presents the design study with domain experts that resulted in the design guidelines, the tool, and user feedback. | false | false | [
"Arpit Narechania",
"Ahsan Qamar",
"Alex Endert"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2007.15832v3",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/z4Bwo1v4Xhk",
"icon": "video"
}
] |
InfoVis | 2,020 | Scalability of Network Visualisation from a Cognitive Load Perspective | 10.1109/TVCG.2020.3030459 | Node-link diagrams are widely used to visualise networks. However, even the best network layout algorithms ultimately result in ‘hairball’ visualisations when the graph reaches a certain degree of complexity, requiring simplification through aggregation or interaction (such as filtering) to remain usable. Until now, there has been little data to indicate at what level of complexity node-link diagrams become ineffective or how visual complexity affects cognitive load. To this end, we conducted a controlled study to understand workload limits for a task that requires a detailed understanding of the network topology-finding the shortest path between two nodes. We tested performance on graphs with 25 to 175 nodes with varying density. We collected performance measures (accuracy and response time), subjective feedback, and physiological measures (EEG, pupil dilation, and heart rate variability). To the best of our knowledge this is the first network visualisation study to include physiological measures. Our results show that people have significant difficulty finding the shortest path in high density node-link diagrams with more than 50 nodes and even low density graphs with more than 100 nodes. From our collected EEG data we observe functional differences in brain activity between hard and easy tasks. We found that cognitive load increased up to certain level of difficulty after which it decreased, likely because participants had given up. We also explored the effects of global network layout features such as size or number of crossings, and features of the shortest path such as length or straightness on task difficulty. We found that global features generally had a greater impact than those of the shortest path. | false | false | [
"Vahan Yoghourdjian",
"Yalong Yang 0001",
"Tim Dwyer",
"Lawrence Lee",
"Michael Wybrow",
"Kim Marriott"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.07944v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/BH14iZqlZVI",
"icon": "video"
}
] |
InfoVis | 2,020 | Semantic Discriminability for Visual Communication | 10.1109/TVCG.2020.3030434 | To interpret information visualizations, observers must determine how visual features map onto concepts. First and foremost, this ability depends on perceptual discriminability; observers must be able to see the difference between different colors for those colors to communicate different meanings. However, the ability to interpret visualizations also depends on semantic discriminability, the degree to which observers can infer a unique mapping between visual features and concepts, based on the visual features and concepts alone (i.e., without help from verbal cues such as legends or labels). Previous evidence suggested that observers were better at interpreting encoding systems that maximized semantic discriminability (maximizing association strength between assigned colors and concepts while minimizing association strength between unassigned colors and concepts), compared to a system that only maximized color-concept association strength. However, increasing semantic discriminability also resulted in increased perceptual distance, so it is unclear which factor was responsible for improved performance. In the present study, we conducted two experiments that tested for independent effects of semantic distance and perceptual distance on semantic discriminability of bar graph data visualizations. Perceptual distance was large enough to ensure colors were more than just noticeably different. We found that increasing semantic distance improved performance, independent of variation in perceptual distance, and when these two factors were uncorrelated, responses were dominated by semantic distance. These results have implications for navigating trade-offs in color palette design optimization for visual communication. | false | false | [
"Karen B. Schloss",
"Zachary Leggon",
"Laurent Lessard"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.03171v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Sequence Braiding: Visual Overviews of Temporal Event Sequences and Attributes | 10.1109/TVCG.2020.3030442 | Temporal event sequence alignment has been used in many domains to visualize nuanced changes and interactions over time. Existing approaches align one or two sentinel events. Overview tasks require examining all alignments of interest using interaction and time or juxtaposition of many visualizations. Furthermore, any event attribute overviews are not closely tied to sequence visualizations. We present Sequence Braiding, a novel overview visualization for temporal event sequences and attributes using a layered directed acyclic network. Sequence Braiding visually aligns many temporal events and attribute groups simultaneously and supports arbitrary ordering, absence, and duplication of events. In a controlled experiment we compare Sequence Braiding and IDMVis on user task completion time, correctness, error, and confidence. Our results provide good evidence that users of Sequence Braiding can understand high-level patterns and trends faster and with similar error. A full version of this paper with all appendices; the evaluation stimuli, data, and analysis code; and source code are available at $\text{osf.io}/\mathrm{mq}2\mathrm{wt}$. | false | false | [
"Sara Di Bartolomeo",
"Yixuan Zhang",
"Fangfang Sheng",
"Cody Dunne"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "https://osf.io/mq2wt",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/dPBnoCoGdq4",
"icon": "video"
}
] |
InfoVis | 2,020 | Shared Surfaces and Spaces: Collaborative Data Visualisation in a Co-located Immersive Environment | 10.1109/TVCG.2020.3030450 | Immersive technologies offer new opportunities to support collaborative visual data analysis by providing each collaborator a personal, high-resolution view of a flexible shared visualisation space through a head mounted display. However, most prior studies of collaborative immersive analytics have focused on how groups interact with surface interfaces such as tabletops and wall displays. This paper reports on a study in which teams of three co-located participants are given flexible visualisation authoring tools to allow a great deal of control in how they structure their shared workspace. They do so using a prototype system we call FIESTA: the Free-roaming Immersive Environment to Support Team-based Analysis. Unlike traditional visualisation tools, FIESTA allows users to freely position authoring interfaces and visualisation artefacts anywhere in the virtual environment, either on virtual surfaces or suspended within the interaction space. Our participants solved visual analytics tasks on a multivariate data set, doing so individually and collaboratively by creating a large number of 2D and 3D visualisations. Their behaviours suggest that the usage of surfaces is coupled with the type of visualisation used, often using walls to organise 2D visualisations, but positioning 3D visualisations in the space around them. Outside of tightly-coupled collaboration, participants followed social protocols and did not interact with visualisations that did not belong to them even if outside of its owner's personal workspace. | false | false | [
