Understanding the difference between small and big values spanning over several orders of magnitude can be difficult. In this demo, we introduce a proof of concept named AR Data Ribbons, an augmented reality visualization scaled so that even smaller values are visible but requiring people to unroll the biggest values of a bar chart going beyond its frame. While the smallest values appear as classic bars on a 2D canvas, participants are invited to pick up a roll handle and to unroll ribbons representing the biggest values of the dataset. Ribbons are then unfolded in the space surrounding the participant until reaching the length corresponding to the data value. We hope our design could lead to a greater engagement, creative ribbon output patterns and better data understanding.

Data videos combine visualization, animation, narration, and often background music to tell stories with data. While music is widely believed to enhance emotion and persuasion, its impact in data videos remains unexplored. We conducted a preregistered between-subjects experiment comparing six widely-viewed data videos with or without background music. Using Bayesian modeling and thematic analysis, we did not observe consistent measurable effects of background music on persuasion, engagement, or information recall. Qualitative responses revealed a more nuanced picture: some participants described the music as distracting or mismatched, while others reported that it enhanced enjoyment, supported focus, or strengthened emotional resonance when well aligned with the video’s tone. These findings suggest that the influence of background music in data videos is highly context-dependent, shaped by genre, familiarity, and its alignment with visual–narrative structure. We discuss possible reasons for the limited measurable effects observed in real-world videos and outline opportunities for future work on purpose-designed, incidental, or adaptive music for data-driven storytelling.

Augmented reality (AR) has increasingly been used to communicate environmental impacts, offering greater engagement than conventional displays. However, its effect on message credibility-how much people believe in the content of the communication-remains unclear. In a preregistered study, we compared the perceived credibility of environmental information presented via visualizations on an AR headset or a desktop display. We created display-specific visual encodings (3D concrete for AR, 2D bar charts for desktop) and added two control conditions to cross display and encoding. We found no difference in message credibility between AR and desktop, though concrete AR was rated most engaging. Supplementary material is available at https://osf.io/n4p5c/.

We know little about the impact of augmented reality (AR) on human cognition, particularly regarding involuntary autobiographical memory (IAM).IAMs are spontaneous recollections of personal events, ubiquitous in daily life but under-researched in both psychology and human-computer interaction.We first discuss the potential opportunities and risks of replacing conventional displays with AR to increase the likelihood of IAMs.We then report on a study investigating whether stimuli displayed on the same mobile device using video-see-through AR are more likely to resurface than those shown with a simple 3D viewer.We found that AR elicits approximately twice as many IAMs in controlled settings with immediate re-exposure to contextual cues, but no measurable effect was found in everyday settings with delayed re-exposure.Therefore, AR can enhance IAMs, but its effects may be modest and short-lived in most cases.Nevertheless, future studies could reveal stronger effects of AR in other settings.Supplementary material is available at osf.io/gscpv/.

While data visualizations are typically abstract, there is a growing body of work around concrete visualizations, which use familiar objects to convey data. Concrete visualizations can complement abstract ones, especially in immersive analytics, but it is unclear how to design smoothly animated transitions between these two kinds of representations. We investigate a design space of abstract and concrete visualizations, where animated transitions are pathways through the design space. The design space is defined with four axes, each corresponding to a different transformation. We consider different ways to design animated transitions by staging and ordering the transformations along these axes. In a controlled experiment conducted in virtual reality with 16 participants, we compared four types of animated transitions and found quantitative and qualitative evidence of the superiority of a specific staging approach over the simultaneous application of all transformations. Our study pre-registration is available at https://osf.io/8mu73.

This January 2025 issue of the IEEE Transactions on Visualization and Computer Graphics (TVCG) contains the proceedings of IEEE VIS 2024, held on October 1318 October, 2024 in St. Pete Beach, Florida, USA, with the three General Chairs Paul Rosen (University of Utah), Kristi Potter (U.S. National Renewable Energy Laboratory), and Remco Chang (Tufts University). With IEEE VIS 2024, the conference series is in its 35th year.

