Many potential benefits of data physicalizations are thought to stem from their tangible nature and their presence in a shared space, which allows for physical interaction in a social environment. Prior research has studied where observers touch data physicalizations and how these touches depend on task. However, limited research explores the moment-by-moment details of touches, gestures, and verbal dialogue and how these interactions contribute to the larger data physicalization sensemaking process. This case study offers a new data analysis from a previous study to provide fine-grained accounting of one participant’s touches, gestures, and conversations around a data object. We identify four types of touches and gestures and describe relational patterns between individual interactions. This work provides a foundation for further exploring the reasons people touch or gesture with data physicalizations, connecting these efforts with gesture studies research, and identifying design implications for data physicalization.

Data physicalizations are tangible objects, and touching them may improve their interpretation. However, little is known about how people actually touch physicalizations. We recorded verbal and tactile responses to data physicalizations in three consecutive conditions: as an unspecified object, as a representation of unknown data, and with full information about data and encoding. Our two stimulus objects present data for nine countries in a 3x3 grid. We varied vertical axis polarity, with positive data values either above (convex) or below (concave) baseline. Using an analog tracer method, we examine whether some components of the physicalization are touched more than others, whether touch varies by task and the impact of axis polarity. We found large differences in the degree to which different components were touched and that the effect of vertical axis polarity depended on task. We describe additional tactile and verbal behaviors that can inform the design of data physicalizations.

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.

This book chapter focuses on designing effective and user-friendly information visualizations that enhance usability, user experience, and inclusivity. It examines core design concepts, including taking into account the intended audience, the complexity of the data, and the application environment. The chapter addresses how interaction can improve user experience and places a strong emphasis on the value of user testing and feedback methods in the design process. Ad-ditionally, it emphasizes the difficulties and possibilities of designing for inclusion, taking into account issues like accessibility, diversity, and cultural sensitivity. In summary, this chapter offers helpful advice and insights for information visualization designers, researchers, and practitioners who want to produce attractive, efficient designs that satisfy the requirements and expectations of a wide range of users

Traces of touch provide valuable insight into how we interact with the physical world. Measuring touch behavior, however, is expensive and imprecise. Utilizing a fluorescent UV tracer powder, we developed a low-cost analog method to capture persistent, high-contrast touch records on arbitrary objects. We describe our process for selecting a tracer, methods for capturing, enhancing, and aggregating traces, and approaches to examining qualitative aspects of the user experience. Three user studies demonstrate key features of this method. First, we show that it provides clear and durable traces on objects representative of scientific visualization, physicalization, and product design. Second, we demonstrate how this method could be used to study touch perception, by measuring how task and narrative framing elicit different touch behaviors on the same object. Third, we demonstrate how this method can be used to evaluate data physicalizations by observing how participants touch two different physicalizations of COVID-19 time-series data.

During these past years, international COVID data have been collected by several reputable organizations and made available to the worldwide community. This has resulted in a wellspring of different visualizations. Many different measures can be selected (e.g., cases, deaths, hospitalizations). And for each measure, designers and policy makers can make a myriad of different choices of how to represent the data. Data from individual countries may be presented on linear or log scales, daily, weekly, or cumulative, alone or in the context of other countries, scaled to a common grid, or scaled to their own range, raw or per capita, etc. It is well known that the data representation can influence the interpretation of data. But, what visual features in these different representations affect our judgments? To explore this idea, we conducted an experiment where we asked participants to look at time-series data plots and assess how safe they would feel if they were traveling to one of the countries represented, and how confident they are of their judgment. Observers rated 48 visualizations of the same data, rendered differently along 6 controlled dimensions. Our initial results provide insight into how characteristics of the visual representation affect human judgments of time series data. We also discuss how these results could impact how public policy and news organizations choose to represent data to the public.

Common Fate is a Gestalt principle (Wertheimer (1923), in which static elements embedded in a static background are indistinguishable, but become instantly visible when they move synchronously. Our work is inspired by analysis tasks, where the goal is to find correlated groups of moving elements in environments where the background elements are also moving. Adapting the paradigm introduced by Treisman and Gelade (1980), we measured reaction time as a function of target area for static and moving dot patterns, which were embedded in either static or moving dot backgrounds. All target and background dots moved smoothly to a new random position every second, but the spatial-temporal movements of the 7 target dots were always synchronized. Reaction time for moving dots on a static background was fast, and constant, independent of the spatial area subtended by the target dots. When the background dots were also animated, however, reaction time increased linearly with the spatial area of the target pattern. In a second experiment, we varied the spatial configuration of the target pattern, to explore whether this bifurcation depended on Gestalt feature (good continuation vs. proximity) or on the experimental task (detection vs. identification). In all four conditions, the static background did not affect reaction time for the animated target, which did not change with target area. Moreover, performance was only marginally slower than for animated or static targets with no background at all. When the background was animated, reaction time increased linearly with target area for all four conditions. Percent correct, however, decreased more rapidly for identification tasks, suggesting greater interference from the moving background. Reaction time for a correlated group of target dots was independent of target array size for both detection and identification, but this parallel process breaks down with target area when the background dots are also animated.

It is human to want to touch artworks, to feel their surface curvature and texture, their shapes and structures, and to feel the hand of the artist. Museum guards need to be constantly vigilant to protect art objects from adoring and exploring touches by visitors. This paper introduces a novel technique for capturing where and how art objects are touched. In this method, the users' touch either adds, or subtracts, microscopic fluorescent particles from a three-dimensional art object. Viewing the object under ultraviolet light reveals their touch traces and gestures. We present human touch behavior for a three-dimensional stylized landscape, and for two abstract and two representational art objects. We also present the results of video recordings of real-time behavior and user interviews. The resulting data show the kinds of touches, and where they are directed, and also reveal important individual differences. We feel this method opens the door to studying art perception through touch, and also enables new kinds of studies into touch behavior in other applications, including visualization, embodied cognition, and design.