"Benjamin Lee",
"Xiaoyun Hu",
"Maxime Cordeil",
"Arnaud Prouzeau",
"Bernhard Jenny",
"Tim Dwyer"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.00050v2",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/fs4R0N42_MU",
"icon": "video"
}
] |
InfoVis | 2,020 | ShuttleSpace: Exploring and Analyzing Movement Trajectory in Immersive Visualization | 10.1109/TVCG.2020.3030392 | We present ShuttleSpace, an immersive analytics system to assist experts in analyzing trajectory data in badminton. Trajectories in sports, such as the movement of players and balls, contain rich information on player behavior and thus have been widely analyzed by coaches and analysts to improve the players' performance. However, existing visual analytics systems often present the trajectories in court diagrams that are abstractions of reality, thereby causing difficulty for the experts to imagine the situation on the court and understand why the player acted in a certain way. With recent developments in immersive technologies, such as virtual reality (VR), experts gradually have the opportunity to see, feel, explore, and understand these 3D trajectories from the player's perspective. Yet, few research has studied how to support immersive analysis of sports data from such a perspective. Specific challenges are rooted in data presentation (e.g., how to seamlessly combine 2D and 3D visualizations) and interaction (e.g., how to naturally interact with data without keyboard and mouse) in VR. To address these challenges, we have worked closely with domain experts who have worked for a top national badminton team to design ShuttleSpace. Our system leverages 1) the peripheral vision to combine the 2D and 3D visualizations and 2) the VR controller to support natural interactions via a stroke metaphor. We demonstrate the effectiveness of ShuttleSpace through three case studies conducted by the experts with useful insights. We further conduct interviews with the experts whose feedback confirms that our first-person immersive analytics system is suitable and useful for analyzing badminton data. | false | false | [
"Shuainan Ye",
"Zhutian Chen",
"Xiangtong Chu",
"Yifan Wang",
"Siwei Fu",
"Lejun Shen",
"Kun Zhou",
"Yingcai Wu"
] | [] | [] | [] |
InfoVis | 2,020 | SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects | 10.1109/TVCG.2020.3030404 | In this paper, we propose SineStream, a new variant of streamgraphs that improves their readability by minimizing sine illusion effects. Such effects reflect the tendency of humans to take the orthogonal rather than the vertical distance between two curves as their distance. In SineStream, we connect the readability of streamgraphs with minimizing sine illusions and by doing so provide a perceptual foundation for their design. As the geometry of a streamgraph is controlled by its baseline (the bottom-most curve) and the ordering of the layers, we re-interpret baseline computation and layer ordering algorithms in terms of reducing sine illusion effects. For baseline computation, we improve previous methods by introducing a Gaussian weight to penalize layers with large thickness changes. For layer ordering, three design requirements are proposed and implemented through a hierarchical clustering algorithm. Quantitative experiments and user studies demonstrate that SineStream improves the readability and aesthetics of streamgraphs compared to state-of-the-art methods. | false | false | [
"Chuan Bu",
"Quanjie Zhang",
"Qianwen Wang",
"Jian Zhang 0070",
"Michael Sedlmair",
"Oliver Deussen",
"Yunhai Wang"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/ZWYynQEBKj4",
"icon": "video"
}
] |
InfoVis | 2,020 | Staged Animation Strategies for Online Dynamic Networks | 10.1109/TVCG.2020.3030385 | Dynamic networks-networks that change over time-can be categorized into two types: offline dynamic networks, where all states of the network are known, and online dynamic networks, where only the past states of the network are known. Research on staging animated transitions in dynamic networks has focused more on offline data, where rendering strategies can take into account past and future states of the network. Rendering online dynamic networks is a more challenging problem since it requires a balance between timeliness for monitoring tasks-so that the animations do not lag too far behind the events-and clarity for comprehension tasks-to minimize simultaneous changes that may be difficult to follow. To illustrate the challenges placed by these requirements, we explore three strategies to stage animations for online dynamic networks: time-based, event-based, and a new hybrid approach that we introduce by combining the advantages of the first two. We illustrate the advantages and disadvantages of each strategy in representing low- and high-throughput data and conduct a user study involving monitoring and comprehension of dynamic networks. We also conduct a follow-up, think-aloud study combining monitoring and comprehension with experts in dynamic network visualization. Our findings show that animation staging strategies that emphasize comprehension do better for participant response times and accuracy. However, the notion of “comprehension” is not always clear when it comes to complex changes in highly dynamic networks, requiring some iteration in staging that the hybrid approach affords. Based on our results, we make recommendations for balancing event-based and time-based parameters for our hybrid approach. | false | false | [
"Tarik Crnovrsanin",
"Shilpika",
"Senthil K. Chandrasegaran",