“Cultural Windows” is a research initiative aimed at enhancing cross-cultural understanding through immersive extended reality (XR) experiences. The project deploys AR and VR platforms to allow users to explore diverse living spaces, bridging the gap between preconceived notions and the actual appearance of these spaces. By using 3D scanning to create accurate models of culturally significant objects and integrating them into immersive systems, the project provides insights into the use of immersive technologies in cultural education, promoting engagement with global living designs.

Situated visualizations are a type of visualization where data is presented next to its physical referent (i.e., the physical object, space, or person it refers to), often using augmented-reality displays. While situated visualizations can be beneficial in various contexts and have received research attention, they are typically designed with the assumption that the physical referent is visible. However, in practice, a physical referent may be obscured by another object, such as a wall, or may be outside the user's visual field. In this paper, we propose a conceptual framework and a design space to help researchers and user interface designers handle non-visible referents in situated visualizations. We first provide an overview of techniques proposed in the past for dealing with non-visible objects in the areas of 3D user interfaces, 3D visualization, and mixed reality. From this overview, we derive a design space that applies to situated visualizations and employ it to examine various trade-offs, challenges, and opportunities for future research in this area.

Is it true that if citizens understand hurricane probabilities, they will make more rational decisions for evacuation? Finding answers to such questions is not straightforward in the literature because the terms "judgment" and "decision making" are often used interchangeably. This terminology conflation leads to a lack of clarity on whether people make suboptimal decisions because of inaccurate judgments of information conveyed in visualizations or because they use alternative yet currently unknown heuristics. To decouple judgment from decision making, we review relevant concepts from the literature and present two preregistered experiments (N = 601) to investigate if the task (judgment versus decision making), the scenario (sports versus humanitarian), and the visualization (quantile dotplots, density plots, probability bars) affect accuracy. While experiment 1 was inconclusive, we found evidence for a difference in experiment 2. Contrary to our expectations and previous research, which found decisions less accurate than their direct-equivalent judgments, our results pointed in the opposite direction. Our findings further revealed that decisions were less vulnerable to status-quo bias, suggesting decision makers may disfavor responses associated with inaction. We also found that both scenario and visualization types can influence people's judgments and decisions. Although effect sizes are not large and results should be interpreted carefully, we conclude that judgments cannot be safely used as proxy tasks for decision making, and discuss implications for visualization research and beyond. Materials and preregistrations are available at https://osf.io/ufzp5/?view_only=adc0f78a23804c31bf7fdd9385cb264f.

Studies on human decision-making focused on humanitarian aid have found that cognitive biases can hinder the fair allocation of resources. However, few HCI and Information Visualization studies have explored ways to overcome those cognitive biases. This work investigates whether the design of interactive resource allocation tools can help to promote allocation fairness. We specifically study the effect of presentation format (using text or visualization) and a specific framing strategy (showing resources allocated to groups or individuals). In our three crowdsourced experiments, we provided different tool designs to split money between two fictional programs that benefit two distinct communities. Our main finding indicates that individual-framed visualizations and text may be able to curb unfair allocations caused by group-framed designs. This work opens new perspectives that can motivate research on how interactive tools and visualizations can be engineered to combat cognitive biases that lead to inequitable decisions.

Aparticipatory data physicalization, or PDP, is a physical visualization that allows people to physically participate in the creation of the visualization, by directly encoding their data. PDPs offer a way to engage a community with data of personal relevance that otherwise would be intangible. However, their design space has only begun to be explored, and most prior work shows relatively simple encoding rules. Therefore, we present a design exploration of different ways of faceting contributed data: by week, day, or person. Specifically, we designed Edo, a PDP that allows a small community to contribute their data to a physical visualization showing the climate impact of their dietary choices. In this pictorial, we elaborate on the design process of Edo and reflect on the findings of a three-week deployment of different layouts of Edo. Fabrication instructions and data are available under CC-BY 4.0 at osf.io/q5fr6.

