We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation.

Density plots effectively summarize large numbers of points, which would otherwise lead to severe overplotting in, for example, a scatter plot. However, when applied to line-based datasets, such as trajectories or time series, density plots alone are insufficient, as they disrupt path continuity, obscuring smooth trends and rare anomalies. We propose a bin-based illumination model that decouples structure from density to enhance flow and reveal sparse outliers while preserving the original colormap. We introduce a bin-based outlierness metric to rank trajectories. Guided by this ranking, we construct a structural normal map and apply locally-adaptive lighting in the luminance channel to highlight chosen patterns-from dominant trends to atypical paths-with acceptable color distortion. Our interactive method enables analysts to prioritize main trends, focus on outliers, or strike a balance between the two. We demonstrate our method on several real-world datasets, showing it reveals details missed by simpler alternatives, achieves significantly lower CIEDE2000 color distortion than standard shading, and supports interactive updates for up to 10,000 lines.

Diversity in image generation is essential to ensure fair representations and support creativity in ideation. Hence, many text-to-image models have implemented diversification mechanisms. Yet, after a few iterations of generation, a lack of diversity becomes apparent, because each user has their own diversity goals (e.g., different colors, brands of cars), and there are diverse attributions to be specified. To support user-driven diversity control, we propose Varif.ai that employs text-to-image and Large Language Models to iteratively i) (re)generate a set of images, ii) verify if user-specified attributes have sufficient coverage, and iii) vary existing or new attributes. Through an elicitation study, we uncovered user needs for diversity in image generation. A pilot validation showed that Varif.ai made achieving diverse image sets easier. In a controlled evaluation with 20 participants, Varif.ai proved more effective than baseline methods across various scenarios. Thus, this supports user control of diversity in image generation for creative ideation and scalable image generation.

Data-visualizations are now commonly used in online press articles which often supports engaging data-driven stories. However, due to its visual nature, this type of content inherently lacks accessibility (e.g. when one wants to consume those visualizations using conversational agents, hearing them in audible formats, or using screen reader). Writing alternative texts is the recommended standard in order to provide text descriptions associated with an image. However, newsrooms rarely produce them for data-visualizations, or when they do, these are overly simplistic. Several intertwined limitations explain that situation like the limited amount of time journalists have to produce these expected detailed descriptions or the lack of precise and standardized writing guidelines for describing visualizations. To address this issue, we propose a new approach to help journalists generate descriptions of data-visualizations, based on a set of generated question and answer pairs (hereafter referred to as Q/A). Due to the previously enumerated limitations, our method first generates those Q/As using a generative AI model of Natural Language Processing (NLP). This approach alleviates and homogenizes the writing task workload and allows for a systematic and more exhaustive exploration of the possible Q/As for a given visualization. However, among the critical challenges of using AI-based generative tools in a journalism context is the risk of publishing unreliable or biased information. Therefore, the methodology proposed in this paper gives the journalist user a high level of control over the AI-generated Q/As. To enable and optimize this mandatory validation task, we design an interface where Q/As are grouped in terms of semantic and textual content, and accessibility interest. Visual cues are also displayed to improve the journalist’s decision-making. To evaluate this proposed methodology, that we call GenQA, we conducted a comparative design study that gathered journalists from two different Canadian newsrooms and teachers. We observed that GenQA was efficiently used by those users and helped them to produce detailed visualization descriptions that met their expectations in terms of quality and workload. This study also showed that GenQA triggered significant serendipity potential, allowing users to explore and produce Q/As that cover aspects they might not have considered.

The rise of multitasking in contemporary lifestyles has positioned audio-first content as an essential medium for information consumption. We present AudioXtend, an approach to augment audiobook experiences during daily tasks by integrating glanceable, AI-generated visuals through optical see-through head-mounted displays (OHMDs). Our initial study showed that these visual augmentations not only preserved users’ primary task efficiency but also dramatically enhanced immediate auditory content recall by 33.3% and 7-day recall by 32.7%, alongside a marked improvement in narrative engagement. Through participatory design workshops involving digital arts designers, we crafted a set of design principles for visual augmentations that are attuned to the requirements of multitaskers. Finally, a 3-day take-home field study further revealed new insights for everyday use, underscoring the potential of assisted reality (aR) to enhance heads-up listening and incidental learning experiences.

