International audience

Most work on collaborative immersive systems mimics real-world settings, using fully shared virtual workspaces that foster close collaboration. However, recent work in educational contexts using remote desktop environments suggests these shared approaches may not be optimal for learning, as it showed that individual workspaces lead to better learning outcomes. In this paper, we investigate whether individual workspaces also lead to better outcomes in a collaborative VR learning environment. We compare three distinct workspace configurations in a problem-solving task: (1) a fully shared environment where two users work on the same materials, (2) a replicated environment where each user has their own copy of the materials but can still see their partner and their workspace, and (3) a separated environment where users cannot see each other nor each other's workspace and each has individual materials. We evaluate how these configurations influenced collaborative interaction, problem-solving strategies, and learning. Our results suggest the replicated workspace reduced social experience and did not improve learning outcomes compared to the shared one, however, it allowed broader exploration of the problem space.

We investigate the perception of visual variables on wall-sized tiled displays within an immersive environment. We designed and conducted two formal user studies focusing on elementary visualization reading tasks in VR. The first study compared three different virtual display arrangements (Flat, Cylinder, and Cockpit). It showed that participants made smaller errors on virtual curved walls (Cylinder and Cockpit) compared to Flat. Following that, we compared the results with those from a previous study conducted in a real-world setting. The comparative analysis showed that virtual curved walls resulted in smaller errors than the real-world flat wall display, but with longer task completion time. The second study evaluated the impact of four 3D user interaction techniques (Selection, Walking, Steering, and Teleportation) on performing the elementary task on the virtual Flat wall display. The results confirmed that interaction techniques further improved task performance. Finally, we discuss the limitations and future work.

We present the results of an exploratory study on how pairs interact with speech commands and touch gestures on a wall-sized display during a collaborative sensemaking task. Previous work has shown that speech commands, alone or in combination with other input modalities, can support visual data exploration by individuals. However, it is still unknown whether and how speech commands can be used in collaboration, and for what tasks. To answer these questions, we developed a functioning prototype that we used as a technology probe. We conducted an in-depth exploratory study with 10 participant pairs to analyze their interaction choices, the interplay between the input modalities, and their collaboration. While touch was the most used modality, we found that participants preferred speech commands for global operations, used them for distant interaction, and that speech interaction contributed to the awareness of the partner's actions. Furthermore, the likelihood of using speech commands during collaboration was related to the personality trait of agreeableness. Regarding collaboration styles, participants interacted with speech equally often whether they were in loosely or closely coupled collaboration. While the partners stood closer to each other during close collaboration, they did not distance themselves to use speech commands. From our findings, we derive and contribute a set of design considerations for collaborative and multimodal interactive data analysis systems. All supplemental materials are available at https://osf.io/8gpv2.

We present a systematic review, an empirical study, and a first set of considerations for designing visualizations in motion, derived from a concrete scenario in which these visualizations were used to support a primary task. In practice, when viewers are confronted with embedded visualizations, they often have to focus on a primary task and can only quickly glance at a visualization showing rich, often dynamically updated, information. As such, the visualizations must be designed so as not to distract from the primary task, while at the same time being readable and useful for aiding the primary task. For example, in games, players who are engaged in a battle have to look at their enemies but also read the remaining health of their own game character from the health bar over their character's head. Many trade-ofts are possible in the design of embedded visualizations in such dynamic scenarios, which we explore in-depth in this paper with a focus on user experience. We use video games as an example of an application context with a rich existing set of visualizations in motion. We begin our work with a systematic review of in-game visualizations in motion. Next, we conduct an empirical user study to investigate how different embedded visualizations in motion designs impact user experience. We conclude with a set of considerations and trade-offs for designing visualizations in motion more broadly as derived from what we learned about video games. All supplemental materials of this paper are available at osf.io/3v8wm/.

We report on challenges and considerations for supporting design processes for visualizations in motion embedded in sports videos. We derive our insights from analyzing swimming race visualizations and motion-related data, building a technology probe, as well as a study with designers. Understanding how to design situated visualizations in motion is important for a variety of contexts. Competitive sports coverage, in particular, increasingly includes information on athlete or team statistics and records. Although moving visual representations attached to athletes or other targets are starting to appear, systematic investigations on how to best support their design process in the context of sports videos are still missing. Our work makes several contributions in identifying opportunities for visualizations to be added to swimming competition coverage but, most importantly, in identifying requirements and challenges for designing situated visualizations in motion. Our investigations include the analysis of a survey with swimming enthusiasts on their motion-related information needs, an ideation workshop to collect designs and elicit design challenges, the design of a technology probe that allows to create embedded visualizations in motion based on real data (Fig. 1), and an evaluation with visualization designers that aimed to understand the benefits of designing directly on videos.