Gesture elicitation studies are commonly used for designing novel gesture-based interfaces. There is a rich methodology literature on metrics and analysis methods that helps researchers understand and characterize data arising from such studies. However, deriving concrete gesture vocabularies from this data, which is often the ultimate goal, remains largely based on heuristics and ad hoc methods. In this paper, we treat the problem of deriving a gesture vocabulary from gesture elicitation data as a computational optimization problem. We show how to formalize it as an optimal assignment problem and discuss how to express objective functions and custom design constraints through integer programs. In addition, we introduce a set of tools for assessing the uncertainty of optimization outcomes due to random sampling, and for supporting researchers’ decisions on when to stop collecting data from a gesture elicitation study. We evaluate our methods on a large number of simulated studies.

Abstract Analysing data from experiments is a complex, multi‐step process, often with multiple defensible choices available at each step. While analysts often report a single analysis without documenting how it was chosen, this can cause serious transparency and methodological issues. To make the sensitivity of analysis results to analytical choices transparent, some statisticians and methodologists advocate the use of ‘multiverse analysis’: reporting the full range of outcomes that result from all combinations of defensible analytic choices. Summarizing this combinatorial explosion of statistical results presents unique challenges; several approaches to visualizing the output of multiverse analyses have been proposed across a variety of fields (e.g. psychology, statistics, economics, neuroscience). In this article, we (1) introduce a consistent conceptual framework and terminology for multiverse analyses that can be applied across fields; (2) identify the tasks researchers try to accomplish when visualizing multiverse analyses and (3) classify multiverse visualizations into ‘archetypes’, assessing how well each archetype supports each task. Our work sets a foundation for subsequent research on developing visualization tools and techniques to support multiverse analysis and its reporting.

When showing data about people, visualization designers and data journalists often use design strategies that presumably help the audience relate to those people. The term anthropographics has been recently coined to refer to this practice and the resulting visualizations. Anthropographics is a rich and growing area, but the work so far has remained scattered. Despite preliminary empirical work and a few web essays written by practitioners, there is a lack of clear language for thinking about and communicating about anthropographics. We address this gap by introducing a conceptual framework and a design space for anthropographics. Our design space consists of seven elementary design dimensions that can be reasonably hypothesized to have some effect on prosocial feelings or behavior. It extends a previous design space and is informed by an analysis of 105 visualizations collected from newspapers, websites, and research articles. We use our conceptual framework and design space to discuss trade-offs, common design strategies, as well as future opportunities for design and research in the area of anthropographics.

We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-author matching can be accessed through https://nyu.databrary.org/volume/1301.

We explore how the lens of fictional superpowers can help characterize how visualizations empower people and provide inspiration for new visualization systems. Researchers and practitioners often tout visualizations' ability to "make the invisible visible" and to "enhance cognitive abilities." Meanwhile superhero comics and other modern fiction often depict characters with similarly fantastic abilities that allow them to see and interpret the world in ways that transcend traditional human perception. We investigate the intersection of these domains, and show how the language of superpowers can be used to characterize existing visualization systems and suggest opportunities for new and empowering ones. We introduce two frameworks: The first characterizes seven underlying mechanisms that form the basis for a variety of visual superpowers portrayed in fiction. The second identifies seven ways in which visualization tools and interfaces can instill a sense of empowerment in the people who use them. Building on these observations, we illustrate a diverse set of "visualization superpowers" and highlight opportunities for the visualization community to create new systems and interactions that empower new experiences with data Material and illustrations are available under CC-BY 4.0 at osf.io/8yhfz.

Visualizations designed to make readers compassionate with the persons whose data is represented have been called anthropographics and are commonly employed by practitioners. Empirical studies have recently examined whether anthropographics indeed promote empathy, compassion, or the likelihood of prosocial behavior, but findings have been inconclusive so far. This work contributes a detailed overview of past experiments, and two new experiments that use large samples and a combination of design strategies to maximize the possibility of finding an effect. We tested an information-rich anthropographic against a simple bar chart, asking participants to allocate hypothetical money in a crowdsourcing study. We found that the anthropographic had, at best, a small effect on money allocation. Such a small effect may be relevant for large-scale donation campaigns, but the large sample sizes required to observe an effect and the noise involved in measuring it make it very difficult to study in more depth. Data and code are available at https://osf.io/xqae2/.