"Kwan-Liu Ma"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.02005v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | StructGraphics: Flexible Visualization Design through Data-Agnostic and Reusable Graphical Structures | 10.1109/TVCG.2020.3030476 | Information visualization research has developed powerful systems that enable users to author custom data visualizations without textual programming. These systems can support graphics-driven practices by bridging lazy data-binding mechanisms with vector-graphics editing tools. Yet, despite their expressive power, visualization authoring systems often assume that users want to generate visual representations that they already have in mind rather than explore designs. They also impose a data-to-graphics workflow, where binding data dimensions to graphical properties is a necessary step for generating visualization layouts. In this paper, we introduce StructGraphics, an approach for creating data-agnostic and fully reusable visualization designs. StructGraphics enables designers to construct visualization designs by drawing graphics on a canvas and then structuring their visual properties without relying on a concrete dataset or data schema. In StructGraphics, tabular data structures are derived directly from the structure of the graphics. Later, designers can link these structures with real datasets through a spreadsheet user interface. StructGraphics supports the design and reuse of complex data visualizations by combining graphical property sharing, by-example design specification, and persistent layout constraints. We demonstrate the power of the approach through a gallery of visualization examples and reflect on its strengths and limitations in interaction with graphic designers and data visualization experts. | false | false | [
"Theophanis Tsandilas"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/1CAfAmzC4z0",
"icon": "video"
}
] |
InfoVis | 2,020 | Table Scraps: An Actionable Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism | 10.1109/TVCG.2020.3030462 | For the many journalists who use data and computation to report the news, data wrangling is an integral part of their work. Despite an abundance of literature on data wrangling in the context of enterprise data analysis, little is known about the specific operations, processes, and pain points journalists encounter while performing this tedious, time-consuming task. To better understand the needs of this user group, we conduct a technical observation study of 50 public repositories of data and analysis code authored by 33 professional journalists at 26 news organizations. We develop two detailed and cross-cutting taxonomies of data wrangling in computational journalism, for actions and for processes. We observe the extensive use of multiple tables, a notable gap in previous wrangling analyses. We develop a concise, actionable framework for general multi-table data wrangling that includes wrangling operations documented in our taxonomy that are without clear parallels in other work. This framework, the first to incorporate tables as first-class objects, will support future interactive wrangling tools for both computational journalism and general-purpose use. We assess the generative and descriptive power of our framework through discussion of its relationship to our set of taxonomies. | false | false | [
"Stephen Kasica",
"Charles Berret",
"Tamara Munzner"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.02373v2",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/g2gXq1DGULg",
"icon": "video"
}
] |
InfoVis | 2,020 | The Effectiveness of Interactive Visualization Techniques for Time Navigation of Dynamic Graphs on Large Displays | 10.1109/TVCG.2020.3030446 | Dynamic networks can be challenging to analyze visually, especially if they span a large time range during which new nodes and edges can appear and disappear. Although it is straightforward to provide interfaces for visualization that represent multiple states of the network (i.e., multiple timeslices) either simultaneously (e.g., through small multiples) or interactively (e.g., through interactive animation), these interfaces might not support tasks in which disjoint timeslices need to be compared. Since these tasks are key for understanding the dynamic aspects of the network, understanding which interactive visualizations best support these tasks is important. We present the results of a series of laboratory experiments comparing two traditional approaches (small multiples and interactive animation), with a more recent approach based on interactive timeslicing. The tasks were performed on a large display through a touch interface. Participants completed 24 trials of three tasks with all techniques. The results show that interactive timeslicing brings benefit when comparing distant points in time, but less benefits when analyzing contiguous intervals of time. | false | false | [
"Alexandra Lee",
"Daniel Archambault",
"Miguel A. Nacenta"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.12747v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | Towards Modeling Visualization Processes as Dynamic Bayesian Networks | 10.1109/TVCG.2020.3030395 | Visualization designs typically need to be evaluated with user studies, because their suitability for a particular task is hard to predict. What the field of visualization is currently lacking are theories and models that can be used to explain why certain designs work and others do not. This paper outlines a general framework for modeling visualization processes that can serve as the first step towards such a theory. It surveys related research in mathematical and computational psychology and argues for the use of dynamic Bayesian networks to describe these time-dependent, probabilistic processes. It is discussed how these models could be used to aid in design evaluation. The development of concrete models will be a long process. Thus, the paper outlines a research program sketching how to develop prototypes and their extensions from existing models, controlled experiments, and observational studies. | false | false | [
"Christian Heine 0002"
] | [] | [] | [] |
InfoVis | 2,020 | Truth or Square: Aspect Ratio Biases Recall of Position Encodings | 10.1109/TVCG.2020.3030422 | Bar charts are among the most frequently used visualizations, in part because their position encoding leads them to convey data values precisely. Yet reproductions of single bars or groups of bars within a graph can be biased. Curiously, some previous work found that this bias resulted in an overestimation of reproduced data values, while other work found an underestimation. Across three empirical studies, we offer an explanation for these conflicting findings: this discrepancy is a consequence of the differing aspect ratios of the tested bar marks. Viewers are biased to remember a bar mark as being more similar to a prototypical square, leading to an overestimation of bars with a wide aspect ratio, and an underestimation of bars with a tall aspect ratio. Experiments 1 and 2 showed that the aspect ratio of the bar marks indeed influenced the direction of this bias. Experiment 3 confirmed that this pattern of misestimation bias was present for reproductions from memory, suggesting that this bias may arise when comparing values across sequential displays or views. We describe additional visualization designs that might be prone to this bias beyond bar charts (e.g., Mekko charts and treemaps), and speculate that other visual channels might hold similar biases toward prototypical values. | false | false | [
"Cristina R. Ceja",
"Caitlyn M. McColeman",
"Cindy Xiong",
"Steven Franconeri"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.06773v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/YdMBzudK-sA",
"icon": "video"
}
] |