Geographical maps encoded with rainbow color scales are widely used by climate scientists. Despite a plethora of evidence from the visualization and vision sciences literature about the shortcomings of the rainbow color scale, they continue to be preferred over perceptually optimal alternatives. To study and analyze this mismatch between theory and practice, we present a web-based user study that compares the effect of color scales on performance accuracy for climate-modeling tasks. In this study, we used pairs of continuous geographical maps generated using climatological metrics for quantifying pairwise magnitude difference and spatial similarity. For each pair of maps, 39 scientist-observers judged: i) the magnitude of their difference, ii) their degree of spatial similarity, and iii) the region of greatest dissimilarity between them. Besides the rainbow color scale, two other continuous color scales were chosen such that all three of them covaried two dimensions (luminance monotonicity and hue banding), hypothesized to have an impact on task performance. We also analyzed subjective performance measures, such as user confidence, perceived accuracy, preference, and familiarity in using the different color scales. We found that monotonic luminance scales produced significantly more accurate judgments of magnitude difference but were not superior in spatial comparison tasks, and that hue banding had differential effects based on the task and conditions. Scientists expressed the highest preference and perceived confidence and accuracy with the rainbow, despite its poor performance on the magnitude comparison tasks. We also report on interesting interactions among stimulus conditions, tasks, and color scales, that lead to open research questions.

Data physicalization involves representing numbers and relationships using physical, tangible displays. These displays provide tactile, as well as visual metaphors for expressing and experiencing data, and can unlock new analytical insights and emotional responses. This Dagstuhl seminar brought together a diverse group of researchers and practitioners to explore the benefits and challenges of physicalization – computer scientists trained in visualization, virtual reality and human-computer interaction; architects of virtual and augmented systems; perceptual and cognitive scientists; and artists and designers. Through interactive discussions and demonstrations, we explored physicalization, as a set of methodologies for representing data, for engaging audiences, and for artistic expression. Seminar October 28–November 2, 2018 – http://www.dagstuhl.de/18441 2012 ACM Subject Classification Human-centered computing → Collaborative and social computing, Human-centered computing → Interaction design, Human-centered computing → Ubiquitous and mobile computing, Human-centered computing → Visualization

The authors of this paper teach a masters-level course, called “Data Visualization and Design,” at Columbia University. The goal of this paper is to share the components of the course, the principles and guidelines we are teaching to our students, and specific methods we have adopted. The course has three main themes: (1) Visual design, perception and cognition, (2) Fundamental insight into the mapping of different data types onto different visual geometries, depending on the task, and, perhaps most important, (3) Using visualization to discover patterns and features in data. The course includes readings in visualization, visual analytics, perception and design; hands-on homework assignments using current visualization software; and projects that challenge the students’ skills in using visualization to solve real-world problems. We emphasize core skills that transcend specific choices of software, and which generalize across different data types and analytical methods. In teaching this course, we have looked at fundamental principles of data visualization science and practice through a pedagogical lens; we hope our experience will encourage further discussion in the visualization research community on how we teach, train, and assess students’ mastery of visualization.

Visual alerts are commonly used in video monitoring and surveillance systems to mark events, presumably making them more salient to human observers. Surprisingly, the effectiveness of computer-generated alerts in improving human performance has not been widely studied. To address this gap, we have developed a tool for simulating different alert parameters in a realistic visual monitoring situation, and have measured human detection performance under conditions that emulated different set-points in a surveillance algorithm. In the High-Sensitivity condition, the simulated alerts identified 100% of the events with many false alarms. In the Lower-Sensitivity condition, the simulated alerts correctly identified 70% of the targets, with fewer false alarms. In the control condition, no simulated alerts were provided. To explore the effects of learning, subjects performed these tasks in three sessions, on separate days, in a counterbalanced, within subject design. We explore these results within the context of cognitive models of human attention and learning.We found that human observers were more likely to respond to events when marked by a visual alert. Learning played a major role in the two alert conditions. In the first session, observers generated almost twice as many False Alarms as in the No-Alert condition, as the observers responded pre-attentively to the computer-generated false alarms. However, this rate dropped equally dramatically in later sessions, as observers learned to discount the false cues. Highest observer Precision, Hits/(Hits + False Alarms), was achieved in the High Sensitivity condition, but only after training. The successful evaluation of surveillance systems depends on understanding human attention and performance.

Electronic imaging applications hinge on the ability to discover features in data. For example, doctors examine diagnostic images for tumors, broken bones and changes in metabolic activity. Financial analysts explore visualizations of market data to find correlations, outliers and interaction effects. Seismologists look for signatures in geological data to tell them where to drill or where an earthquake may begin. These data are very diverse, including images, numbers, graphs, 3-D graphics, and text, and are growing exponentially, largely through the rise in automatic data collection technologies such as sensors and digital imaging. This paper explores important trends in the art and science of finding features in data, such as the tension between bottom-up and top-down processing, the semantics of features, and the integration of human- and algorithm-based approaches. This story is told from the perspective of the IS and T/SPIE Conference on Human Vision and Electronic Imaging (HVEI), which has fostered research at the intersection between human perception and the evolution of new technologies.