Abstract We present the first empirical study on using colour manipulation and stylization to make surgery images/videos more palatable. While aversion to such material is natural, it limits many people's ability to satisfy their curiosity, educate themselves and make informed decisions. We selected a diverse set of image processing techniques to test them both on surgeons and lay people. While colour manipulation techniques and many artistic methods were found unusable by surgeons, edge‐preserving image smoothing yielded good results both for preserving information (as judged by surgeons) and reducing repulsiveness (as judged by lay people). We then conducted a second set of interview with surgeons to assess whether these methods could also be used on videos and derive good default parameters for information preservation. We provide extensive supplemental material at osf.io/4pfes/ .

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.

We present explorable multiverse analysis reports, a new approach to statistical reporting where readers of research papers can explore alternative analysis options by interacting with the paper itself. This approach draws from two recent ideas: i) multiverse analysis, a philosophy of statistical reporting where paper authors report the outcomes of many different statistical analyses in order to show how fragile or robust their findings are; and ii) explorable explanations, narratives that can be read as normal explanations but where the reader can also become active by dynamically changing some elements of the explanation. Based on five examples and a design space analysis, we show how combining those two ideas can complement existing reporting approaches and constitute a step towards more transparent research papers.

This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work.

Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We present multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography. We then introduce a declarative model-a simple yet expressive JSON grammar associated with visual semantics-that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.

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 provide a reappraisal of Tal and Wansink's study "Blinded with Science", where seemingly trivial charts were shown to increase belief in drug efficacy, presumably because charts are associated with science. Through a series of four replications conducted on two crowdsourcing platforms, we investigate an alternative explanation, namely, that the charts allowed participants to better assess the drug's efficacy. Considered together, our experiments suggest that the chart seems to have indeed promoted understanding, although the effect is likely very small. Meanwhile, we were unable to replicate the original study's findings, as text with chart appeared to be no more persuasive - and sometimes less persuasive - than text alone. This suggests that the effect may not be as robust as claimed and may need specific conditions to be reproduced. Regardless, within our experimental settings and considering our study as a whole (), the chart's contribution to understanding was clearly larger than its contribution to persuasion.

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.

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.

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 introduce embedded data representations, the use of visual and physical representations of data that are deeply integrated with the physical spaces, objects, and entities to which the data refers. Technologies like lightweight wireless displays, mixed reality hardware, and autonomous vehicles are making it increasingly easier to display data in-context. While researchers and artists have already begun to create embedded data representations, the benefits, trade-offs, and even the language necessary to describe and compare these approaches remain unexplored. In this paper, we formalize the notion of physical data referents - the real-world entities and spaces to which data corresponds - and examine the relationship between referents and the visual and physical representations of their data. We differentiate situated representations, which display data in proximity to data referents, and embedded representations, which display data so that it spatially coincides with data referents. Drawing on examples from visualization, ubiquitous computing, and art, we explore the role of spatial indirection, scale, and interaction for embedded representations. We also examine the tradeoffs between non-situated, situated, and embedded data displays, including both visualizations and physicalizations. Based on our observations, we identify a variety of design challenges for embedded data representation, and suggest opportunities for future research and applications.

We introduce a novel method based on physical proxies for investigating fundamental differences between touch and tangible interfaces. This method uses physical chips to emulate the flat, non-graspable objects that make up touch interfaces, in a way that supports direct comparison with tangible interfaces. We ran an experiment to test the effect of object thickness on participants' behavior, performance and subjective experience in spatial rearrangement tasks. We found that for the tasks tested, thick objects are faster but less accurate to operate, and that their graspability is only used occasionally. We also found that coarse manipulation of multiple thin objects is error-prone, an issue that only thick objects may allow to alleviate.