InfoVis | 2,020 | VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network | 10.1109/TVCG.2020.3030343 | We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode. | false | false | [
"Peiying Zhang",
"Chenhui Li",
"Changbo Wang"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2009.03817v1",
"icon": "paper"
}
] |
InfoVis | 2,020 | VisConnect: Distributed Event Synchronization for Collaborative Visualization | 10.1109/TVCG.2020.3030366 | Tools and interfaces are increasingly expected to be synchronous and distributed to accommodate remote collaboration. Yet, adoption of these techniques for data visualization is low partly because development is difficult: existing collaboration software systems either do not support simultaneous interaction or require expensive redevelopment of existing visualizations. We contribute VisConnect: a web-based synchronous distributed collaborative visualization system that supports most web-based SVG data visualizations, balances system safety with responsiveness, and supports simultaneous interaction from many collaborators. VisConnect works with existing visualization implementations with little-to-no code changes by synchronizing low-level JavaScript events across clients such that visualization updates proceed transparently across clients. This is accomplished via a peer-to-peer system that establishes consensus among clients on the per-element sequence of events, and uses a lock service to grant access over elements to clients. We contribute collaborative extensions of traditional visualization interaction techniques, such as drag, brush, and lasso, and discuss different strategies for collaborative visualization interactions. To demonstrate the utility of VisConnect, we present novel examples of collaborative visualizations in the healthcare domain, remote collaboration with annotation, and show in an education case study for e-learning with 22 participants that students found the ability to remotely collaborate on class activities helpful and enjoyable for understanding concepts. A free copy of this paper and source code are available on OSF at osf.io/ut7e6 and at visconnect.us. | false | false | [
"Michail Schwab",
"David Saffo",
"Yixuan Zhang",
"Shash Sinha",
"Cristina Nita-Rotaru",
"James Tompkin 0001",
"Cody Dunne",
"Michelle Borkin"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "https://osf.io/ut7e6",
"icon": "paper"
}
] |
InfoVis | 2,020 | Visual Analysis of Discrimination in Machine Learning | 10.1109/TVCG.2020.3030471 | The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination. | false | false | [
"Qianwen Wang",
"Zhenhua Xu",
"Zhutian Chen",
"Yong Wang 0021",
"Shixia Liu",
"Huamin Qu"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2007.15182v2",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/-K5ZXfMNLVY",
"icon": "video"
}
] |
InfoVis | 2,020 | Visual Reasoning Strategies for Effect Size Judgments and Decisions | 10.1109/TVCG.2020.3030335 | Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added. We find that adding means to uncertainty visualizations has small biasing effects on both magnitude estimation and decision-making, consistent with discounting uncertainty. We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks. In a qualitative analysis of users' strategy descriptions, we find that many users switch strategies and do not employ an optimal strategy when one exists. Uncertainty visualizations which are optimally designed in theory may not be the most effective in practice because of the ways that users satisfice with heuristics, suggesting opportunities to better understand visualization effectiveness by modeling sets of potential strategies. | false | false | [
"Alex Kale",
"Matthew Kay 0001",
"Jessica Hullman"
] | [
"BP"
] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2007.14516v3",
"icon": "paper"
}
] |
InfoVis | 2,020 | What Makes a Data-GIF Understandable? | 10.1109/TVCG.2020.3030396 | GIFs are enjoying increasing popularity on social media as a format for data-driven storytelling with visualization; simple visual messages are embedded in short animations that usually last less than 15 seconds and are played in automatic repetition. In this paper, we ask the question, “What makes a data-GIF understandable?” While other storytelling formats such as data videos, infographics, or data comics are relatively well studied, we have little knowledge about the design factors and principles for “data-GIFs”. To close this gap, we provide results from semi-structured interviews and an online study with a total of 118 participants investigating the impact of design decisions on the understandability of data-GIFs. The study and our consequent analysis are informed by a systematic review and structured design space of 108 data-GIFs that we found online. Our results show the impact of design dimensions from our design space such as animation encoding, context preservation, or repetition on viewers understanding of the GIF's core message. The paper concludes with a list of suggestions for creating more effective Data-GIFs. | false | false | [
"Xinhuan Shu",
"Aoyu Wu",
"Junxiu Tang",
"Benjamin Bach",
"Yingcai Wu",
"Huamin Qu"
] | [] | [
"V"
] | [
{
"name": "Fast Forward",
"url": "https://youtu.be/vs3iucWl0qI",
"icon": "video"
}
] |
InfoVis | 2,020 | Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale | 10.1109/TVCG.2020.3030399 | Visualizing spatial data on small-screen devices such as smartphones and smartwatches poses new challenges in computational cartography. The current interfaces for map exploration require their users to zoom in and out frequently. Indeed, zooming and panning are tools suitable for choosing the map extent corresponding to an area of interest. They are not as suitable, however, for resolving the graphical clutter caused by a high feature density since zooming in to a large map scale leads to a loss of context. Therefore, in this paper, we present new external labeling methods that allow a user to navigate through dense sets of points of interest while keeping the current map extent fixed. We provide a unified model, in which labels are placed at the boundary of the map and visually associated with the corresponding features via connecting lines, which are called leaders. Since the screen space is limited, labeling all features at the same time is impractical. Therefore, at any time, we label a subset of the features. We offer interaction techniques to change the current selection of features systematically and, thus, give the user access to all features. We distinguish three methods, which allow the user either to slide the labels along the bottom side of the map or to browse the labels based on pages or stacks. We present a generic algorithmic framework that provides us with the possibility of expressing the different variants of interaction techniques as optimization problems in a unified way. We propose both exact algorithms and fast and simple heuristics that solve the optimization problems taking into account different criteria such as the ranking of the labels, the total leader length as well as the distance between leaders. In experiments on real-world data we evaluate these algorithms and discuss the three variants with respect to their strengths and weaknesses proving the flexibility of the presented algorithmic framework. | false | false | [