Artificial Intelligence (AI) has recently made significant advancements and is now pervasive across various application domains. This holds true for Air Transportation as well, where AI is increasingly involved in decision-making processes. While these algorithms are designed to assist users in their daily tasks, they still face challenges related to acceptance and trustworthiness. Users often harbor doubts about the decisions proposed by AI, and in some cases, they may even oppose them. This is primarily because AI-generated decisions are often opaque, non-intuitive, and incompatible with human reasoning. Moreover, when AI is deployed in safety-critical contexts like Air Traffic Management (ATM), the individual decisions generated by AI models must be highly reliable for human operators. Understanding the behavior of the model and providing explanations for its results are essential requirements in every life-critical domain. In this scope, this project aimed to enhance transparency and explainability in AI algorithms within the Air Traffic Management domain. This article presents the results of the project’s validation conducted for a Conflict Detection and Resolution task involving 21 air traffic controllers (10 experts and 11 students) in En-Route position (i.e. hight altitude flight management). Through a controlled study incorporating three levels of explanation, we offer initial insights into the impact of providing additional explanations alongside a conflict resolution algorithm to improve decision-making. At a high level, our findings indicate that providing explanations is not always necessary, and our project sheds light on potential research directions for education and training purposes.

This article explores how the ability to recall information in data visualizations depends on the presentation technology. Participants viewed 10 Isotype visualizations on a 2D screen, in 3D, in Virtual Reality (VR) and in Mixed Reality (MR). To provide a fair comparison between the three 3D conditions, we used LIDAR to capture the details of the physical rooms, and used this information to create our textured 3D models. For all environments, we measured the number of visualizations recalled and their order (2D) or spatial location (3D, VR, MR). We also measured the number of syntactic and semantic features recalled. Results of our study show increased recall and greater richness of data understanding in the MR condition. Not only did participants recall more visualizations and ordinal/spatial positions in MR, but they also remembered more details about graph axes and data mappings, and more information about the shape of the data. We discuss how differences in the spatial and kinesthetic cues provided in these different environments could contribute to these results, and reasons why we did not observe comparable performance in the 3D and VR conditions.

Forensic practitioners analyse intrinsic 3D data daily on 2D screens. We explore novel immersive visualisation techniques that enable digital autopsy through analysis of 3D imagery. We employ a user-centred design process involving four rounds of user feedback: (1) formative interviews eliciting opportunities and requirements for mixed-reality digital autopsies; (2) a larger workshop identifying our prototype’s limitations and further use-cases and interaction ideas; (3+4) two rounds of qualitative user validation of successive prototypes of novel interaction techniques for pathologist sensemaking. Overall, we find MR holds great potential to enable digital autopsy, initially to supplement physical autopsy, but ultimately to replace it. We found that experts were able to use our tool to perform basic virtual autopsy tasks, MR setup promotes exploration and sense making of cause of death, and subject to limitations of current MR technology, the proposed system is a valid option for digital autopsies, according to experts’ feedback.

We present a novel, web-based visual eye-tracking analytics tool called Gazealytics. Our open-source toolkit features a unified combination of gaze analytics features that support flexible exploratory analysis, along with annotation of areas of interest (AOI) and filter options based on multiple criteria to visually analyse eye tracking data across time and space. Gazealytics features coordinated views unifying spatiotemporal exploration of fixations and scanpaths for various analytical tasks. A novel matrix representation allows analysis of relationships between such spatial or temporal features. Data can be grouped across samples, user-defined AOIs or time windows of interest (TWIs) to support aggregate or filtered analysis of gaze activity. This approach exceeds the capabilities of existing systems by supporting flexible comparison between and within subjects, hypothesis generation, data analysis and communication of insights. We demonstrate in a walkthrough that Gazealytics supports multiple types of eye tracking datasets and analytical tasks.

We present Target Netgrams as a visualization technique for radial layouts of graphs. Inspired by manually created target sociograms, we propose an annulus-constrained stress model that aims to position nodes onto the annuli between adjacent circles for indicating their radial hierarchy, while maintaining the network structure (clusters and neighborhoods) and improving readability as much as possible. This is achieved by having more space on the annuli than traditional layout techniques. By adapting stress majorization to this model, the layout is computed as a constrained least square optimization problem. Additional constraints (e.g., parent-child preservation, attribute-based clusters and structure-aware radii) are provided for exploring nodes, edges, and levels of interest. We demonstrate the effectiveness of our method through a comprehensive evaluation, a user study, and a case study.