This paper introduces swarm user interfaces, a new class of human-computer interfaces comprised of many autonomous robots that handle both display and interaction. We describe the design of Zooids, an open-source open-hardware platform for developing tabletop swarm interfaces. The platform consists of a collection of custom-designed wheeled micro robots each 2.6 cm in diameter, a radio base-station, a high-speed DLP structured light projector for optical tracking, and a software framework for application development and control. We illustrate the potential of tabletop swarm user interfaces through a set of application scenarios developed with Zooids, and discuss general design considerations unique to swarm user interfaces.

We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets.

Physical representations of data have existed for thousands of years. Yet it is now that advances in digital fabrication, actuated tangible interfaces, and shape-changing displays are spurring an emerging area of research that we call Data Physicalization. It aims to help people explore, understand, and communicate data using computer-supported physical data representations. We call these representations physicalizations, analogously to visualizations -- their purely visual counterpart. In this article, we go beyond the focused research questions addressed so far by delineating the research area, synthesizing its open challenges and laying out a research agenda.

We explore interactive relief shearing, a set of non-intrusive, direct manipulation interactions that expose depth and shape information in terrain maps using ephemeral animations. Reading and interpreting topography and relief on terrain maps is an important aspect of map use, but extracting depth information from 2D maps is notoriously difficult. Modern mapping software attempts to alleviate this limitation by presenting digital terrain using 3D views. However, 3D views introduce occlusion, complicate distance estimations, and typically require more complex interactions. In contrast, our approach reveals depth information via shearing animations on 2D maps, and can be paired with existing interactions such as pan and zoom. We examine explicit, integrated, and hybrid interactions for triggering relief shearing and present a version that uses device tilt to control depth effects. Our evaluation shows that these interactive techniques improve depth perception when compared to standard 2D and perspective views.

SmartTokens are small-sized tangible tokens that can sense multiple types of motion, multiple types of touch/grip, and send input events wirelessly as state-machine transitions. By providing an open platform for embedding basic sensing capabilities within small form-factors, SmartTokens extend the design space of tangible user interfaces. We describe the design and implementation of SmartTokens and illustrate how they can be used in practice by introducing a novel TUI design for event notification and personal task management.

Abstract Node‐link infographics are visually very rich and can communicate messages effectively, but can be very difficult to create, often involving a painstaking and artisanal process. In this paper we present an investigation of node‐link visualizations for communication and how to better support their creation. We begin by breaking down these images into their basic elements and analyzing how they are created. We then present a set of techniques aimed at improving the creation workflow by bringing more flexibility and power to users, letting them manipulate all aspects of a node‐link diagram (layout, visual attributes, etc.) while taking into account the context in which it will appear. These techniques were implemented in a proof‐of‐concept prototype called GraphCoiffure, which was designed as an intermediary step between graph drawing/editing software and image authoring applications. We describe how GraphCoiffure improves the workflow and illustrate its benefits through practical examples.

Pointing on large displays with an indirect, relative pointing device such as a touchpad often requires clutching. This article introduces gliding, where the cursor continues to move during the clutching gestures. The effect is that of controlling the cursor as a detached object that can be pushed, with inertia and friction similar to a puck being pushed on a table. We analyze gliding from a practical and a theoretical perspective and report on two studies. The first controlled experiment establishes that gliding reduces clutching and can improve pointing performance for large distances. We introduce cursor efficiency to capture the effects of gliding on clutching. The second experiment demonstrates that participants use gliding even when an efficient acceleration function lets them perform the task without it, without degrading performance.

Physical visualizations come in increasingly diverse forms, and are used in domains including art and entertainment, business analytics, and scientific research. However, creating physical visualizations requires laborious craftsmanship and demands expertise in both data visualization and digital fabrication. We present three case studies that illustrate limitations of current visualization fabrication workflows. We then present MakerVis, a prototype tool that integrates the entire process of creating physical visualizations, from data filtering to physical fabrication. Design sessions with three end users demonstrate how tools such as MakerVis can dramatically lower the barriers to producing physical visualizations. Observations and interviews from these sessions highlighted future research areas, including customization support, using material properties to represent data variables, and allowing the reuse of physical data objects in new visualizations.