"Sven Gedicke",
"Annika Bonerath",
"Benjamin Niedermann",
"Jan-Henrik Haunert"
] | [] | [
"P",
"V"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2008.13556v1",
"icon": "paper"
},
{
"name": "Fast Forward",
"url": "https://youtu.be/IuMhk8jp54c",
"icon": "video"
}
] |
EuroVis | 2,020 | A Globally Conforming Lattice Structure for 2D Stress Tensor Visualization | 10.1111/cgf.13991 | We present a visualization technique for 2D stress tensor fields based on the construction of a globally conforming lattice. Conformity ensures that the lattice edges follow the principal stress directions and the aspect ratio of lattice elements represents the stress anisotropy. Since such a lattice structure cannot be space‐filling in general, it is constructed from multiple intersecting lattice beams. Conformity at beam intersections is ensured via a constrained optimization problem, by computing the aspect ratio of elements at intersections so that their edges meet when continued along the principal stress lines. In combination with a coloring scheme that encodes relative stress magnitudes, a global visualization is achieved. By introducing additional constraints on the positional variation of the beam intersections, coherent visualizations are achieved when external loads or material parameters are changed. In a number of experiments using non‐trivial scenarios, we demonstrate the capability of the proposed visualization technique to show the global and local structure of a given stress field. | false | false | [
"Junpeng Wang",
"Jun Wu 0005",
"Rüdiger Westermann"
] | [] | [] | [] |
EuroVis | 2,020 | A Survey of Seed Placement and Streamline Selection Techniques | 10.1111/cgf.14036 | Streamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state‐of‐the‐art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high‐level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density‐based, feature‐based, and similarity‐based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future. | false | false | [
"Sudhanshu Sane",
"Roxana Bujack",
"Christoph Garth",
"Hank Childs"
] | [] | [] | [] |
EuroVis | 2,020 | A Survey on Transit Map Layout - from Design, Machine, and Human Perspectives | 10.1111/cgf.14030 | Transit maps are designed to present information for using public transportation systems, such as urban railways. Creating a transit map is a time‐consuming process, which requires iterative information selection, layout design, and usability validation, and thus maps cannot easily be customised or updated frequently. To improve this, scientists investigate fully‐ or semi‐automatic techniques in order to produce high quality transit maps using computers and further examine their corresponding usability. Nonetheless, the quality gap between manually‐drawn maps and machine‐generated maps is still large. To elaborate the current research status, this state‐of‐the‐art report provides an overview of the transit map generation process, primarily from Design, Machine, and Human perspectives. A systematic categorisation is introduced to describe the design pipeline, and an extensive analysis of perspectives is conducted to support the proposed taxonomy. We conclude this survey with a discussion on the current research status, open challenges, and future directions. | false | false | [
"Hsiang-Yun Wu",
"Benjamin Niedermann",
"Shigeo Takahashi",
"Maxwell J. Roberts",
"Martin Nöllenburg"
] | [] | [] | [] |
EuroVis | 2,020 | A Visual Analytics Approach to Facilitate Crime Hotspot Analysis | 10.1111/cgf.13969 | 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. | false | false | [
"Jose Florencio de Queiroz Neto",
"Emanuele Marques dos Santos",
"Creto Augusto Vidal",
"David S. Ebert"
] | [] | [] | [] |
EuroVis | 2,020 | Augmenting Node-Link Diagrams with Topographic Attribute Maps | 10.1111/cgf.13987 | 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. | false | false | [
"Reinhold Preiner",
"Johanna Schmidt",
"Katharina Krösl",
"Tobias Schreck",
"Gabriel Mistelbauer"
] | [] | [] | [] |
EuroVis | 2,020 | Bombalytics: Visualization of Competition and Collaboration Strategies of Players in a Bomb Laying Game | 10.1111/cgf.13965 | Competition and collaboration form complex interaction patterns between the agents and objects involved. Only by understanding these interaction patterns, we can reveal the strategies the participating parties applied. In this paper, we study such competition and collaboration behavior for a computer game. Serving as a testbed for artificial intelligence, the multiplayer bomb laying game Pommerman provides a rich source of advanced behavior of computer agents. We propose a visualization approach that shows an overview of multiple games, with a detailed timeline‐based visualization for exploring the specifics of each game. Since an analyst can only fully understand the data when considering the direct and indirect interactions between agents, we suggest various visual encodings of these interactions. Based on feedback from expert users and an application example, we demonstrate that the approach helps identify central competition strategies and provides insights on collaboration. | false | false | [
"Shivam Agarwal",
"Günter Wallner",
"Fabian Beck 0001"
] | [] | [] | [] |
EuroVis | 2,020 | Boxer: Interactive Comparison of Classifier Results | 10.1111/cgf.13972 | 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. | false | false | [
"Michael Gleicher",
"Aditya Barve",
"Xinyi Yu",
"Florian Heimerl"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2004.07964v1",
"icon": "paper"
}
] |
EuroVis | 2,020 | Canis: A High-Level Language for Data-Driven Chart Animations | 10.1111/cgf.14005 | 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. | false | false | [
"T. Ge",
"Y. Zhao",
"B. Lee",
"D. Ren",
"B. Chen",
"Y. Wang"
] | [] | [] | [] |
EuroVis | 2,020 | Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks | 10.1111/cgf.13973 | 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. | false | false | [
"Alex Bäuerle",
"Heiko Neumann",
"Timo Ropinski"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1808.03114v4",
"icon": "paper"
}
] |
EuroVis | 2,020 | Co-creating Visualizations: A First Evaluation with Social Science Researchers | 10.1111/cgf.13981 | 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. | false | false | [
"Gabriela Molina León",
"Andreas Breiter"
] | [] | [] | [] |
EuroVis | 2,020 | CPU Ray Tracing of Tree-Based Adaptive Mesh Refinement Data | 10.1111/cgf.13958 | 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. | false | false | [
"Feng Wang 0013",
"Nathan Marshak",
"William Usher 0001",
"Carsten Burstedde",
"Aaron Knoll",
"Timo Heister",
"Chris R. Johnson 0001"
] | [] | [] | [] |