Eye tracking is growing in popularity for multiple application areas, yet analysing and exploring the large volume of complex data remains difficult for most users. We present a comprehensive eye tracking visual analytics system to enable the exploration and presentation of eye-tracking data across time and space in an efficient manner. The application allows the user to gain an overview of general patterns and perform deep visual analysis of local gaze exploration. The ability to link directly to the video of the underlying scene allows the visualisation insights to be verified on the fly. The system was motivated by the need to analyse eye-tracking data collected from an ‘in the wild’ study with energy network operators and has been further evaluated via interviews with 14 eye-tracking experts in multiple domains. Results suggest that, thanks to state-of-the-art visualisation techniques and by providing context with videos, our system could enable an improved analysis of eye-tracking data through interactive exploration, facilitating comparison between different participants or conditions, thus enhancing the presentation of complex data analysis to non-experts. This research paper provides four contributions: (1) analysis of a motivational use case demonstrating the need for rich visual-analytics workflow tools for eye-tracking data; (2) a highly dynamic system to visually explore and present complex eye-tracking data; (3) insights from our applied use case evaluation and interviews with experienced users demonstrating the potential for the system and visual analytics for the wider eye-tracking community.

Infographics range from minimalism that aims to convey the raw data to elaborately decorated, or embellished, graphics that aim to engage readers by telling a story. Several studies have shown evidence to negative, but also positive, effects on embellishments. We conducted a set of experiments to gauge more precisely how embellishments affect how people relate to infographics and make sense of the conveyed story. By analyzing questionnaires, interviews, and eye-tracking data simplified by bundling, we show that, within bounds, embellishments have a positive effect on how users get engaged in understanding an infographic, with very limited downside. To our knowledge, our work is the first that fuses the aforementioned three information sources to understand infographics. Our findings can help to design more fine-grained studies to quantify the effects of embellishments and also to design infographics that effectively use the embellishments’ positive aspects identified. I think the contribution does not appear well in the abstract. It’s not just that the visual embellishments are positive. We show a methodology that allows us to see what these effects are (in addition to engagement, memorization and recall) at several levels (scales, interviews, eye tracking) which are therefore physiological and emotional. We could include this idea in the abstract?

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.

Much of the visualization literature focuses on assessment of visual representations with regard to their effectiveness for understanding data. In the present work, we instead focus on making data visualization experiences more enjoyable, to foster deeper engagement with data. We investigate two strategies to make visualization experiences more enjoyable and engaging: personalization, and immersion. We selected pictographs (composed of multiple data glyphs) as this representation affords creative freedom, allowing people to craft symbolic or whimsical shapes of personal significance to represent data. We present the results of a qualitative study with 12 participants crafting pictographs using a large pen-enabled device and while immersed within a VR environment. Our results indicate that personalization and immersion both have positive impact on making visualizations more enjoyable experiences.

Brushing techniques have a long history with the first interactive selection tools appearing in the 1990s. Since then, many additional techniques have been developed to address selection accuracy, scalability and flexibility issues. Selection is especially difficult in large datasets where many visual items tangle and create overlapping. Existing techniques rely on trial and error combined with many view modifications such as panning, zooming, and selection refinements. For moving object analysis, recorded positions are connected into line segments forming trajectories and thus creating more occlusions and overplotting. As a solution for selection in cluttered views, this paper investigates a novel brushing technique which not only relies on the actual brushing location but also on the shape of the brushed area. The process can be described as follows. Firstly, the user brushes the region where trajectories of interest are visible (standard brushing technique). Secondly, the shape of the brushed area is used to select similar items. Thirdly, the user can adjust the degree of similarity to filter out the requested trajectories. This brushing technique encompasses two types of comparison metrics, the piecewise Pearson correlation and the similarity measurement based on information geometry. To show the efficiency of this novel brushing method, we apply it to concrete scenarios with datasets from air traffic control, eye tracking, and GPS trajectories.

Abstract 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.