Wall displays are well suited for collaborative work and are often placed in rooms with ample space in front of them that remains largely unused. Augmented Reality (AR) headsets can seamlessly extend the collaboration space around the Wall. Nevertheless, it is unclear if extending Walls with AR is effective and how it may affect collaboration. We first present a prototype combining a Wall and AR headsets to extend the Wall workspace. We then use this prototype to study how users utilize the virtual space created in AR. In an experiment with 24 participants, we compare how pairs solve collaborative tasks with the Wall alone and with Wall+AR. Our qualitative and quantitative results highlight that with Wall+AR, participants use the physical space in front and around the Wall extensively, and while this creates interaction overhead, it does not impact performance and improves the user experience.

We present three studies that investigate the effectiveness of multiple glanceable part-to-whole proportion representations on smartwatch faces. Our goal was to understand how quickly and accurately people can make judgments about their progress toward multiple goals displayed in a small space. We designed our three studies with increasing external validity. The first study compared bar charts, radial bar charts, and text representations—shown with a digital time display. The second study added an analog time dial as a distractor to increase the complexity of the watch face. To emulate realistic viewing conditions, the third study investigated the effect of viewing angles. In Study 1 bar and radial bar charts outperformed text representations, in Study 2 adding an analog time dial as a distractor did not affect task performance, and in Study 3 only the most extreme angle led to some performance decrease. Supplementary material is available at https://osf.io/ad2z7/.

In domains, such as agronomy or manufacturing, experts need to consider trade-offs when making decisions that involve several, often competing, objectives. Such analysis is complex and may be conducted over long periods of time, making it hard to revisit. In this paper, we consider the use of analytic provenance mechanisms to aid experts recall and keep track of trade-off analysis. We implemented VisProm, a web-based trade-off analysis system, that incorporates in-visualization provenance views, designed to help experts keep track of trade-offs and their objectives. We used VisProm as a technology probe to understand user needs and explore the potential role of provenance in this context. Through observation sessions with three groups of experts analyzing their own data, we make the following contributions. We first, identify eight high-level tasks that experts engaged in during trade-off analysis, such as locating and characterizing interest zones in the trade-off space, and show how these tasks can be supported by provenance visualization. Second, we refine findings from previous work on provenance purposes such as recall and reproduce, by identifying specific objects of these purposes related to trade-off analysis, such as interest zones, and exploration structure (e.g., exploration of alternatives and branches). Third, we discuss insights on how the identified provenance objects and our designs support these trade-off analysis tasks, both when revisiting past analysis and while actively exploring. And finally, we identify new opportunities for provenance-driven trade-off analysis, for example related to monitoring the coverage of the trade-off space, and tracking alternative trade-off scenarios.

Storyline visualizations are a powerful way to compactly visualize how the relationships between people evolve over time. Real-world relationships often also involve space, for example the cities that two political rivals visited together or alone over the years. By default, Storyline visualizations only show implicitly geospatial co-occurrence between people (drawn as lines), by bringing their lines together. Even the few designs that do explicitly show geographic locations only do so in abstract ways (e.g., annotations) and do not communicate geospatial information, such as the direction or extent of their political campains. We introduce Geo-Storylines, a collection of visualisation designs that integrate geospatial context into Storyline visualizations, using different strategies for compositing time and space. Our contribution is twofold. First, we present the results of a sketching workshop with 11 participants, that we used to derive a design space for integrating maps into Storylines. Second, by analyzing the strengths and weaknesses of the potential designs of the design space in terms of legibility and ability to scale to multiple relationships, we extract the three most promising: Time Glyphs, Coordinated Views, and Map Glyphs. We compare these three techniques first in a controlled study with 18 participants, under five different geospatial tasks and two maps of different complexity. We additionally collected informal feedback about their usefulness from domain experts in data journalism. Our results indicate that, as expected, detailed performance depends on the task. Nevertheless, Coordinated Views remain a highly effective and preferred technique across the board.

We present the findings of four studies related to the visualization of sleep data on wearables with two form factors: smartwatches and fitness bands. Our goal was to understand the interests, preferences, and effectiveness of different sleep visualizations by form factor. In a survey, we showed that wearers were mostly interested in weekly sleep duration, and nightly sleep phase data. Visualizations of this data were generally preferred over purely text-based representations, and the preferred chart type for fitness bands, and smartwatches was often the same. In one in-person pilot study, and two crowdsourced studies, we then tested the effectiveness of the most preferred representations for different tasks, and found that participants performed simple tasks effectively on both form factors but more complex tasks benefited from the larger smartwatch size. Lastly, we reflect on our crowdsourced study methodology for testing the effectiveness of visualizations for wearables. Supplementary material is available at https://osf.io/yz8ar/.