EuroVis | 2,020 | Data Comets: Designing a Visualization Tool for Analyzing Autonomous Aerial Vehicle Logs with Grounded Evaluation | 10.1111/cgf.13994 | Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post‐flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of Data Comets, an open‐source and web‐based interactive visualization tool for post‐flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in‐the‐wild usage. A free copy of this paper and source code are available at osf.io/h4p7g | false | false | [
"David Saffo",
"Aristotelis Leventidis",
"Twinkle Jain",
"Michelle A. Borkin",
"Cody Dunne"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2005.06011v1",
"icon": "paper"
}
] |
EuroVis | 2,020 | DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning | 10.1111/cgf.13962 | 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. | false | false | [
"Theo Jaunet",
"Romain Vuillemot",
"Christian Wolf 0001"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1909.02982v2",
"icon": "paper"
}
] |
EuroVis | 2,020 | Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates | 10.1111/cgf.14000 | The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users’ confidence for two different strategies using both synthetic and real‐world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs. | false | false | [
"Michael Blumenschein",
"Xuan Zhang",
"David Pomerenke",
"Daniel A. Keim",
"Johannes Fuchs 0001"
] | [] | [] | [] |
EuroVis | 2,020 | Extraction of Distinguished Hyperbolic Trajectories for 2D Time-Dependent Vector Field Topology | 10.1111/cgf.13982 | This paper does two main contributions to 2D time‐dependent vector field topology. First, we present a technique for robust, accurate, and efficient extraction of distinguished hyperbolic trajectories (DHT), the generative structures of 2D time‐dependent vector field topology. It is based on refinement of initial candidate curves. In contrast to previous approaches, it is robust because the refinement converges for reasonably close initial candidates, it is accurate due to its adaptive scheme, and it is efficient due to its high convergence speed. Second, we provide a detailed evaluation and discussion of previous approaches for the extraction of DHTs and time‐dependent vector field topology in general. We demonstrate the utility of our approach using analytical flows, as well as data from computational fluid dynamics. | false | false | [
"Lutz Hofmann",
"Filip Sadlo"
] | [] | [] | [] |
EuroVis | 2,020 | Feature Driven Combination of Animated Vector Field Visualizations | 10.1111/cgf.13992 | 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. | false | false | [
"María-Jesús Lobo",
"Alexandru C. Telea",
"Christophe Hurter"
] | [] | [] | [] |
EuroVis | 2,020 | Fiber Surfaces for many Variables | 10.1111/cgf.13983 | 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. | false | false | [
"Christian Blecha",
"Felix Raith",
"A. J. Präger",
"Thomas Nagel",
"Olaf Kolditz",
"Jobst Maßmann",
"Niklas Röber",
"Michael Böttinger",
"Gerik Scheuermann"
] | [] | [] | [] |
EuroVis | 2,020 | Fuzzy Contour Trees: Alignment and Joint Layout of Multiple Contour Trees | 10.1111/cgf.13985 | 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. | false | false | [
"Anna Pia Lohfink",
"Florian Wetzels",
"Jonas Lukasczyk",
"Gunther H. Weber",
"Christoph Garth"
] | [] | [] | [] |
EuroVis | 2,020 | GTMapLens: Interactive Lens for Geo-Text Data Browsing on Map | 10.1111/cgf.13995 | 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. | false | false | [
"Chao Ma 0023",
"Ye Zhao 0003",
"Shamal Al-Dohuki",
"Jing Yang 0001",
"Xinyue Ye",
"Farah Kamw",
"Md. Amiruzzaman"
] | [] | [] | [] |
EuroVis | 2,020 | Hairy Slices II: Depth Cues for Visualizing 3D Streamlines Through Cutting Planes | 10.1111/cgf.13960 | Visualizing 3D vector fields is challenging because of occlusion problems and the difficulty of providing depth cues that adequately support the perception of direction of flow lines in 3D space. One of the depth cues that has proven most valuable for the perception of other kinds of 3D data, notably 3D networks and 3D point clouds, is structure‐from‐motion (also called the Kinetic Depth Effect); another powerful depth cue is stereoscopic viewing. We carried out an experiment of the perception of direction for short streamlines passing through a cutting plane. The conditions included viewing with and without structure‐from‐motion and with and without stereoscopic depth. Conditions also include comparing streamtubes to lines. The results show that for this particular task, stereo provided an effective depth cue, but structure‐from‐motion did not. Ringed streamtubes and streamcones provided good 3D direction information, even without stereoscopic viewing. We conclude with guidelines for viewing slices through vector fields. | false | false | [
"Andrew H. Stevens",
"Colin Ware",
"Thomas Butkiewicz",
"David H. Rogers 0001",
"Greg Abram"
] | [] | [] | [] |
EuroVis | 2,020 | Infomages: Embedding Data into Thematic Images | 10.1111/cgf.14004 | 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. | false | false | [
"Darius Coelho",
"Klaus Mueller 0001"
] | [
"HM"
] | [] | [] |
EuroVis | 2,020 | Knowledge-Assisted Comparative Assessment of Breast Cancer using Dynamic Contrast-Enhanced Magnetic Resonance Imaging | 10.1111/cgf.13959 | Breast perfusion data are dynamic medical image data that depict perfusion characteristics of the investigated tissue. These data consist of a series of static datasets that are acquired at different time points and aggregated into time intensity curves (TICs) for each voxel. The characteristics of these TICs provide important information about a lesion's composition, but their analysis is time‐consuming due to their large number. Subsequently, these TICs are used to classify a lesion as benign or malignant. This lesion scoring is commonly done manually by physicians and may therefore be subject to bias. We propose an approach that addresses both of these problems by combining an automated lesion classification with a visual confirmatory analysis, especially for uncertain cases. Firstly, we cluster the TICs of a lesion using ordering points to identify the clustering structure (OPTICS) and then visualize these clusters. Together with their relative size, they are added to a library. We then model fuzzy inference rules by using the lesion's TIC clusters as antecedents and its score as consequent. Using a fuzzy scoring system, we can suggest a score for a new lesion. Secondly, to allow physicians to confirm the suggestion in uncertain cases, we display the TIC clusters together with their spatial distribution and allow them to compare two lesions side by side. With our knowledge‐assisted comparative visual analysis, physicians can explore and classify breast lesions. The true positive prediction accuracy of our scoring system achieved 71.4 % in one‐fold cross‐validation using 14 lesions. | false | false | [
"Kai Nie",
"Pascal A. Baltzer",
"Bernhard Preim",
"Gabriel Mistelbauer"
] | [] | [] | [] |
