Qualitative and quantitative eye tracking studies are prominent in many fields to understand behavior related to visual attention, in particular, for visualization research. The design, setup, and execution of a study, as well as the analysis of the acquired eye tracking data, can be difficult. This work proposes guidelines for eye tracking studies in visualization. We differentiate three major phases, focusing on before, during, and after a study. These guidelines are based on our experiences from conducting more than 100 eye tracking studies and additional literature research for each phase. As a result, we present a structured plan and chronological order of general requirements as a checklist for conducting eye tracking studies. A living document of the checklist can be found at: https://github.com/mibu1976/etvis2025

In this paper, we propose an interactive visualization tool that supports comparative views on eye movement data by splitting the data into data categories based on the independent, user-selected, and automatically detected variables available in the eye movement data. Moreover, the tool is composed of multiple linked, comparative, and coordinated views to visually depict the data in statistical, point- and AOI-based, as well as spatio-temporal visualizations overlaid on the visual stimuli. It supports algorithmic approaches including clustering, trend detection, and dimensionality reduction to aggregate, group, structure, or filter the data. We illustrate the usefulness of the tool by applying it to eye movement data from a public transport map eye tracking study that is publicly available and show perspectives on each of the three variable categories.

In this paper we investigate the problem of creating an interactive visual overview for dynamic pairwise currency correlations in the form of an interactive visual analytics tool. To reach this goal we extract real-time data and compute pairwise correlations for user-defined time windows on which the correlation values are based. To structure the generated matrix of pairwise currency correlations we further extend our approach by matrix reordering based on a hierarchical clustering with which we can visually identify groups and subgroups of currency pairs that stand in a certain correlation behavior. Moreover, we explore the dynamic data from the perspective of economic and pandemic events like COVID-19. We illustrate the usefulness of our technique by applying it to currency data from countries all over the world and explore several data dimensions in it while focusing on the Swiss Francs currency in particular. Based on our algorithmic and visual analysis we can find out that COVID-19 as well as economic events have impacts on the currencies and currency groups. Finally, we discuss scalability issues and limitations of our interactive visual analytics tool, coming in the form of an interactive dashboard.

We investigate mouse movement as an interaction technique to solve tasks in simple visual interface settings. For this purpose, we recruited 17 participants in a controlled within-subjects study. The design of the study is based on six different task groups. As an independent variable, we tested the impact of typical Gestalt principles visually encoded in user interfaces on the dependent variable mouse movement, in particular on movement path trajectories and task completion times until mouse click. The variations of the independent variable take into account Gestalt principles such as proximity, similarity, common region, and figure ground, testing cases in which the principles are followed and cases in which they are disregarded. We illustrate movement paths as trajectory visualizations in three variations showing the paths themselves over space and time, movement speed, and mouse clicks. The main result of the study suggests that visual interfaces that follow Gestalt principles produce shorter mouse movement paths and task completion times.

There are various styles to visually represent relational data in the form of node-link diagrams. In particular, for planar graphs we can find orthogonal node-link diagrams consisting of links bending only at ninety degrees a successful and prominent variant. One of the benefits of such drawings is the tracking of longer paths through a network with the eyes due to their limited number of link orientations, changes, and variations, but on the negative side the links can have arbitrary bending shapes. In this article we developed a novel way to visualize such orthogonal planar drawings by making use of mazes that look more natural to the human eye due to the street-like visual metaphor that many people are familiar with. Tracking paths is one of the major tasks in such graph visualizations, similar to orthogonal node-link diagrams, however, we argue that mazes are a more natural way to find paths. To get insights in the visual scanning behavior when reading graph mazes we conducted a comparative eye tracking study with 26 male versus female participants of different experience levels while also alternating between orthogonal node-link drawings and graph mazes as well as different graph size levels. The major result of this comparative study is that the participants can track paths in both representation styles, including a geodesic path tendency in their visual search behavior, but typically have a longer fixation duration at branching nodes and locations in the mazes that lead to opposite directions to the geodesic path tendency, maybe the viewers had to start a reorientation phase in their visual scanning behavior. We also found out that the size, that is the number of graph vertices has an impact on the visual scanning behavior for both orthogonal node-link diagrams as well as street-like maze representations, but for the mazes we found this impact to be less strong (in terms of the eye movement data metrics fixation durations and saccade lengths) compared to the node-link diagrams. To conclude the article, we discuss limitations and scalability issues of our approach. Moreover, we give an outlook and future work for possible extensions.