Extended Reality (XR) systems (which encapsulate AR, VR and MR) is an emerging field which enables the development of novel visualization and interaction techniques. To develop and to assess such techniques, researchers and designers have to face choices in terms of which development tools to adopt, and with very little information about how such tools support some of the very basic tasks for information visualization, such as selecting data items, linking and navigating. As a solution, we propose Flex-ER, a flexible web-based environment that enables users to prototype, debug and share experimental conditions and results. Flex-ER enables users to quickly switch between hardware platforms and input modalities by using a JSON specification that supports both defining interaction techniques and tasks at a low cost. We demonstrate the flexibility of the environment through three task design examples: brushing, linking and navigating. A qualitative user study suggest that Flex-ER can be helpful to prototype and explore different interaction techniques for immersive analytics.

We present a new technique to enable the creation of shape-bounded Wordles, we call ShapeWordle, in which we fit words to form a given shape. To guide word placement within a shape, we extend the traditional Archimedean spirals to be shape-aware by formulating the spirals in a differential form using the distance field of the shape. To handle non-convex shapes, we introduce a multi-centric Wordle layout method that segments the shape into parts for our shape-aware spirals to adaptively fill the space and generate word placements. In addition, we offer a set of editing interactions to facilitate the creation of semantically-meaningful Wordles. Lastly, we present three evaluations: a comprehensive comparison of our results against the state-of-the-art technique (WordArt), case studies with 14 users, and a gallery to showcase the coverage of our technique.

Many edge bundling techniques (i.e., data simplification as a support for data visualization and decision making) exist but they are not directly applicable to any kind of dataset and their parameters are often too abstract and difficult to set up. As a result, this hinders the user ability to create efficient aggregated visualizations. To address these issues, we investigated a novel way of handling visual aggregation with a task-driven and user-centered approach. Given a graph, our approach produces a decluttered view as follows: first, the user investigates different edge bundling results and specifies areas, where certain edge bundling techniques would provide user-desired results. Second, our system then computes a smooth and structural preserving transition between these specified areas. Lastly, the user can further fine-tune the global visualization with a direct manipulation technique to remove the local ambiguity and to apply different visual deformations. In this paper, we provide details for our design rationale and implementation. Also, we show how our algorithm gives more suitable results compared to current edge bundling techniques, and in the end, we provide concrete instances of usages, where the algorithm combines various edge bundling results to support diverse data exploration and visualizations.

We investigate the smooth pursuit eye movement based interaction using an unmodified off-the-shelf RGB camera. In each pair of sequential video frames, we compute the indicative direction of the eye movement by analyzing flow vectors obtained using the Lucas-Kanade optical flow algorithm. We discuss how carefully selected low vectors could replace the traditional pupil centers detection in smooth pursuit interaction. We examine implications of unused features in the eye camera imaging frame as potential elements for detecting gaze gestures. This simple approach is easy to implement and abstains from many of the complexities of pupil based approaches. In particular, EyeFlow does not call for either a 3D pupil model or 2D pupil detection to track the pupil center location. We compare this method to state-of-the-art approaches and ind that this can enable pursuit interactions with standard cameras. Results from the evaluation with 12 users data yield an accuracy that compares to previous studies. In addition, the benefit of this work is that the approach does not necessitate highly matured computer vision algorithms and expensive IR-pass cameras.

The virtual reality (VR) has nowadays numerous applications in training, education, and rehabilitation. To efficiently present the immersive 3D stimuli, we need to understand how spatial attention is oriented in VR. The efficiency of different cues can be compared using the Posner paradigm. In this study, we designed an ecological environment where participants were presented with a modified version of the Posner cueing paradigm. Twenty subjects equipped with an eye-tracking system and VR HMD performed a sandwich preparation task. They were asked to assemble the ingredients which could be either endogenously and exogenously cued in both auditory and visual modalities. The results showed that all valid cues made participants react faster. While directional arrow (visual endogenous) and 3D sound (auditory exogenous) oriented attention globally to the entire cued hemifield, the vocal instruction (auditory endogenous) and object highlighting (visual exogenous) allowed more local orientation, in a specific region of space. No differences in gaze shift initiation nor time to fixate the target were found suggesting the covert orienting.

We investigate the mechanisms of attentional orienting in a 360-degree virtual environments. Through the use of Posner's paradigm, we study the effects of different attentional guidance techniques designed to improve information processing. The most efficient technique will be applied to a procedure learning tool in virtual reality and a remote air traffic control tower. The eye-tracker allows us to explore the differential effects of overt and covert orienting, to estimate the effectiveness of visual research and to use it as a technique for interaction in virtual reality.