Problem-driven visualization work is rooted in deeply understanding the data, actors, processes, and workflows of a target domain. However, an individual's personality traits and cognitive abilities may also influence visualization use. Diverse user needs and abilities raise natural questions for specificity in visualization design: Could individuals from different domains exhibit performance differences when using visualizations? Are any systematic variations related to their cognitive abilities? This study bridges domain-specific perspectives on visualization design with those provided by cognition and perception. We measure variations in visualization task performance across chemistry, computer science, and education, and relate these differences to variations in spatial ability. We conducted an online study with over 60 domain experts consisting of tasks related to pie charts, isocontour plots, and 3D scatterplots, and grounded by a well-documented spatial ability test. Task performance (correctness) varied with profession across more complex visualizations (isocontour plots and scatterplots), but not pie charts, a comparatively common visualization. We found that correctness correlates with spatial ability, and the professions differ in terms of spatial ability. These results indicate that domains differ not only in the specifics of their data and tasks, but also in terms of how effectively their constituent members engage with visualizations and their cognitive traits. Analyzing participants' confidence and strategy comments suggests that focusing on performance neglects important nuances, such as differing approaches to engage with even common visualizations and potential skill transference. Our findings offer a fresh perspective on discipline-specific visualization with specific recommendations to help guide visualization design that celebrates the uniqueness of the disciplines and individuals we seek to serve.

This February 2022 issue of theIEEE Transactions on Visualization and Computer Graphics (TVCG)contains the proceedings of IEEE VIS 2021, held online on October 24-29, 2021, with General Chairs from Tulane University and Universidade de Sao Paulo. With IEEE VIS 2021, the conference series is in its 32nd year.

We contribute a research agenda for visualization in motion and two experiments to understand how well viewers can read data from moving visualizations. We define visualizations in motion as visual data representations that are used in contexts that exhibit relative motion between a viewer and an entire visualization. Sports analytics, video games, wearable devices, or data physicalizations are example contexts that involve different types of relative motion between a viewer and a visualization. To analyze the opportunities and challenges for designing visualization in motion, we show example scenarios and outline a first research agenda. Motivated primarily by the prevalence of and opportunities for visualizations in sports and video games we started to investigate a small aspect of our research agenda: the impact of two important characteristics of motion-speed and trajectory on a stationary viewer's ability to read data from moving donut and bar charts. We found that increasing speed and trajectory complexity did negatively affect the accuracy of reading values from the charts and that bar charts were more negatively impacted. In practice, however, this impact was small: both charts were still read fairly accurately.

Geographical propagation phenomena occur in multiple domains, such as in epidemiology and social media. Propagation dynamics are often complex, and visualizations play a key role in helping subject-matter experts understand and analyze them. However, there is little empirical data about the effectiveness of the various strategies used to visualize geographical propagation. To fill this gap, we conduct an experiment to evaluate the effectiveness of three strategies: an animated map, small-multiple maps, and a single map with glyphs. We compare them under five tasks that vary in one of the following dimensions: propagation scope, direction, speed, peaks, and spatial jumps. Our results show that small-multiple maps perform best overall, but that the effectiveness of each visualization varies depending on the task considered.

Recent years have seen an increasing interest in the authoring and crafting of personal visualizations. Mainstream data analysis and authoring tools lack the flexibility for customization and personalization, whereas tools from the research community either require creativity and drawing skills, or are limited to simple vector graphics. We present DataQuilt, a novel system that enables visualization authors to iteratively design pictorial visualizations as collages. Real images (e.g., paintings, photographs, sketches) act as both inspiration and as a resource of visual elements that can be mapped to data. The creative pipeline involves the semi-guided extraction of relevant elements of an image (arbitrary regions, regular shapes, color palettes, textures) aided by computer vision techniques; the binding of these graphical elements and their features to data in order to create meaningful visualizations; and the iterative refinement of both features and visualizations through direct manipulation. We demonstrate the usability of DataQuilt in a controlled study and its expressiveness through a collection of authored visualizations from a second open-ended study.

Shared displays are well suited to public viewing and collaboration, however they lack personal space to view private information and act without disturbing others. Combining them with Augmented Reality (AR) headsets allows interaction without altering the context on the shared display. We study a set of such interaction techniques in the context of network navigation, in particular path following, an important network analysis task. Applications abound, for example planning private trips on a network map shown on a public display. The proposed techniques allow for hands-free interaction, rendering visual aids inside the headset, in order to help the viewer maintain a connection between the AR cursor and the network that is only shown on the shared display. In two experiments on path following, we found that adding persistent connections between the AR cursor and the network on the shared display works well for high precision tasks, but more transient connections work best for lower precision tasks. More broadly, we show that combining personal AR interaction with shared displays is feasible for network navigation.