Eye tracking studies are more complicated to design, conduct, and to evaluate than traditional studies solely based on performance measures like error rates and response times. This is typically due to the more complex hardware setup, the calibration procedures, and the spatio-temporal nature of the recorded data that must be analyzed, visualized, or statistically evaluated. As a benefit, eye movement data contains patterns of visual attention over space and time that are not observable in standard error rates, completion times, and qualitative feedback. Students in the field of visualization, human-computer interaction, and user experience represent an interest group that would benefit from the application of eye tracking during their studies and in their future careers. Consequently, instructing them how to design, setup, conduct, and evaluate an eye tracking study is of special interest to current researchers involved in teaching. We describe education in eye tracking in five courses with 79 students from bachelor, master, and PhD levels. We outline our concept and discuss the challenges to raise people with no experience in eye tracking to a level of knowledge that allows them to apply this emerging technology to different scenarios including visual stimuli and related research questions. We discuss our teaching strategy in two course setups (summer school and traditional university lecture), the results of the students' eye tracking studies, and which challenges they and the teachers faced during the course.

Traffic circles, also known as roundabouts, are a modern way to improve traffic situations and in particular, the traffic flow which leads to a decrease in the number of accidents and air pollution. Due to these benefits there is an increasing number of traffic circles in the world with various properties, forms, shapes, colors, and several other visual features and enhancements, even multi-lane versions. However, they also come with a multitude of drawbacks. Since there exist really many of them and there is no concrete database containing information about them, it would be beneficial to quickly identify them on users’ or algorithms’ demands, for example, to analyze the impact of traffic flow over longer time periods, in certain traffic situations, as well as in certain geographic regions. To support such a quick traffic circle identification we apply several deep learning methods by training models with and without data augmentation, coming in the form of Edge Detection (ED) as well as Circle Hough Transform (CHT). With these techniques we are able to categorize whether a certain geographic region contains a traffic circle or not, as a pre-identification step to other traffic-related post-analyses. Consequently, the resulting binary classification can quickly provide a list of traffic circles in the geography to offer a starting point for further explorations and analyses based on the list of identified circles. We illustrate the usefulness and results of our method by applying it to geographic regions in Switzerland by using more than 3,000 satellite images while we also compare several such detection methods like ResNet50 or VGG16. Finally, we discuss scalability and limitation issues of the described methods and present some ideas for possible future work.

We introduce FitYou, an interactive dashboard for health data visualizations from wearable devices. It supports the exploration of health trends and health attribute correlations. Wearable devices are small enough to wear on the body and that can be equipped with sensors to track various physical and physiological attributes like heart rate, steps taken, and sleep quality, to just mention a few. By exploiting those attribute values we can visually monitor health-related issues, for example during sports activities like training, a sports competition, or a rehabilitation phase. Activity levels are hard to detect without such support but can help to improve someone’s overall well-being. We illustrate the usefulness of FitYou by means of some application examples. Finally, we discuss the challenges and limitations of the dashboard by focusing on algorithmic, visual, and perceptual issues.

Event sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position double trees as a domain‐agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour‐coded categories. We integrate the double tree and category‐based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non‐spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach.

Interactive visualization and visual analytics tools have been designed and developed in the past and will be developed in the future as well. In each application domain in which data is measured, generated, and recorded we see a potential candidate for an interactive visualization tool with the goal to find insights and knowledge in the data. This knowledge can be found either visually by humans' interventions or algorithmically by the machine, in the best case by applying both concepts in combination as in visual analytics. One of the easiest ways to get an interactive visualization tool running is by means of dashboards, typically implemented as webpages that can run in a web browser and are accessible online, creating some kind of web-based solution. This book describes ways to design and implement dashboards based on the programming language Python, the graphics library Plotly, and Dash. The readers can use the provided dashboard codes as a starting point and extend the functionality and features on their desire. Technical topics discussed in the book include: Design in visualization Interaction principles in information visualization User interface design Linking Python, Dash, and Plotly Coding in Python Dashboard examples with Python code.

In the roles of lecturers we have to present certain topics to students by means of Powerpoint slides, videos, animations, and the like. The content itself but also the presentation speed play crucial roles to make a lecture understandable for the students. Since each student has a different experience level, personal mood, or might be distracted by other scenarios the lecturers cannot keep track of all the individual students in a course but has to present the content more or less independently of the audience. In this paper we introduce work-in-progress that focuses on monitoring the students’ behavior in a course including the understanding of the presentation in terms of presentation speed. To reach this goal we make use of mobile eye tracking devices that permanently track the eye movements of the students paying visual attention to the lecturer’s slides. In the current state of the developed system we record two options, whether an individual student could follow and understand the slides or not while the summed up students’ feedback is reflected and displayed to the teacher in real-time, providing some kind of overview and avoiding asking questions permanently about the understanding of the slides’ contents. Finally, we will discuss challenges and limitations of our gaze-based monitoring system.