We introduce IATK, the Immersive Analytics Toolkit, a software package for Unity that allows interactive authoring and exploration of data visualisation in immersive environments. The design of IATK was informed by interdisciplinary expert-collaborations as well as visual analytics applications and iterative refinement over several years. IATK allows for easy assembly of visualisations through a grammar of graphics that a user can configure in a GUI-in addition to a dedicated visualisation API that supports the creation of novel immersive visualisation designs and interactions. IATK is designed with scalability in mind, allowing visualisation and fluid responsive interactions in the order of several million points at a usable frame rate. This paper outlines our design requirements, IATK's framework design and technical features, its user interface, as well as application examples.

Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.

Occlusion is an issue in volumetric visualization as it prevents direct visualization of the region of interest. While many techniques such as transfer functions, volume segmentation or view distortion have been developed to address this, there is still room for improvement to better support the understanding of objects' vicinity. However, most existing Focus+Context fail to solve partial occlusion in datasets where the target and the occluder are very similar density-wise. For these reasons, we investigate a new technique which maintains the general structure of the investigated volumetric dataset while addressing occlusion issues. With our technique, the user interactively defines an area of interest where an occluded region or object is partially visible. Then our lens starts pushing at its border occluding objects, thus revealing hidden volumetric data. Next, the lens is modified with an extended field of view (fish-eye deformation) to better see the vicinity of the selected region. Finally, the user can freely explore the surroundings of the area under investigation within the lens. To provide real-time exploration, we implemented our lens using a GPU accelerated ray-casting framework to handle ray deformations, local lighting, and local viewpoint manipulation. We illustrate our technique with five application scenarios in baggage inspection, 3D fluid flow visualization, chest radiology, air traffic planning, and DTI fiber exploration.

We present PathWord (PATH passWORD), a multimodal digit entry method for ad-hoc authentication based on known digits shape and user relative eye movements. PathWord is a touch-free, gaze-based input modality, which attempts to decrease shoulder surfing attacks when unlocking a system using PINs. The system uses a modified web camera to detect the user's eye. This enables suppressing direct touch, making it difficult for passer-bys to be aware of the input digits, thus reducing shoulder surfing and smudge attacks. In addition to showing high accuracy rates (Study 1: 87.1% successful entries) and strong confidentiality through detailed evaluations with 42 participants (Study 2), we demonstrate how PathWord considerably diminishes the potential of stolen passwords (on average 2.38% stolen passwords with PathWord vs. over 90% with traditional PIN screen). We show use-cases of PathWord and discuss its advantages over traditional input modalities. We envision PathWord as a method to foster confidence while unlocking a system through gaze gestures.

Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization.

Edge bundling techniques provide a visual simplification of cluttered graph drawings or trail sets. While many bundling techniques exist, only few recent ones can handle large datasets and also allow selective bundling based on edge attributes. We present a new technique that improves on both above points, in terms of increasing both the scalability and computational speed of bundling, while keeping the quality of the results on par with state-of-the-art techniques. For this, we shift the bundling process from the image space to the spectral (frequency) space, thereby increasing computational speed. We address scalability by proposing a data streaming process that allows bundling of extremely large datasets with limited GPU memory. We demonstrate our technique on several real-world datasets and by comparing it with state-of-the-art bundling methods.

Cardiography, quantitative measurement of the functioning of the heart, traditionally requires customized obtrusive contact sensors. Using new methods photoplethysmography and ballistocardiography signals can be captured using ubiquitous sensors, such as webcams and accelerometers. However, these signals are not visible to the unaided eye. We present Cardiolens - a mixed reality system that enables real-time, hands-free measurement and visualization of blood flow and vital signs from multiple people. The system combines a front-facing webcam, imaging ballistocardiography, and remote imaging photoplethysmography methods for recovering pulse signals. A heads up display allows users to view their own heart rate whenever they are wearing the device and the heart rate and heart rate variability of another person simply by looking at them. Cardiolens provides the wearer with a new way to understand physiological signals and has applications in human-computer interaction and in the study of social psychology.

