Dynamic Composite Data Physicalization Using Wheeled Micro-Robots

Touching the Ground: Evaluating the Effectiveness of Data Physicalizations for Spatial Data Analysis Tasks

Inspired by recent advances in digital fabrication, artists and scientists have demonstrated that physical data encodings (i.e., data physicalizations) can increase engagement with data, foster collaboration, and in some cases, improve data legibility and analysis relative to digital alternatives. However, prior empirical studies have only investigated abstract data encoded in physical form (e.g., laser cut bar charts) and not continuously sampled spatial data fields relevant to climate and medical science (e.g., heights, temperatures, densities, and velocities sampled on a spatial grid). This paper presents the design and results of the first study to characterize human performance in 3D spatial data analysis tasks across analogous physical and digital visualizations. Participants analyzed continuous spatial elevation data with three visualization modalities: (1) 2D digital visualization; (2) perspective-tracked, stereoscopic "fishtank" virtual reality; and (3) 3D printed data physicalization. Their tasks included tracing paths downhill, looking up spatial locations and comparing their relative heights, and identifying and reporting the minimum and maximum heights within certain spatial regions. As hypothesized, in most cases, participants performed the tasks just as well or better in the physical modality (based on time and error metrics). Additional results include an analysis of open-ended feedback from participants and discussion of implications for further research on the value of data physicalization. All data and supplemental materials are available at https://osf.io/7xdq4/.

A Survey of Designs for Combined 2D+3D Visual Representations

We examine visual representations of data that make use of combinations of both 2D and 3D data mappings. Combining 2D and 3D representations is a common technique that allows viewers to understand multiple facets of the data with which they are interacting. While 3D representations focus on the spatial character of the data or the dedicated 3D data mapping, 2D representations often show abstract data properties and take advantage of the unique benefits of mapping to a plane. Many systems have used unique combinations of both types of data mappings effectively. Yet there are no systematic reviews of the methods in linking 2D and 3D representations. We systematically survey the relationships between 2D and 3D visual representations in major visualization publications-IEEE VIS, IEEE TVCG, and EuroVis-from 2012 to 2022. We closely examined 105 articles where 2D and 3D representations are connected visually, interactively, or through animation. These approaches are designed based on their visual environment, the relationships between their visual representations, and their possible layouts. Through our analysis, we introduce a design space as well as provide design guidelines for effectively linking 2D and 3D visual representations.

From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making

In the face of complex decisions, people often engage in a three-stage process that spans from (1) exploring and analyzing pertinent information (intelligence); (2) generating and exploring alternative options (design); and ultimately culminating in (3) selecting the optimal decision by evaluating discerning criteria (choice). We can fairly assume that all good visualizations aid in the "intelligence" stage by enabling data exploration and analysis. Yet, to what degree and how do visualization systems currently support the other decision making stages, namely "design" and "choice"? To further explore this question, we conducted a comprehensive review of decision-focused visualization tools by examining publications in major visualization journals and conferences, including VIS, EuroVis, and CHI, spanning all available years. We employed a deductive coding method and in-depth analysis to assess whether and how visualization tools support design and choice. Specifically, we examined each visualization tool by (i) its degree of visibility for displaying decision alternatives, criteria, and preferences, and (ii) its degree of flexibility for offering means to manipulate the decision alternatives, criteria, and preferences with interactions such as adding, modifying, changing mapping, and filtering. Our review highlights the opportunities and challenges that decision-focused visualization tools face in realizing their full potential to support all stages of the decision making process. It reveals a surprising scarcity of tools that support all stages, and while most tools excel in offering visibility for decision criteria and alternatives, the degree of flexibility to manipulate these elements is often limited, and the lack of tools that accommodate decision preferences and their elicitation is notable. Based on our findings, to better support the choice stage, future research could explore enhancing flexibility levels and variety, exploring novel visualization paradigms, increasing algorithmic support, and ensuring that this automation is user-controlled via the enhanced flexibility I evels. Our curated list of the 88 surveyed visualization tools is available in the OSF link (https://osf.io/nrasz/?view_only=b92a90a34ae241449b5f2cd33383bfcb).