Interactive visual applications often rely on animation to transition from one display state to another. There are multiple animation techniques to choose from, and it is not always clear which should produce the best visual correspondences between display elements. One major factor is whether the animation relies on staggering-an incremental delay in start times across the moving elements. It has been suggested that staggering may reduce occlusion, while also reducing display complexity and producing less overwhelming animations, though no empirical evidence has demonstrated these advantages. Work in perceptual psychology does show that reducing occlusion, and reducing inter-object proximity (crowding) more generally, improves performance in multiple object tracking. We ran simulations confirming that staggering can in some cases reduce crowding in animated transitions involving dot clouds (as found in, e.g., animated 2D scatterplots). We empirically evaluated the effect of two staggering techniques on tracking tasks, focusing on cases that should most favour staggering. We found that introducing staggering has a negligible, or even negative, impact on multiple object tracking performance. The potential benefits of staggering may be outweighed by strong costs: a loss of common-motion grouping information about which objects travel in similar paths, and less predictability about when any specific object would begin to move. Staggering may be beneficial in some conditions, but they have yet to be demonstrated. The present results are a significant step toward a better understanding of animation pacing, and provide direction for further research.

We present Bertifier, a web app for rapidly creating tabular visualizations from spreadsheets. Bertifier draws from Jacques Bertin's matrix analysis method, whose goal was to "simplify without destroying" by encoding cell values visually and grouping similar rows and columns. Although there were several attempts to bring this method to computers, no implementation exists today that is both exhaustive and accessible to a large audience. Bertifier remains faithful to Bertin's method while leveraging the power of today's interactive computers. Tables are formatted and manipulated through crossets, a new interaction technique for rapidly applying operations on rows and columns. We also introduce visual reordering, a semi-interactive reordering approach that lets users apply and tune automatic reordering algorithms in a WYSIWYG manner. Sessions with eight users from different backgrounds suggest that Bertifier has the potential to bring Bertin's method to a wider audience of both technical and non-technical users, and empower them with data analysis and communication tools that were so far only accessible to a handful of specialists.

Data sculptures are an increasingly popular form of physical visualization whose purposes are essentially artistic, communicative or educational. But can physical visualizations help carry out actual information visualization tasks? We present the first infovis study comparing physical to on-screen visualizations. We focus on 3D visualizations, as these are common among physical visualizations but known to be problematic on computers. Taking 3D bar charts as an example, we show that moving visualizations to the physical world can improve users' efficiency at information retrieval tasks. In contrast, augmenting on-screen visualizations with stereoscopic rendering alone or with prop-based manipulation was of limited help. The efficiency of physical visualizations seems to stem from features that are unique to physical objects, such as their ability to be touched and their perfect visual realism. These findings provide empirical motivation for current research on fast digital fabrication and self-reconfiguring interfaces.

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.

We present an interaction model for beyond-desktop visualizations that combines the visualization reference model with the instrumental interaction paradigm. Beyond-desktop visualizations involve a wide range of emerging technologies such as wall-sized displays, 3D and shape-changing displays, touch and tangible input, and physical information visualizations. While these technologies allow for new forms of interaction, they are often studied in isolation. New conceptual models are needed to build a coherent picture of what has been done and what is possible. We describe a modified pipeline model where raw data is processed into a visualization and then rendered into the physical world. Users can explore or change data by directly manipulating visualizations or through the use of instruments. Interactions can also take place in the physical world outside the visualization system, such as when using locomotion to inspect a large scale visualization. Through case studies we illustrate how this model can be used to describe both conventional and unconventional interactive visualization systems, and compare different design alternatives.

We introduce interactive graph matching, a process that conciliates visualization, interaction and optimization approaches to address the graph matching and graph comparison problems as a whole. Interactive graph matching is based on a multi-layered interaction model and on a visual reification of graph matching functions. We present three case studies and a system named Donatien to demonstrate the interactive graph matching approach. The three case studies involve different datasets a) subgraphs of a lexical network, b) graph of keywords extracted from the InfoVis contest benchmark, and c) clustered graphs computed from different clustering algorithms for comparison purposes.