Abstract We present thegeneralized space‐time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space‐time cube operations and explain how these operations can be combined and parameterized. The generalized space‐time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo‐spatial, videos, networks, multivariate data). The proper choice of space‐time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space‐time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bachet al.'s (2014) work.

Abstract Bundling techniques provide a visual simplification of a graph drawing or trail set, by spatially grouping similar graph edges or trails. This way, the structure of the visualization becomes simpler and thereby easier to comprehend in terms of assessing relations that are encoded by such paths, such as finding groups of strongly interrelated nodes in a graph, finding connections between spatial regions on a map linked by a number of vehicle trails, or discerning the motion structure of a set of objects by analyzing their paths. In this state of the art report, we aim to improve the understanding of graph and trail bundling via the following main contributions. First, we propose a data‐based taxonomy that organizes bundling methods on the type of data they work on (graphs vs trails, which we refer to as paths). Based on a formal definition of path bundling, we propose a generic framework that describes the typical steps of all bundling algorithms in terms of high‐level operations and show how existing method classes implement these steps. Next, we propose a description of tasks that bundling aims to address. Finally, we provide a wide set of example applications of bundling techniques and relate these to the above‐mentioned taxonomies. Through these contributions, we aim to help both researchers and users to understand the bundling landscape as well as its technicalities.

In this paper, we investigate Confluent Drawings (CD), a technique for bundling edges in node-link diagrams based on network connectivity. Edge-bundling techniques are designed to reduce edge clutter in node-link diagrams by coalescing lines into common paths or bundles. Unfortunately, traditional bundling techniques introduce ambiguity since edges are only bundled by spatial proximity, rather than network connectivity; following an edge from its source to its target can lead to the perception of incorrect connectivity if edges are not clearly separated within the bundles. Contrary, CDs bundle edges based on common sources or targets. Thus, a smooth path along a confluent bundle indicates precise connectivity. While CDs have been described in theory, practical investigation and application to real-world networks (i.e., networks beyond those with certain planarity restrictions) is currently lacking. Here, we provide the first algorithm for constructing CDs from arbitrary directed and undirected networks and present a simple layout method, embedded in a sand box environment providing techniques for interactive exploration. We then investigate patterns and artifacts in CDs, which we compare to other common edge-bundling techniques. Finally, we present the first user study that compares edge-compression techniques, including CD, power graphs, metro-style, and common edge bundling. We found that users without particular expertise in visualization or network analysis are able to read small CDs without difficulty. Compared to existing bundling techniques, CDs are more likely to allow people to correctly perceive connectivity.

Animations are commonplace in today's user interfaces. From bouncing icons that catch attention, to transitions helping with orientation, to tutorials, animations can serve numerous purposes. We revisit Baecker and Small's pioneering work Animation at the Interface, 25 years later. We reviewed academic publications and commercial systems, and interviewed 20 professionals of various backgrounds. Our insights led to an expanded set of roles played by animation in interfaces today for keeping in context, teaching, improving user experience, data encoding and visual discourse. We illustrate each role with examples from practice and research, discuss evaluation methods and point to opportunities for future research. This expanded description of roles aims at inspiring the HCI research community to find novel uses of animation, guide them towards evaluation and spark further research.

Visualization of the trajectories of moving objects leads to dense and cluttered images, which hinders exploration and understanding. It also hinders adding additional visual information, such as direction, and makes it difficult to interactively extract traffic flows, i.e., subsets of trajectories. In this paper we present our approach to visualize traffic flows and provide interaction tools to support their exploration. We show an overview of the traffic using a density map. The directions of traffic flows are visualized using a particle system on top of the density map. The user can extract traffic flows using a novel selection widget that allows for the intuitive selection of an area, and filtering on a range of directions and any additional attributes. Using simple, visual set expressions, the user can construct more complicated selections. The dynamic behaviors of selected flows may then be shown in annotation windows in which they can be interactively explored and compared. We validate our approach through use cases where we explore and analyze the temporal behavior of aircraft and vessel trajectories, e.g., landing and takeoff sequences, or the evolution of flight route density. The aircraft use cases have been developed and validated in collaboration with domain experts.

