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Visually encoding quantitative information associated with graph links is an important problem in graph visualization. A conventional approach is to vary the thickness of lines to encode the strength of connections in node-link diagrams. In this paper, we present Sticky Links, a novel visual encoding method that draws graph links with stickiness. Taking the metaphor of links with glues, sticky links represent connection strength using spiky shapes, ranging from two broken spikes for weak connections to connected lines for strong connections. We conducted a controlled user study to compare the efficiency and aesthetic appeal of stickiness with conventional thickness encoding. Our results show that stickiness enables more effective and expressive quantitative encoding while maintaining the perception of node connectivity. Participants also found sticky links to be more aesthetic and less visually cluttering than conventional thickness encoding. Overall, our findings suggest that sticky links offer a promising alternative to conventional methods for encoding quantitative information in graphs.

Physical activity is extremely important at an older age and has major benefits. There is a range of applications that help maintain physical activity. However, their adoption among older adults is still limited. The purpose of the study is to explore the key aspects of the design of mobile applications that support walking for older adults. We conducted a field study with older adults, aged 69-79 years, using a technology probe (a mobile application developed as an early prototype) with the purpose of eliciting requirements for mobile health applications. We interviewed the participants during and after the study period, asking them about their motivation for walking, usage of the application, and overall preferences when using such technologies. The findings suggest that mobile applications that support walking should address a range of walking variables, support a long-term learning process, and enable the user to take control and responsibility for the walk. In addition, we provide design guidelines concerning the motivation for walking and the data visualization that would make technology adoption easier. The findings from this study can be used to inform the design of more usable products for older users.

Static visual attributes such as color and shape are used with great success in visual charts designed to be displayed in static, hard-copy form. However, nowadays digital displays become ubiquitous in the visualization of any form of data, lifting the confines of static presentations. In this article, we propose incorporating data-driven animations to bring static charts to life, with the purpose of encoding and emphasizing certain attributes of the data. We lay out a design space for data-driven animated effects and experiment with three versatile effects, marching ants, geometry deformation and gradual appearance. For each, we provide practical details regarding their mode of operation and extent of interaction with existing visual encodings. We examine the impact and effectiveness of our enhancements through an empirical user study to assess preference as well as gauge the influence of animated effects on human perception in terms of speed and accuracy of visual understanding.

One of the fundamental tasks in visualization is to compare two or more visual elements. However, it is often difficult to visually differentiate graphical elements encoding a small difference in value, such as the heights of similar bars in bar chart or angles of similar sections in pie chart. Perceptual laws can be used in order to model when and how we perceive this difference. In this work, we model the perception of Just Noticeable Differences (JNDs), the minimum difference in visual attributes that allow faithfully comparing similar elements, in charts. Specifically, we explore the relation between JNDs and two major visual variables: the intensity of visual elements and the distance between them, and study it in three charts: bar chart, pie chart and bubble chart. Through an empirical study, we identify main effects on JND for distance in bar charts, intensity in pie charts, and both distance and intensity in bubble charts. By fitting a linear mixed effects model, we model JND and find that JND grows as the exponential function of variables. We highlight several usage scenarios that make use of the JND modeling in which elements below the fitted JND are detected and enhanced with secondary visual cues for better discrimination.

(1) Background: Mobile phone use during gait is associated with adverse health outcomes, namely increased risk of pedestrian injury. Healthy individuals can voluntarily prioritize concurrent task performance, but the factors underlying the impact of phone use during walking remain largely unknown. Thus, the objective of this work was to evaluate the relationship between subjective (perceived) prioritization, cognitive flexibility and dual-task performance when using a mobile phone during walking. (2) Methods: Thirty young participants walked for one minute with and without reading or texting on a mobile phone, as well as reading or texting while sitting. Walking performance (kinematics) was recorded, as well as phone use (text comprehension, text read/written), mental workload, perceived prioritization (visual analog scale), and cognitive flexibility (trail-making test). (3) Results: Texting while walking was associated with larger decreases in gait speed, larger gait variability, higher mental workload, and lower text comprehension compared to reading. Perceived prioritization was associated with walking dual-task costs (DTCs) (r = 0.39-0.42, p < 0.04) when texting, and better cognitive flexibility was associated with lower gait DTCs when texting (r = 0.55, p = 0.002) but not reading. (4) Conclusions: The context-dependent link between perceived prioritization, cognitive flexibility, and walking DTCs promotes our understanding of the factors underlying texting-while-walking performance. This could identify individuals who are more prone to dual-task interference in this increasingly common and dangerous task.

This work proposes Winglets, an enhancement to the classic scatterplot to better perceptually pronounce multiple classes by improving the perception of association and uncertainty of points to their related cluster. Designed as a pair of dual-sided strokes belonging to a data point, Winglets leverage the Gestalt principle of Closure to shape the perception of the form of the clusters, rather than use an explicit divisive encoding. Through a subtle design of two dominant attributes, length and orientation, Winglets enable viewers to perform a mental completion of the clusters. A controlled user study was conducted to examine the efficiency of Winglets in perceiving the cluster association and the uncertainty of certain points. The results show Winglets form a more prominent association of points into clusters and improve the perception of associating uncertainty.

A composite indicator (CI) is a measuring and benchmark tool used to capture multi-dimensional concepts, such as Information and Communication Technology (ICT) usage. Individual indicators are selected and combined to reflect a phenomena being measured. Visualization of a composite indicator is recommended as a tool to enable interested stakeholders, as well as the public audience, to better understand the indicator components and evolution over time. However, existing CI visualizations introduce a variety of solutions and there is a lack in CI's visualization guidelines. Radial visualizations are popular among these solutions because of CI's inherent multi-dimensionality. Although in dispute, Radar-charts are often used for CI presentation. However, no empirical evidence on Radar's effectiveness and efficiency for common CI tasks is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares the Radar chart technique with two other radial visualization methods: Flower-charts as used in the well-known OECD Betterlife index, and Circle-charts which could be adopted for this purpose. Examples of these charts in the current context are shown in Figure 1. We evaluated these charts, showing the same data with each of the mentioned techniques applying small multiple views for different dimensions of the data. We compared users' performance and preference empirically under a formal task-taxonomy. Results indicate that the Radar chart was the least effective and least liked, while performance of the two other options were mixed and dependent on the task. Results also showed strong preference of participants toward the Flower chart. Summarizing our results, we provide specific design guidelines for composite indicator visualization. Fig. 1: Three radial solutions for composite indicator visualizations compared empirically for users' performance and preferences.