We explore the use of customizable tangible remote controllers for interacting with wall-size displays. Such controllers are especially suited to visual exploration tasks where users need to move to see details of complex visualizations. In addition, we conducted a controlled user study suggesting that tangibles make it easier for users to focus on the visual display while they interact. We explain how to build such controllers using off-the-shelf touch tablets and describe a sample application that supports multiple dynamic queries.

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.

People have difficulty understanding statistical information and are unaware of their wrong judgments, particularly in Bayesian reasoning. Psychology studies suggest that the way Bayesian problems are represented can impact comprehension, but few visual designs have been evaluated and only populations with a specific background have been involved. In this study, a textual and six visual representations for three classic problems were compared using a diverse subject pool through crowdsourcing. Visualizations included area-proportional Euler diagrams, glyph representations, and hybrid diagrams combining both. Our study failed to replicate previous findings in that subjects' accuracy was remarkably lower and visualizations exhibited no measurable benefit. A second experiment confirmed that simply adding a visualization to a textual Bayesian problem is of little help, even when the text refers to the visualization, but suggests that visualizations are more effective when the text is given without numerical values. We discuss our findings and the need for more such experiments to be carried out on heterogeneous populations of non-experts.

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.

When pointing to a target on a computer desktop, we may think we are taking the shortest possible path. But new shortcuts become possible if we allow the mouse cursor to jump from one edge of the screen to the opposite one, i.e., if we turn the desktop into a torus. We discuss the design of TORUSDESKTOP, a pointing technique that allows to wrap the cursor around screen edges to open this pointing backdoor. A dead zone and an off-screen cursor feedback make the technique more usable and more compatible with everyday desktop usage. We report on three controlled experiments conducted to refine the design of the technique and evaluate its performance. The results suggest clear benefits of using the backdoor when target distance is more than 80% the screen size in our experimental conditions.

We present a quick preview technique that smoothly transitions between document markup code and its visual rendering. This technique allows users to regularly check the code they are editing in-place, without leaving the text editor. This method can complement classical preview windows by offering rapid overviews of code-to-document mappings and leaving more screen real-estate. We discuss the design and implementation of our technique.

Targets of only a few pixels are notoriously difficult to acquire. Despite many attempts at facilitating pointing, the reasons for this difficulty are poorly understood. We confirm a strong departure from Fitts' Law for small target acquisition using a mouse and investigate three potential sources of problems: motor accuracy, legibility, and quantization. We find that quantization is not a problem, but both motor and visual sizes are limiting factors. This suggests that small targets should be magnified in both motor and visual space to facilitate pointing. Since performance degrades exponentially as targets get very small, we further advocate the exploration of uniform, target-agnostic magnification strategies. We also confirm Welford's 1969 proposal that motor inaccuracy can be modeled by subtracting a “tremor constant” from target size. We argue for the adoption of this model, rather than Fitts' law, when reflecting on small target acquisition.

Visualization applications routinely map quantitative attributes to color using color scales. Although color is an effective visualization channel, it is limited by both display hardware and the human visual system. We propose a new interaction technique that overcomes these limitations by dynamically optimizing color scales based on a set of sampling lenses. The technique inspects the lens contents in data space, optimizes the initial color scale, and then renders the contents of the lens to the screen using the modified color scale. We present two prototype implementations of this pipeline and describe several case studies involving both information visualization and image inspection applications. We validate our approach with two mutually linked and complementary user studies comparing the Color Lens with explicit contrast control for visual search.

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.

We present a method for browsing videos by directly dragging their content. This method brings the benefits of direct manipulation to an activity typically mediated by widgets. We support this new type of interactivity by: 1) automatically extracting motion data from videos; and 2) a new technique called relative flow dragging that lets users control video playback by moving objects of interest along their visual trajectory. We show that this method can outperform the traditional seeker bar in video browsing tasks that focus on visual content rather than time.

Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using scatterplots. This exploration is performed using a matrix of scatterplots that gives an overview of the possible configurations, thumbnails of the scatterplots, and support for interactive navigation in the multidimensional space. Transitions between scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets.