Edge bundling methods reduce visual clutter of dense and occluded graphs. However, existing bundling techniques either ignore edge properties such as direction and data attributes, or are otherwise computationally not scalable, which makes them unsuitable for tasks such as exploration of large trajectory datasets. We present a new framework to generate bundled graph layouts according to any numerical edge attributes such as directions, timestamps or weights. We propose a GPU-based implementation linear in number of edges, which makes our algorithm applicable to large datasets. We demonstrate our method with applications in the analysis of aircraft trajectory datasets and eye-movement traces.

Data videos, motion graphics that incorporate visualizations about facts, are increasingly gaining popularity as a means of telling stories with data. However, very little is systematically recorded about (a) what elements are featured in data videos and (b) the processes used to create them. In this article, we provide initial insights to build this knowledge. We first report on a qualitative analysis of 50 professionally designed data videos, extracting and exposing their most salient constituents. Second, we report on a series of workshops with experienced storytellers from cinematography, graphics design and screenplay writing. We provided them with a set of data facts and visualizations and observed them create storyboards for data videos. From these exploratory studies, we derive broader implications for the design of an authoring tool to enable a wide audience to create data videos. Our findings highlight the importance of providing a flexible tool supporting a non-linear creation process and allowing users to iteratively go back to different phases of the process.

Interactive data exploration and manipulation are often hindered by dataset sizes. For 3D data, this is aggravated by occlusion, important adjacencies, and entangled patterns. Such challenges make visual interaction via common filtering techniques hard. We describe a set of realtime multi-dimensional data deformation techniques that aim to help users to easily select, analyze, and eliminate spatial-and-data patterns. Our techniques allow animation between view configurations, semantic filtering and view deformation. Any data subset can be selected at any step along the animation. Data can be filtered and deformed to reduce occlusion and ease complex data selections. Our techniques are simple to learn and implement, flexible, and real-time interactive with datasets of tens of millions of data points. We demonstrate our techniques on three domain areas: 2D image segmentation and manipulation, 3D medical volume exploration, and astrophysical exploration.

Depicting change captured by dynamic graphs and temporal paths, or trails, is hard. We present two techniques for simplified visualization of such data sets using edge bundles. The first technique uses an efficient image-based bundling method to create smoothly changing bundles from streaming graphs. The second technique adds edge-correspondence data atop of any static bundling algorithm, and is best suited for graph sequences. We show how these techniques can produce simplified visualizations of streaming and sequence graphs. Next, we show how several temporal attributes can be added atop of our dynamic graphs. We illustrate our techniques with data sets from aircraft monitoring, software engineering, and eye-tracking of static and dynamic scenes.

Dynamic graphs are increasingly pervasive in modern information systems. However, understanding how a graph changes in time is difficult. We present here two techniques for simplified visualization of dynamic graphs using edge bundles. The first technique uses a recent image-based graph bundling method to create smoothly changing bundles from streaming graphs. The second technique incorporates additional edge-correspondence data and is thereby suited to visualize discrete graph sequences. We illustrate our methods with examples from real-world large dynamic graph datasets.

We reflect upon the design of a paper-based tangible interactive space to support air traffic control. We have observed, studied, prototyped and discussed with controllers a new mixed interaction system based on Anoto, video projection, and tracking. Starting from the understanding of the benefits of tangible paper strips, our goal is to study how mixed physical and virtual augmented data can support the controllers' mental work. The context of the activity led us to depart from models that are proposed in tangible interfaces research where coherence is based on how physical objects are representative of virtual objects. We propose a new account of coherence in a mixed interaction system that integrates externalization mechanisms. We found that physical objects play two roles: they act both as representation of mental objects and as tangible artifacts for interacting with augmented features. We observed that virtual objects represent physical ones, and not the reverse, and, being virtual representations of physical objects, should seamlessly converge with the cognitive role of the physical object. Finally, we show how coherence is achieved by providing a seamless interactive space.

A transmogrifier is a novel interface that enables quick, on-the-fly graphic transformations. A region of a graphic can be specified by a shape and transformed into a destination shape with real-time, visual feedback. Both origin and destination shapes can be circles, quadrilaterals or arbitrary shapes defined through touch. Transmogrifiers are flexible, fast and simple to create and invite use in casual InfoVis scenarios, opening the door to alternative ways of exploring and displaying existing visualizations (e.g., rectifying routes or rivers in maps), and enabling free-form prototyping of new visualizations (e.g., lenses).

Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.