EuroVis | 2,020 | LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic Vector Data Visualization | 10.1111/cgf.13993 | Visualization of large vector line data is a core task in geographic and cartographic systems. Vector maps are often displayed at different cartographic generalization levels, traditionally by using several discrete levels‐of‐detail (LODs). This limits the generalization levels to a fixed and predefined set of LODs, and generally does not support smooth LOD transitions. However, fast GPUs and novel line rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU‐based algorithms for locally‐adaptive line simplification and real‐time rendering. We propose a new technique that interactively visualizes large line vector datasets at variable LODs. It is based on the Douglas‐Peucker line simplification principle, generating an exhaustive set of line segments whose specific subsets represent the lines at any variable LOD. At run time, an appropriate and view‐dependent error metric supports screen‐space adaptive LOD levels and the display of the correct subset of line segments accordingly. Our implementation shows that we can simplify and display large line datasets interactively. We can successfully apply line style patterns, dynamic LOD selection lenses, and anti‐aliasing techniques to our line rendering. | false | false | [
"Alireza Amiraghdam",
"Alexandra Diehl",
"Renato Pajarola"
] | [
"HM"
] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1909.05511v2",
"icon": "paper"
}
] |
EuroVis | 2,020 | Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments | 10.1111/cgf.13976 | 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. | false | false | [
"Jillian Aurisano",
"Abhinav Kumar 0002",
"Abeer Alsaiari",
"Barbara Di Eugenio",
"Andrew E. Johnson 0001"
] | [] | [] | [] |
EuroVis | 2,020 | Metro Maps on Octilinear Grid Graphs | 10.1111/cgf.13986 | 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. | false | false | [
"Hannah Bast",
"Patrick Brosi",
"Sabine Storandt"
] | [] | [] | [] |
EuroVis | 2,020 | MotionGlyphs: Visual Abstraction of Spatio-Temporal Networks in Collective Animal Behavior | 10.1111/cgf.13963 | 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. | false | false | [
"Eren Cakmak",
"Hanna Schäfer",
"Juri Buchmüller",
"Johannes Fuchs 0001",
"Tobias Schreck",
"Alex Jordan",
"Daniel A. Keim"
] | [] | [] | [] |
EuroVis | 2,020 | Ocupado: Visualizing Location-Based Counts Over Time Across Buildings | 10.1111/cgf.13968 | 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. | false | false | [
"Michael Oppermann",
"Tamara Munzner"
] | [] | [] | [] |
EuroVis | 2,020 | Orchard: Exploring Multivariate Heterogeneous Networks on Mobile Phones | 10.1111/cgf.13967 | 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. | false | false | [
"Philipp Eichmann",
"Darren Edge",
"Nathan Evans",
"Bongshin Lee",
"Matthew Brehmer",
"Christopher M. White"
] | [] | [] | [] |
EuroVis | 2,020 | PAVED: Pareto Front Visualization for Engineering Design | 10.1111/cgf.13990 | 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. | false | false | [
"Lena Cibulski",
"Hubert Mitterhofer",
"Thorsten May",
"Jörn Kohlhammer"
] | [] | [] | [] |
EuroVis | 2,020 | Peax : Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning | 10.1111/cgf.13971 | 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. | false | false | [
"Fritz Lekschas",
"Brant Peterson",
"Daniel Haehn",
"Eric Ma",
"Nils Gehlenborg",
"Hanspeter Pfister"
] | [
"BP"
] | [] | [] |
EuroVis | 2,020 | Phase Space Projection of Dynamical Systems | 10.1111/cgf.13978 | 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. | false | false | [
"Nemanja Bartolovic",
"Markus Gross 0001",
"Tobias Günther"
] | [
"HM"
] | [] | [] |
EuroVis | 2,020 | Privacy-Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities | 10.1111/cgf.14032 | Preservation of data privacy and protection of sensitive information from potential adversaries constitute a key socio‐technical challenge in the modern era of ubiquitous digital transformation. Addressing this challenge needs analysis of multiple factors: algorithmic choices for balancing privacy and loss of utility, potential attack scenarios that can be undertaken by adversaries, implications for data owners, data subjects, and data sharing policies, and access control mechanisms that need to be built into interactive data interfaces. Visualization has a key role to play as part of the solution space, both as a medium of privacy‐aware information communication and also as a tool for understanding the link between privacy parameters and data sharing policies. The field of privacy‐preserving data visualization has witnessed progress along many of these dimensions. In this state‐of‐the‐art report, our goal is to provide a systematic analysis of the approaches, methods, and techniques used for handling data privacy in visualization. We also reflect on the road‐map ahead by analyzing the gaps and research opportunities for solving some of the pressing socio‐technical challenges involving data privacy with the help of visualization. | false | false | [
"Kaustav Bhattacharjee",
"Min Chen 0001",
"Aritra Dasgupta"
] | [] | [] | [] |
EuroVis | 2,020 | Quantitative Comparison of Time-Dependent Treemaps | 10.1111/cgf.13989 | 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. | false | false | [
"Eduardo Faccin Vernier",
"Max Sondag",
"João Luiz Dihl Comba",
"Bettina Speckmann",
"Alexandru C. Telea",
"Kevin Verbeek"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1906.06014v2",
"icon": "paper"
}
] |
EuroVis | 2,020 | Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques | 10.1111/cgf.13977 | 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. | false | false | [
"Eduardo Faccin Vernier",
"Rafael Garcia",
"Iron Prando da Silva",
"João Luiz Dihl Comba",
"Alexandru C. Telea"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/2002.07481v1",
"icon": "paper"
}
] |
EuroVis | 2,020 | QUESTO: Interactive Construction of Objective Functions for Classification Tasks | 10.1111/cgf.13970 | 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. | false | false | [
"Subhajit Das 0002",
"Shenyu Xu",
"Michael Gleicher",
"Remco Chang",
"Alex Endert"
] | [] | [] | [] |
EuroVis | 2,020 | Reading Traces: Scalable Exploration in Elastic Visualizations of Cultural Heritage Data | 10.1111/cgf.13964 | Through a design study, we develop an approach to data exploration that utilizes elastic visualizations designed to support varying degrees of detail and abstraction. Examining the notions of scalability and elasticity in interactive visualizations, we introduce a visualization of personal reading traces such as marginalia or markings inside the reference library of German realist author Theodor Fontane. To explore such a rich and extensive collection, meaningful visual forms of abstraction and detail are as important as the transitions between those states. Following a growing research interest in the role of fluid interactivity and animations between views, we are particularly interested in the potential of carefully designed transitions and consistent representations across scales. The resulting prototype addresses humanistic research questions about the interplay of distant and close reading with visualization research on continuous navigation along several granularity levels, using scrolling as one of the main interaction mechanisms. In addition to presenting the design process and resulting prototype, we present findings from a qualitative evaluation of the tool, which suggest that bridging between distant and close views can enhance exploration, but that transitions between views need to be crafted very carefully to facilitate comprehension. | false | false | [