Hotkeys are extremely useful in leveraging expert performance, but learning them is a slow process. This paper investigates alternative menu designs that can motivate and help users remember associations between menu commands and hotkeys. Building upon previous work on paired-associate learning, we suggest that the transition to expert use can be accelerated by manipulating feedback and cost associated with menu selection. We evaluate five designs in a pilot study and then two of the most promising ones in a formal experiment, showing that the speed of hotkey learning can indeed be significantly increased with little modifications to the standard menu/hotkey paradigm.

We present the design and evaluation of earPod: an eyes-free menu technique using touch input and reactive auditory feedback. Studies comparing earPod with an iPod-like visual menu technique on reasonably-sized static menus indicate that they are comparable in accuracy. In terms of efficiency (speed), earPod is initially slower, but outperforms the visual technique within 30 minutes of practice. Our results indicate that earPod is potentially a reasonable eyes-free menu technique for general use, and is a particularly exciting technique for use in mobile device interfaces.

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.

This paper is an extended version of the final version of a paper accepted at EHCI-DSVIS 2004, Lecture Notes in Computer Science n°3425

When involved in the visual design of graphical user interfaces, graphic designers can do more than providing static graphics for programmers to incorporate into applications. We describe a technique that allows them to provide examples of graphical objects at various key sizes using their usual drawing tool, then let the system interpolate their resizing behavior. We relate this technique to current practices of graphic designers, provide examples of its use and describe the underlying inference algorithm. We show how the mathematical properties of the algorithm allows the system to be predictable and explain how it can be combined with more traditional layout mechanisms.

This article describes ICON (Input Configurator), an input management system that enables interactive applications to achieve a high level of input adaptability. We define input adaptability as the ability of an interactive application to exploit alternative input devices effectively and offer users a way of adapting input interaction to suit their needs. We describe several examples of interaction techniques implemented using ICON with little or no support from applications that are hard or impossible to implement using regular GUI toolkits.

This paper presents the use of a model-based approach for the formal description of real-time embedded multimodal systems. This modeling technique has been used in the field of military fighter aircrafts. The paper presents the formal description techniques, its application on the case study of a multimodal command and control interface for the Rafale aircraft as well as its relationship with architectural model for interactive systems.

In this paper, we introduce input adaptability as the ability of an application to exploit alternative sets of input devices effectively and offer users a way of adapting input interaction to suit their needs. We explain why input adaptability must be seriously considered today and show how it is poorly supported by current systems, applications and tools. We then describe ICon (Input Configurator), an input toolkit that allows interactive applications to achieve a high level of input adaptability. We present the software architecture behind ICon then the toolkit itself, and give several examples of non-standard interaction techniques that are easy to build and modify using ICon's graphical editor while being hard or impossible to support using regular GUI toolkits.

Despite novel interaction techniques proposed for virtual desktops, common yet challenging tasks remain to be investigated. Dragging and dropping between overlapping windows is one of them. The fold-and-drop technique presented here offers a natural and efficient way of performing those tasks. We show how this technique successfully builds upon several interaction paradigms previously described, while shedding new light on them.

This article presents MaggLite, a toolkit and sketch-based interface builder allowing fast and interactive design of post-WIMP user interfaces. MaggLite improves design of advanced UIs thanks to its novel mixed-graph architecture that dynamically combines scene-graphs with interaction-graphs. Scene-graphs provide mechanisms to describe and produce rich graphical effects, whereas interaction-graphs allow expressive and fine-grained description of advanced interaction techniques and behaviors such as multiple pointers management, toolglasses, bimanual interaction, gesture, and speech recognition. Both graphs can be built interactively by sketching the UI and specifying the interaction using a dataflow visual language. Communication between the two graphs is managed at runtime by components we call Interaction Access Points. While developers can extend the toolkit by refining built-in generic mechanisms, UI designers can quickly and interactively design, prototype and test advanced user interfaces by applying the MaggLite principle: "draw it, connect it and run it".