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Flowdashboard: authoring pandemic dashboards with a transparent flow model

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Abstract

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.

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Notes

  1. https://coronavirus.jhu.edu/map.html.

  2. https://covid19.ufrgs.dev/dashboard/#/dashboard.

  3. https://delphi.cmu.edu/covidcast/.

  4. https://www.coronatracker.com/.

  5. https://ncov2019.live/.

  6. https://covid-19.sledilnik.org.

  7. https://covid19.who.int/.

  8. https://vega.github.io/.

  9. https://www.mapbox.com/.

  10. https://d3js.org/.

  11. https://echarts.apache.org/.

  12. https://covid19.who.int/WHO-COVID-19-global-data.csv.

  13. https://github.com/CSSEGISandData/COVID-19.

  14. https://raw.githubusercontent.com/owid/monkeypox/main/owid-monkeypox-data.csv.

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (No.61872304) and the Talent Project of Sichuan University of Science and Engineering (No.2020RC20).

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Correspondence to Yadong Wu.

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Wang, G., Wu, Y., Wang, J. et al. Flowdashboard: authoring pandemic dashboards with a transparent flow model. J Vis (2024). https://doi.org/10.1007/s12650-024-00994-y

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