"Mark-Jan Bludau",
"Viktoria Brüggemann",
"Anna Busch",
"Marian Dörk"
] | [] | [] | [] |
EuroVis | 2,020 | Representative Isovalue Detection and Isosurface Segmentation Using Novel Isosurface Measures | 10.1111/cgf.13961 | 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. | false | false | [
"Cuilan Wang"
] | [] | [] | [] |
EuroVis | 2,020 | Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis | 10.1111/cgf.13997 | Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts. | false | false | [
"Jianping Kelvin Li",
"Shenyu Xu",
"Yecong (Chris) Ye",
"Kwan-Liu Ma"
] | [] | [] | [] |
EuroVis | 2,020 | SEEVis: A Smart Emergency Evacuation Plan Visualization System with Data-Driven Shot Designs | 10.1111/cgf.13999 | Despite the significance of tracking human mobility dynamics in a large‐scale earthquake evacuation for an effective first response and disaster relief, the general understanding of evacuation behaviors remains limited. Numerous individual movement trajectories, disaster damages of civil engineering, associated heterogeneous data attributes, as well as complex urban environment all obscure disaster evacuation analysis. Although visualization methods have demonstrated promising performance in emergency evacuation analysis, they cannot effectively identify and deliver the major features like speed or density, as well as the resulting evacuation events like congestion or turn‐back. In this study, we propose a shot design approach to generate customized and narrative animations to track different evacuation features with different exploration purposes of users. Particularly, an intuitive scene feature graph that identifies the most dominating evacuation events is first constructed based on user‐specific regions or their tracking purposes on a certain feature. An optimal camera route, i.e., a storyboard is then calculated based on the previous user‐specific regions or features. For different evacuation events along this route, we employ the corresponding shot design to reveal the underlying feature evolution and its correlation with the environment. Several case studies confirm the efficacy of our system. The feedback from experts and users with different backgrounds suggests that our approach indeed helps them better embrace a comprehensive understanding of the earthquake evacuation. | false | false | [
"Quan Li",
"Yingjie Liu",
"Li Chen",
"Xingchao Yang",
"Yi Peng",
"Xiaoru Yuan",
"M. L. L. Wijerathne"
] | [] | [] | [] |
EuroVis | 2,020 | SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics | 10.1111/cgf.13998 | 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. | false | false | [
"Meng Xia",
"Min Xu",
"Chuan-En Lin",
"Ta Ying Cheng",
"Huamin Qu",
"Xiaojuan Ma"
] | [] | [] | [] |
EuroVis | 2,020 | Set Streams: Visual Exploration of Dynamic Overlapping Sets | 10.1111/cgf.13988 | In many applications, membership of set elements changes over time. Since each element can be present in multiple sets, the sets also overlap. As a result, it becomes challenging to visualize the temporal change in set membership of elements across several timesteps while showing individual set intersections. We propose Set Streams, a visualization technique that represents changing set structures on a timeline as branching and merging streams. The streams encode the changing membership of elements with set intersections. A query‐based selection mechanism supports a flexible comparison of selected groups of elements across the temporal evolution. The main timeline view is complemented with additional panels to provide details about the elements. While the proposed visualization is an application‐independent visualization technique for dynamic sets, we demonstrate its effectiveness and applicability through three diverse application examples and expert feedback. | false | false | [
"Shivam Agarwal",
"Fabian Beck 0001"
] | [] | [] | [] |
EuroVis | 2,020 | Short-Contact Touch-Manipulation of Scatterplot Matrices on Wall Displays | 10.1111/cgf.13979 | 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. | false | false | [
"Patrick Riehmann",
"Gabriela Molina León",
"Joshua Reibert",
"Florian Echtler",
"Bernd Fröhlich 0001"
] | [] | [] | [] |
EuroVis | 2,020 | State of the Art in Time-Dependent Flow Topology: Interpreting Physical Meaningfulness Through Mathematical Properties | 10.1111/cgf.14037 | We present a state‐of‐the‐art report on time‐dependent flow topology. We survey representative papers in visualization and provide a taxonomy of existing approaches that generalize flow topology from time‐independent to time‐dependent settings. The approaches are classified based upon four categories: tracking of steady topology, reference frame adaption, pathline classification or clustering, and generalization of critical points. Our unique contributions include introducing a set of desirable mathematical properties to interpret physical meaningfulness for time‐dependent flow visualization, inferring mathematical properties associated with selective research papers, and utilizing such properties for classification. The five most important properties identified in the existing literature include coincidence with the steady case, induction of a partition within the domain, Lagrangian invariance, objectivity, and Galilean invariance. | false | false | [
"Roxana Bujack",
"Lin Yan",
"Ingrid Hotz",
"Christoph Garth",
"Bei Wang 0001"
] | [] | [] | [] |
EuroVis | 2,020 | Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude | 10.1111/cgf.13980 | 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. Additional material: https://flowstory.github.io/attitudes/. | false | false | [
"Johannes Liem",
"Charles Perin",
"Jo Wood"
] | [] | [] | [] |
EuroVis | 2,020 | Sublinear Time Force Computation for Big Complex Network Visualization | 10.1111/cgf.14003 | 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. | false | false | [
"Amyra Meidiana",
"Seok-Hee Hong 0001",
"Marnijati Torkel",
"Shijun Cai",
"Peter Eades"
] | [] | [] | [] |
EuroVis | 2,020 | Sunspot Plots: Model-based Structure Enhancement for Dense Scatter Plots | 10.1111/cgf.14001 | Scatter plots are a powerful and well‐established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real‐world data sets and validate its effectiveness in a user study. | false | false | [
"Thomas Trautner",
"Fabian Bolte",
"Sergej Stoppel",
"Stefan Bruckner"
] | [] | [] | [] |
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