KiriPhys: Exploring New Data Physicalization Opportunities

We present KiriPhys, a new type of data physicalization based on kirigami, a traditional Japanese art form that uses paper-cutting. Within the kirigami possibilities, we investigate how different aspects of cutting patterns offer opportunities for mapping data to both independent and dependent physical variables. As a first step towards understanding the data physicalization opportunities in KiriPhys, we conducted a qualitative study in which 12 participants interacted with four KiriPhys examples. Our observations of how people interact with, understand, and respond to KiriPhys suggest that KiriPhys: 1) provides new opportunities for interactive, layered data exploration, 2) introduces elastic expansion as a new sensation that can reveal data, and 3) offers data mapping possibilities while providing a pleasurable experience that stimulates curiosity and engagement.

A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?

It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research.

Interweaving Multimodal Interaction With Flexible Unit Visualizations for Data Exploration

Multimodal interfaces that combine direct manipulation and natural language have shown great promise for data visualization. Such multimodal interfaces allow people to stay in the flow of their visual exploration by leveraging the strengths of one modality to complement the weaknesses of others. In this article, we introduce an approach that interweaves multimodal interaction combining direct manipulation and natural language with flexible unit visualizations. We employ the proposed approach in a proof-of-concept system, DataBreeze. Coupling pen, touch, and speech-based multimodal interaction with flexible unit visualizations, DataBreeze allows people to create and interact with both systematically bound (e.g., scatterplots, unit column charts) and manually customized views, enabling a novel visual data exploration experience. We describe our design process along with DataBreeze's interface and interactions, delineating specific aspects of the design that empower the synergistic use of multiple modalities. We also present a preliminary user study with DataBreeze, highlighting the data exploration patterns that participants employed. Finally, reflecting on our design process and preliminary user study, we discuss future research directions.

Generating Legible and Glanceable Swarm Robot Motion through Trajectory, Collective Behavior, and Pre-attentive Processing Features

As swarm robots begin to share the same space with people, it is critical to design legible swarm robot motion that clearly and rapidly communicates the intent of the robots to nearby users. To address this, we apply concepts from intent-expressive robotics, swarm intelligence, and vision science. Specifically, we leverage the trajectory, collective behavior, and density of swarm robots to generate motion that implicitly guides people’s attention toward the goal of the robots. Through online evaluations, we compared different types of intent-expressive motions both in terms of legibility as well as glanceability, a measure we introduce to gauge an observer’s ability to predict robots’ intent pre-attentively. The results show that the collective behavior-based motion has the best legibility performance overall, whereas, for glanceability, trajectory-based legible motion is most effective. These results suggest that the optimal solution may involve a combination of these legibility cues based on the scenario and the desired properties of the motion.

What is Interaction for Data Visualization?

Interaction is fundamental to data visualization, but what "interaction" means in the context of visualization is ambiguous and confusing. We argue that this confusion is due to a lack of consensual definition. To tackle this problem, we start by synthesizing an inclusive view of interaction in the visualization community - including insights from information visualization, visual analytics and scientific visualization, as well as the input of both senior and junior visualization researchers. Once this view takes shape, we look at how interaction is defined in the field of human-computer interaction (HCI). By extracting commonalities and differences between the views of interaction in visualization and in HCI, we synthesize a definition of interaction for visualization. Our definition is meant to be a thinking tool and inspire novel and bolder interaction design practices. We hope that by better understanding what interaction in visualization is and what it can be, we will enrich the quality of interaction in visualization systems and empower those who use them.

3D Printed Deformable Surfaces for Shape-Changing Displays

We use interlinked 3D printed panels to fabricate deformable surfaces that are specifically designed for shape-changing displays. Our exploration of 3D printed deformable surfaces, as a fabrication technique for shape-changing displays, shows new and diverse forms of shape output, visualizations, and interaction capabilities. This article describes our general design and fabrication approach, the impact of varying surface design parameters, and a demonstration of possible application examples. We conclude by discussing current limitations and future directions for this work.