Existing systems dealing with the increasing volume of data series cannot guarantee interactive response times, even for fundamental tasks such as similarity search. Therefore, it is necessary to develop analytic approaches that support exploration and decision making by providing progressive results, before the final and exact ones have been computed. Prior works lack both efficiency and accuracy when applied to large-scale data series collections. We present and experimentally evaluate a new probabilistic learning-based method that provides quality guarantees for progressive Nearest Neighbor (NN) query answering. We provide both initial and progressive estimates of the final answer that are getting better during the similarity search, as well suitable stopping criteria for the progressive queries. Experiments with synthetic and diverse real datasets demonstrate that our prediction methods constitute the first practical solution to the problem, significantly outperforming competing approaches.

Observing the relationship between two or more variables over space and time is essential in many domains. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location; and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only). Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Based on these findings we present a set of design guidelines about geo-temporal visualization techniques for communicating correlation.

Information visualization designers strive to design data displays that allow for efficient exploration, analysis, and communication of patterns in data, leading to informed decisions. Unfortunately, human judgment and decision making are imperfect and often plagued by cognitive biases. There is limited empirical research documenting how these biases affect visual data analysis activities. Existing taxonomies are organized by cognitive theories that are hard to associate with visualization tasks. Based on a survey of the literature we propose a task-based taxonomy of 154 cognitive biases organized in 7 main categories. We hope the taxonomy will help visualization researchers relate their design to the corresponding possible biases, and lead to new research that detects and addresses biased judgment and decision making in data visualization.

Experts in different domains rely increasingly on simulation models of complex processes to reach insights, make decisions, and plan future projects. These models are often used to study possible trade-offs, as experts try to optimise multiple conflicting objectives in a single investigation. Understanding all the model intricacies, however, is challenging for a single domain expert. We propose a simple approach to support multiple experts when exploring complex model results. First, we reduce the model exploration space, then present the results on a shared interactive surface, in the form of a scatterplot matrix and linked views. To explore how multiple experts analyse trade-offs using this setup, we carried out an observational study focusing on the link between expertise and insight generation during the analysis process. Our results reveal the different exploration strategies and multi-storyline approaches that domain experts adopt during trade-off analysis, and inform our recommendations for collaborative model exploration systems.

We present the results of two perception studies to assess how quickly people can perform a simple data comparison task for small-scale visualizations on a smartwatch. The main goal of these studies is to extend our understanding of design constraints for smartwatch visualizations. Previous work has shown that a vast majority of smartwatch interactions last under 5 s. It is still unknown what people can actually perceive from visualizations during such short glances, in particular with such a limited display space of smartwatches. To shed light on this question, we conducted two perception studies that assessed the lower bounds of task time for a simple data comparison task. We tested three chart types common on smartwatches: bar charts, donut charts, and radial bar charts with three different data sizes: 7, 12, and 24 data values. In our first study, we controlled the differences of the two target bars to be compared, while the second study varied the difference randomly. For both studies, we found that participants performed the task on average in <300 ms for the bar chart, <220 ms for the donut chart, and in <1780 ms for the radial bar chart. Thresholds in the second study per chart type were on average 1.14-1.35× higher than in the first study. Our results show that bar and donut charts should be preferred on smartwatch displays when quick data comparisons are necessary.

Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias - the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.

A common challenge faced by many domain experts working with time series data is how to identify and compare similar patterns. This operation is fundamental in high-level tasks, such as detecting recurring phenomena or creating clusters of similar temporal sequences. While automatic measures exist to compute time series similarity, human intervention is often required to visually inspect these automatically generated results. The visualization literature has examined similarity perception and its relation to automatic similarity measures for line charts, but has not yet considered if alternative visual representations, such as horizon graphs and colorfields, alter this perception. Motivated by how neuroscientists evaluate epileptiform patterns, we conducted two experiments that study how these three visualization techniques affect similarity perception in EEG signals. We seek to understand if the time series results returned from automatic similarity measures are perceived in a similar manner, irrespective of the visualization technique; and if what people perceive as similar with each visualization aligns with different automatic measures and their similarity constraints. Our findings indicate that horizon graphs align with similarity measures that allow local variations in temporal position or speed (i.e., dynamic time warping) more than the two other techniques. On the other hand, horizon graphs do not align with measures that are insensitive to amplitude and y-offset scaling (i.e., measures based on z-normalization), but the inverse seems to be the case for line charts and colorfields. Overall, our work indicates that the choice of visualization affects what temporal patterns we consider as similar, i.e., the notion of similarity in time series is not visualization independent.

In multi-display environments (MDEs) that include large shared displays and desktops, users can engage in both close collaboration and parallel or personal work. To transition between the displays can be challenging in complex settings, such as crisis management rooms. To provide workspace awareness and to factilitate these transitions, we design and implement three interactions techniques that display users' activities. We explore how and where to display this activity: briefly on the shared display, or more persistently on a peripheral floor display. In a user study, motivated by the context of a crisis room where multiple operators with different roles need to cooperate, we tested the usability of the techniques and provided insights on such transitions in systems running on MDEs.

