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Timelines are commonly represented on a horizontal line, which is not necessarily the most effective way to visualize temporal event sequences. However, few experiments have evaluated how timeline shape influences task performance. We present the design and results of a controlled experiment run on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape affects task completion time, correctness, and user preference. We tested 12 combinations of 4 shapes --- horizontal line, vertical line, circle, and spiral and 3 data types recurrent, non-recurrent, and mixed event sequences. We found good evidence that timeline shape meaningfully affects user task completion time but not correctness and that users have a strong shape preference. Building on our results, we present design guidelines for creating effective timeline visualizations based on user task and data types. A free copy of this paper, the evaluation stimuli and data, and code are available https://osf.io/qr5yu/
Geographical propagation phenomena occur in multiple domains, such as in epidemiology and social media. Propagation dynamics are often complex, and visualizations play a key role in helping subject-matter experts understand and analyze them. However, there is little empirical data about the effectiveness of the various strategies used to visualize geographical propagation. To fill this gap, we conduct an experiment to evaluate the effectiveness of three strategies: an animated map, small-multiple maps, and a single map with glyphs. We compare them under five tasks that vary in one of the following dimensions: propagation scope, direction, speed, peaks, and spatial jumps. Our results show that small-multiple maps perform best overall, but that the effectiveness of each visualization varies depending on the task considered.
There is an increased use of Internet-of-Things and wearable sensing devices in the urban marathon to ensure effective response to unforeseen medical needs. However, the massive amount of real-time, heterogeneous movement and psychological data of runners impose great challenges on prompt medical incident analysis and intervention. Conventional approaches compile such data into one dashboard visualization to facilitate rapid data absorption but fail to support joint decision-making and operations in medical encounters. In this paper, we present MaraVis, a real-time urban marathon visualization and coordinated intervention system. It first visually summarizes real-time marathon data to facilitate the detection and exploration of possible anomalous events. Then, it calculates an optimal camera route with an arrangement of shots to guide offline effort to catch these events in time with a smooth view transition. We conduct a within-subjects study with two baseline systems to assess the efficacy of MaraVis.
With the advent of new sensors and technologies, smart devices that monitor the level of indoor air quality (IAQ) are increasingly available to create a healthy home environment. However, little has been studied regarding design principles for effective IAQ visualizations to help better understand and improve IAQ. We analyzed Amazon reviews of IAQ monitors and their design components for IAQ visualizations. Based on our findings, we created a conceptual framework to explain the process of facilitating an effective IAQ visualization with a proposed set of design considerations in each stage. The process includes helping users easily understand what is happing to IAQ (awareness), what it means to them (understanding), and what to do with the information (action), which results in two outcomes, knowledge gain and emotional relief. We hope our framework can help practitioners and researchers in designing eco-feedback system and beyond to advance both research and practice.
With the advent of mixed reality devices such as the Microsoft HoloLens, developers have been faced with the challenge to utilize the third dimension in information visualization effectively. Research on stereoscopic devices has shown that three-dimensional representation can improve accuracy in specific tasks (e.g., network visualization). Yet, so far the field has remained mute on the underlying mechanism. Our study systematically investigates the differences in user perception between a regular monitor and a mixed reality device. In a real-life within-subject experiment in the field with twenty-eight investment bankers, we assessed subjective and objective task performance with two- and three-dimensional systems, respectively. We tested accuracy with regard to position, size, and color using single and combined tasks. Our results do not show a significant difference in accuracy between mixed-reality and standard 2D monitor visualizations.