The current environment used by French air traffic controllers mixes digital visualization such as radar screens and tangible artifacts such as paper strips. Tangible artifacts do not allow controllers to update the system with the instructions they give to pilots. Previous attempts at replacing them in France failed to prove efficient. This paper is an engineering paper that describes Strip'TIC, a novel system for ATC that mixes augmented paper and digital pen, vision-based tracking and augmented rear and front projection. The system is now working and has enabled us to run workshops with actual controllers to study the role of writing and tangibility in ATC. We describe the system and solutions to technical challenges due to mixing competing technologies.

We present Histomages, a new interaction model for image editing that considers color histograms as spatial rearrangements of image pixels. Users can select pixels on image histograms as they would select image regions and directly manipulate them to adjust their colors. Histomages are also affected by other image tools such as paintbrushes. We explore some possibilities offered by this interaction model, and discuss the four key principles behind it as well as their implications for the design of feature-rich software in general.

Abstract We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow. Our technique can be easily and efficiently implemented using standard graphics acceleration techniques and produces graph bundlings of similar appearance and quality to state‐of‐the‐art methods at a fraction of the cost. Additionally, we show how to create bundled layouts constrained by obstacles and use shading to convey information on the bundling quality. We demonstrate our method on several large graphs.

In previous work, we have proposed a tool for Spatiotemporal Pattern Query. It matches individual moving object trajectories against a given movement pattern. For example, it can be used to find the situations of Missed Approach in ATC data (Air Traffic Control systems, used for tracking the movement of aircrafts), where the landing of the aircraft was interrupted for some reason. This tool expresses the pattern as a set of predicates that must be fulfilled in a certain temporal order. It is implemented as a Plugin to the Secondo DBMS system. Although the tool is generic and flexible, domain expertise is required to formulate and tune queries. The user has to decide the set of predicates, their arguments, and the temporal constraints that best describe the pattern. This paper demonstrates a novel solution where a Visual Analytics system, V-Analytics, is used in integration with this query tool to help a human analyst explore such patterns. The demonstration is based on a real ATC data set.

In this paper, we present a novel approach for constructing bundled layouts of general graphs. As layout cues for bundles, we use medial axes, or skeletons, of edges which are similar in terms of position information. We combine edge clustering, distance fields, and 2D skeletonization to construct progressively bundled layouts for general graphs by iteratively attracting edges towards the centerlines of level sets of their distance fields. Apart from clustering, our entire pipeline is image-based with an efficient implementation in graphics hardware. Besides speed and implementation simplicity, our method allows explicit control of the emphasis on structure of the bundled layout, i.e. the creation of strongly branching (organic-like) or smooth bundles. We demonstrate our method on several large real-world graphs.

We present MoleView, a novel technique for interactive exploration of multivariate relational data. Given a spatial embedding of the data, in terms of a scatter plot or graph layout, we propose a semantic lens which selects a specific spatial and attribute-related data range. The lens keeps the selected data in focus unchanged and continuously deforms the data out of the selection range in order to maintain the context around the focus. Specific deformations include distance-based repulsion of scatter plot points, deforming straight-line node-link graph drawings, and as varying the simplification degree of bundled edge graph layouts. Using a brushing-based technique, we further show the applicability of our semantic lens for scenarios requiring a complex selection of the zones of interest. Our technique is simple to implement and provides real-time performance on large datasets. We demonstrate our technique with actual data from air and road traffic control, medical imaging, and software comprehension applications.

International audience

When displaying thousands of aircraft trajectories on a screen, the visualization is spoiled by a tangle of trails. The visual analysis is therefore difficult, especially if a specific class of trajectories in an erroneous dataset has to be studied. We designed FromDaDy, a trajectory visualization tool that tackles the difficulties of exploring the visualization of multiple trails. This multidimensional data exploration is based on scatterplots, brushing, pick and drop, juxtaposed views and rapid visual design. Users can organize the workspace composed of multiple juxtaposed views. They can define the visual configuration of the views by connecting data dimensions from the dataset to Bertin's visual variables. They can then brush trajectories, and with a pick and drop operation they can spread the brushed information across views. They can then repeat these interactions, until they extract a set of relevant data, thus formulating complex queries. Through two real-world scenarios, we show how FromDaDy supports iterative queries and the extraction of trajectories in a dataset that contains up to 5 million data.