We explore how to rigorously evaluate multidimensional visualizations for their ability to support decision making. We first define multi-attribute choice tasks, a type of decision task commonly performed with such visualizations. We then identify which of the existing multidimensional visualizations are compatible with such tasks, and set out to evaluate three elementary visualizations: parallel coordinates, scatterplot matrices and tabular visualizations. Our method consists in first giving participants low-level analytic tasks, in order to ensure that they properly understood the visualizations and their interactions. Participants are then given multi-attribute choice tasks consisting of choosing holiday packages. We assess decision support through multiple objective and subjective metrics, including a decision accuracy metric based on the consistency between the choice made and self-reported preferences for attributes. We found the three visualizations to be comparable on most metrics, with a slight advantage for tabular visualizations. In particular, tabular visualizations allow participants to reach decisions faster. Thus, although decision time is typically not central in assessing decision support, it can be used as a tie-breaker when visualizations achieve similar decision accuracy. Our results also suggest that indirect methods for assessing choice confidence may allow to better distinguish between visualizations than direct ones. We finally discuss the limitations of our methods and directions for future work, such as the need for more sensitive metrics of decision support.

We explore the effects of providing task context when evaluating visualization tools using crowdsourcing. We gave crowdsource workers i) abstract information visualization tasks without any context, ii) tasks where we added semantics to the dataset, and iii) tasks with two types of backstory narratives: an analytic narrative and a decision-making narrative. Contrary to our expectations, we did not find evidence that adding data semantics increases accuracy, and further found that our backstory narratives can even decrease accuracy. Adding dataset semantics can however increase attention and provide subjective benefits in terms of confidence, perceived easiness, task enjoyability and perceived usefulness of the visualization. Nevertheless, our backstory narratives did not appear to provide additional subjective benefits. These preliminary findings suggest that narratives may have complex and unanticipated effects, calling for more studies in this area.

Large vertical displays are considered well adapted for collaboration, due to their display surface and the space in front of them that can accommodate multiple people. However, there are few studies that empirically support this assertion, and they do not quantitatively assess the differences of collaboration in front of a shared display compared to a non-shared setup, such as multiple desktops with a common view. In this paper, we compare a large shared vertical display with two desktops, when pairs of users learn to perform a path-planning task. Our results did not indicate a significant difference in learning between the two setups, but found that participants adopted different task strategies. Moreover, while pairs were overall faster with the two desktops, quality was more consistent in the vertical shared display where pairs spent more time communicating, even though there is a-priori more implicit collaboration in this setup.

We evaluate and analyse a framework for evolutionary visual exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional data sets toward two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this article, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clearly predefined task, which examines how users leverage the system and how the system evolves to match their needs. While we previously showed that using EVE, domain experts were able to formulate interesting hypotheses and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating interactive evolutionary computation (IEC) techniques based on user study methodologies.

The attraction effect is a well-studied cognitive bias in decision making research, where one's choice between two alternatives is influenced by the presence of an irrelevant (dominated) third alternative. We examine whether this cognitive bias, so far only tested with three alternatives and simple presentation formats such as numerical tables, text and pictures, also appears in visualizations. Since visualizations can be used to support decision making - e.g., when choosing a house to buy or an employee to hire - a systematic bias could have important implications. In a first crowdsource experiment, we indeed partially replicated the attraction effect with three alternatives presented as a numerical table, and observed similar effects when they were presented as a scatterplot. In a second experiment, we investigated if the effect extends to larger sets of alternatives, where the number of alternatives is too large for numerical tables to be practical. Our findings indicate that the bias persists for larger sets of alternatives presented as scatterplots. We discuss implications for future research on how to further study and possibly alleviate the attraction effect.

We systematically reviewed 64 user-study papers on data glyphs to help researchers and practitioners gain an informed understanding of tradeoffs in the glyph design space. The glyphs we consider are individual representations of multi-dimensional data points, often meant to be shown in small-multiple settings. Over the past 60 years many different glyph designs were proposed and many of these designs have been subjected to perceptual or comparative evaluations. Yet, a systematic overview of the types of glyphs and design variations tested, the tasks under which they were analyzed, or even the study goals and results does not yet exist. In this paper we provide such an overview by systematically sampling and tabulating the literature on data glyph studies, listing their designs, questions, data, and tasks. In addition we present a concise overview of the types of glyphs and their design characteristics analyzed by researchers in the past, and a synthesis of the study results. Based on our meta analysis of all results we further contribute a set of design implications and a discussion on open research directions.

Wall-displays allow multiple users to simultaneously view and analyze large amounts of information, such as the increasingly complex graphs present in domains like biology or social network analysis. We focus on how pairs explore graphs on a touch enabled wall-display using two techniques, both adapted for collaboration: a basic localized selection, and a propagation selection technique that uses the idea of diffusion/transmission from an origin node. We assess in a controlled experiment the impact of selection technique on a shortest path identification task. Pairs consistently divided space even if the task is not spatially divisible, and for the basic selection technique that has a localized visual effect, it led to parallel work that negatively impacted accuracy. The large visual footprint of the propagation technique led to close coordination, improving speed and accuracy for complex graphs only. We then observed the use of propagation on additional graph topology tasks, confirming pair strategies on spatial division and coordination.

