Flowdashboard: authoring pandemic dashboards with a transparent flow model

• Published in: Journal of visualization Vol. 27; no. 5; pp. 941 - 961
• Main Authors: Wang, Guijuan, Wu, Yadong, Wang, Jiansong, Guo, Hao, Zhao, Weixin, Luo, Changwei, Tong, Lu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024; Springer Nature B.V
• Subjects: Classical and Continuum Physics; Communication; Computer Imaging; Dashboards; Engineering; Engineering Fluid Dynamics; Engineering Thermodynamics; Heat and Mass Transfer; Pandemics; Pattern Recognition and Graphics; Qualitative analysis; Regular Paper; State-of-the-art reviews; Vision; Pandemic dashboards; Transparency; Domain-specific; Flow model; Visualization tools
• ISSN: 1343-8875; 1875-8975
• DOI: 10.1007/s12650-024-00994-y

Data dashboards with intuitive visualizations make information more accessible and provide a more in-depth explanation. They have emerged as a crucial tool for effectively communicating pandemic information to wide-ranging audiences. However, the urgency and high-impact nature of pandemics requires rapid and trustworthy dashboard creation. Studies shown that information transparency plays a pivotal role in building trust. Therefore, in this paper, we present FlowDashboard, a domain-specific visualization framework that enables users to create pandemic dashboards quickly and transparently. Our design for FlowDashboard is guided by qualitative analysis of 207 practical pandemic dashboards. Based on the identified key requirements of speed and transparency, a novel transparent flow model called TransFlow is proposed as the core dashboard creation approach. This model formalizes intuitive flow diagram design to construct interactive dashboards, making it easy to learn and revealing the underlying data and interaction flows at property level. Additionally, the FlowDashboard framework accommodates all common components used in practical pandemic dashboards, and incorporate the pandemic gallery as an interface to facilitate users quickly learning the design space. Through use cases, user study and comparisons to state-of-the-art works, we demonstrate the usability and effectiveness of our framework. Graphical abstract