Road traffic control centers are of vital importance to modern cities. Interviews with controllers in two such centers identified the need to incorporate the visualization of results from predictive traffic models with real traffic, to help operators choose among different interventions on the network. We explore this idea in a prototype that runs on a wall display, and supports direct touch and input from workstations and mobile devices. Apart from basic functionality to manage the current traffic such as changing traffic light duration or speed limits, the prototype incorporates traffic simulations for forecasting results of possible actions, highlighting their differences to current traffic. Based on needs identified in our interviews, we offer two techniques that visually combine simulated and real situations, taking advantage of the large display space: multiple independent views and DragMagic, a variation of magic lenses. A preliminary laboratory experiment suggests that both techniques are viable design options, even for monitoring several simulations and areas of interest, contrary to expectations from previous work. However DragMagics are easier to master.

We introduce a mobile sketching interface for exploring multi-dimensional datasets on wall displays. We demonstrate the idea of SketchSliders, range sliders that users can freely sketch on a mobile surface to customize their exploration. A small combination of sketches and gestures allows the creation of complex interactive sliders, such as circular sliders for periodic data, slider branches for detailed interaction, and fisheye transformation sliders. We augment sliders with a suite of tools, such as markers, slider cursors, and approximate views of data distributions. Our designs are inspired by a design study with three visualization experts and validated through a user study with six experts using our system. Our findings indicate that our sketching interface accommodates a wide range of exploration strategies, helping users customize as well as focus their visual explorations.

Wall-sized displays can support data visualization and collaboration, but making them interactive is challenging. Smarties allows wall application developers to easily add interactive support to their collaborative applications. It consists of an interface running on touch mobile devices for input, a communication protocol between devices and the wall, and a library that implements the protocol and handles synchronization, locking and input conflicts. The library presents the input as an event loop with callback functions. Each touch mobile has multiple cursor controllers, each associated with keyboards, widgets and clipboards. These controllers can be assigned to specific tasks, are persistent in nature, and can be shared by multiple collaborating users for sharing work. They can control simple cursors on the wall application, or specific content (objects or groups of them). The types of associated widgets are decided by the wall application, making the mobile interface customizable by the wall application it connects to.

Our goal is to enhance navigation in mobile interfaces with quick command gestures that do not make use of explicit mode-switching actions. TilTouch gestures extend the vocabulary of navigation interfaces by combining motion tilt with directional touch. We consider sixteen directional TilTouch gestures that rely on tilt and touch movements along the four main compass directions. An experiment explores their effectiveness for both one-handed and two-handed use. Results identify the best combinations of TilTouch gestures in terms of performance, motor coordination, and user preferences.

We conducted three experiments to investigate the effects of contours on the detection of data similarity with star glyph variations. A star glyph is a small, compact, data graphic that represents a multi-dimensional data point. Star glyphs are often used in small-multiple settings, to represent data points in tables, on maps, or as overlays on other types of data graphics. In these settings, an important task is the visual comparison of the data points encoded in the star glyph, for example to find other similar data points or outliers. We hypothesized that for data comparisons, the overall shape of a star glyph--enhanced through contour lines--would aid the viewer in making accurate similarity judgments. To test this hypothesis, we conducted three experiments. In our first experiment, we explored how the use of contours influenced how visualization experts and trained novices chose glyphs with similar data values. Our results showed that glyphs without contours make the detection of data similarity easier. Given these results, we conducted a second study to understand intuitive notions of similarity. Star glyphs without contours most intuitively supported the detection of data similarity. In a third experiment, we tested the effect of star glyph reference structures (i.e., tickmarks and gridlines) on the detection of similarity. Surprisingly, our results show that adding reference structures does improve the correctness of similarity judgments for star glyphs with contours, but not for the standard star glyph. As a result of these experiments, we conclude that the simple star glyph without contours performs best under several criteria, reinforcing its practice and popularity in the literature. Contours seem to enhance the detection of other types of similarity, e. g., shape similarity and are distracting when data similarity has to be judged. Based on these findings we provide design considerations regarding the use of contours and reference structures on star glyphs.

Abstract We present an Evolutionary Visual Exploration (EVE) system that combines visual analytics with stochastic optimisation to aid the exploration of multidimensional datasets characterised by a large number of possible views or projections. Starting from dimensions whose values are automatically calculated by a PCA, an interactive evolutionary algorithm progressively builds (or evolves) non‐trivial viewpoints in the form of linear and non‐linear dimension combinations, to help users discover new interesting views and relationships in their data. The criteria for evolving new dimensions is not known a priori and are partially specified by the user via an interactive interface: (i) The user selects views with meaningful or interesting visual patterns and provides a satisfaction score. (ii) The system calibrates a fitness function (optimised by the evolutionary algorithm) to take into account the user input, and then calculates new views. Our method leverages automatic tools to detect interesting visual features and human interpretation to derive meaning, validate the findings and guide the exploration without having to grasp advanced statistical concepts. To validate our method, we built a prototype tool (EvoGraphDice) as an extension of an existing scatterplot matrix inspection tool, and conducted an observational study with five domain experts. Our results show that EvoGraphDice can help users quantify qualitative hypotheses and try out different scenarios to dynamically transform their data. Importantly, it allowed our experts to think laterally, better formulate their research questions and build new hypotheses for further investigation.

We present a first investigation into hybrid-image visualization for data analysis in large-scale viewing environments. Hybrid-image visualizations blend two different visual representations into a single static view, such that each representation can be perceived at a different viewing distance. Our work is motivated by data analysis scenarios that incorporate one or more displays with sufficiently large size and resolution to be comfortably viewed by different people from various distances. Hybrid-image visualizations can be used, in particular, to enhance overview tasks from a distance and detail-in-context tasks when standing close to the display. By using a perception-based blending approach, hybrid-image visualizations make two full-screen visualizations accessible without tracking viewers in front of a display. We contribute a design space, discuss the perceptual rationale for our work, provide examples, and introduce a set of techniques and tools to aid the design of hybrid-image visualizations.

Annotations have been identified as an important aid in analysis record-keeping and recently data discovery. In this paper we discuss the use of annotations on visualization dashboards, with a special focus on business intelligence (BI) analysis. In-depth interviews with experts lead to new annotation needs for multi-chart visualization systems, on which we based the design of a dashboard prototype that supports data and context aware annotations. We focus particularly on novel annotation aspects, such as multi-target annotations, annotation transparency across charts and data dimension levels, as well as annotation properties such as lifetime and validity. Moreover, our prototype is built on a data layer shared among different data-sources and BI applications, allowing cross application annotations. We discuss challenges in supporting context aware annotations in dashboards and other visualizations, such as dealing with changing annotated data, and provide design solutions. Finally we report reactions and recommendations from a different set of expert users.

We present the results of two user studies on the perception of visual variables on tiled high-resolution wall-sized displays. We contribute an understanding of, and indicators predicting how, large variations in viewing distances and viewing angles affect the accurate perception of angles, areas, and lengths. Our work, thus, helps visualization researchers with design considerations on how to create effective visualizations for these spaces. The first study showed that perception accuracy was impacted most when viewers were close to the wall but differently for each variable (Angle, Area, Length). Our second study examined the effect of perception when participants could move freely compared to when they had a static viewpoint. We found that a far but static viewpoint was as accurate but less time consuming than one that included free motion. Based on our findings, we recommend encouraging viewers to stand further back from the display when conducting perception estimation tasks. If tasks need to be conducted close to the wall display, important information should be placed directly in front of the viewer or above, and viewers should be provided with an estimation of the distortion effects predicted by our work-or encouraged to physically navigate the wall in specific ways to reduce judgement error.

We report on results of a series of user studies on the perception of four visual variables that are commonly used in the literature to depict uncertainty. To the best of our knowledge, we provide the first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives. In addition to blur, dashing and grayscale, we investigate the use of `sketchiness' as a visual variable because it conveys visual impreciseness that may be associated with data quality. Inspired by work in non-photorealistic rendering and by the features of hand-drawn lines, we generate line trajectories that resemble hand-drawn strokes of various levels of proficiency-ranging from child to adult strokes-where the amount of perturbations in the line corresponds to the level of uncertainty in the data. Our results show that sketchiness is a viable alternative for the visualization of uncertainty in lines and is as intuitive as blur; although people subjectively prefer dashing style over blur, grayscale and sketchiness. We discuss advantages and limitations of each technique and conclude with design considerations on how to deploy these visual variables to effectively depict various levels of uncertainty for line marks.

Animated transitions are popular in many visual applications but they can be difficult to follow, especially when many objects move at the same time. One informal design guideline for creating effective animated transitions has long been the use of slow-in/slow-out pacing, but no empirical data exist to support this practice. We remedy this by studying object tracking performance under different conditions of temporal distortion, i.e., constant speed transitions, slow-in/slow-out, fast-in/fast-out, and an adaptive technique that slows down the visually complex parts of the animation. Slow-in/slow-out outperformed other techniques, but we saw technique differences depending on the type of visual transition.

Traditional software in Formal Concept Analysis makes little use of visualization techniques, producing poorly readable concept lattice representations when the number of concepts exceeds a few dozens. This is problematic as the number of concepts in such lattices grows significantly with the size of the data and the number of its dimensions. In this work we propose several methods to enhance the readability of concept lattices firstly though colouring and distortion techniques, and secondly by extracting and visualizing trees derived from concept lattice structures. These contributions represent an important step in the visual analysis of conceptual structures, as domain experts may visually explore larger datasets that traditional visualizations of concept lattice cannot represent effectively.

We present the results of a user study that compares different ways of representing Dual-Scale data charts. Dual-Scale charts incorporate two different data resolutions into one chart in order to emphasize data in regions of interest or to enable the comparison of data from distant regions. While some design guidelines exist for these types of charts, there is currently little empirical evidence on which to base their design. We fill this gap by discussing the design space of Dual-Scale cartesian-coordinate charts and by experimentally comparing the performance of different chart types with respect to elementary graphical perception tasks such as comparing lengths and distances. Our study suggests that cut-out charts which include collocated full context and focus are the best alternative, and that superimposed charts in which focus and context overlap on top of each other should be avoided.

This article examines the benefits of using text animated transitions for navigating in the revision history of textual documents. We propose an animation technique for smoothly transitioning between different text revisions, then present the Diffamation system. Diffamation supports rapid exploration of revision histories by combining text animated transitions with simple navigation and visualization tools. We finally describe a user study showing that smooth text animation allows users to track changes in the evolution of textual documents more effectively than flipping pages.

Abstract Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors. GraphDice builds upon the ScatterDice system for its main multidimensional navigation paradigm, and extends it with novel mechanisms to support network exploration in general and SNA tasks in particular. Novel mechanisms include visualization of attributes of interval type and projection of numerical edge attributes to node attributes. We show how these extensions to the original ScatterDice system allow to support complex visual analysis tasks on networks with hundreds of actors and up to 30 attributes, while providing a simple and consistent interface for interacting with network data.

GeneaQuilts is a new visualization technique for representing large genealogies of up to several thousand individuals. The visualization takes the form of a diagonally-filled matrix, where rows are individuals and columns are nuclear families. After identifying the major tasks performed in genealogical research and the limits of current software, we present an interactive genealogy exploration system based on GeneaQuilts. The system includes an overview, a timeline, search and filtering components, and a new interaction technique called Bring & Slide that allows fluid navigation in very large genealogies. We report on preliminary feedback from domain experts and show how our system supports a number of their tasks.

This paper explores the nature of interfaces to support people in accessing their files at tabletop displays embedded in the environment. To do this, we designed a study comparing people's interaction with two very different classes of file system access interface: Focus, explicitly designed for tabletops, and the familiar hierarchical Windows Explorer. In our within-subjects double-crossover study, participants collaborated on 4 planning tasks. Based on video, logs, questionnaires and interviews, we conclude that both classes of interface have a place. Notably, Focus contributed to improved collaboration and more efficient use of the workspace than with Explorer. Our results inform a set of recommendations for future interfaces enabling this important class of interaction -- supporting access to files for collaboration at tabletop devices embedded in an ubicomp environment.

Exploring communities is an important task in social network analysis. Such communities are currently identified using clustering methods to group actors. This approach often leads to actors belonging to one and only one cluster, whereas in real life a person can belong to several communities. As a solution we propose duplicating actors in social networks and discuss potential impact of such a move. Several visual duplication designs are discussed and a controlled experiment comparing network visualization with and without duplication is performed, using 6 tasks that are important for graph readability and visual interpretation of social networks. We show that in our experiment, duplications significantly improve community-related tasks but sometimes interfere with other graph readability tasks. Finally, we propose a set of guidelines for deciding when to duplicate actors and choosing candidates for duplication, and alternative ways to render them in social network representations.

Managing large amounts of dynamic visual information involves understanding changes happening out of the user's sight. In this paper, we show how current software does not adequately support users in this task, and motivate the need for a more general approach. We propose an image-based storage, visualization, and implicit interaction paradigm called mnemonic rendering that provides better support for handling visual changes. Once implemented on a system, mnemonic rendering techniques can benefit all applications. We explore its rich design space and discuss its expected benefits as well as limitations based on feedback from users of a small-screen and a wall-size prototype.

We present the design and evaluation of the vacuum, a new interaction technique that enables quick access to items on areas of a large display that are difficult for a user to reach without significant physical movement. The vacuum is a circular widget with a user controllable arc of influence that is centered at the widget's point of invocation and spans out to the edges of the display. Far away objects residing inside this influence arc are brought closer to the widget's centre in the form of proxies that can be manipulated in lieu of the original. We conducted two experiments which compare the vacuum to direct picking and an existing technique called drag-and-pick [2]. Results show that the vacuum outperforms existing techniques when selecting multiple targets in a sequence, performs similarly to existing techniques when selecting single targets located moderately far away, and slightly worse with single targets located very far away in the presence of distracter targets along the path.