Abstract
Abstract. The spread of COVID-19 has motivated a wide interest in visualization tools to represent the pandemic’s spatio-temporal evolution. This tools usually rely on dashboard environments which depict COVID-19 data as temporal series related to different indicators (number of cases, deaths) calculated for several spatial entities at different scales (countries or regions). In these tools, diagrams (line charts or histograms) display the temporal component of data, and 2D cartographic representations display the spatial distribution of data at one moment in time. In this paper, we aim at proposing novel visualization designs in order to help medical experts to detect spatio-temporal structures such as clusters of cases and spatial axes of propagation of the epidemic, through a visual analysis of detailed COVID-19 event data. In this context, we investigate and revisit two visualizations, one based on the Growth Ring Map technique and the other based on the space-time cube applied on a spatial hexagonal grid. We assess the potential of these visualizations for the visual analysis of COVID-19 event data, through two proofs of concept using synthetic cases data and web-based prototypes. The Grow Ring Map visualization appears to facilitate the identification of clusters and propagation axes in the cases distribution, while the space-time cube appears to be suited for the identification of local temporal trends.
Highlights
The spread of COVID-19 has quickly motivated creating and sharing diverse visualizations to represent the spatiotemporal evolution of the pandemic for various purposes of communication, analysis and decision-making (Muller and Louwsma, 2021)
The temporal component of COVID-19 data is visualized in these environments through one temporal diagram(s): representation of the linear evolution of cases at a global scale through a linear curve or a histogram3; representation of the linear evolution of the number of cases at different spatial locations through a collection of temporal diagrams4; branching time representations5, highlighting decisive events corresponding to the birth of new versions of the virus
Given the evidence that 2D views and 3D views might be complementary, we propose using, in parallel, a 2D view using visual variables to represent the temporal component of data and a 3D view using the third dimension to depict time to represent disease events
Summary
The spread of COVID-19 has quickly motivated creating and sharing diverse visualizations to represent the spatiotemporal evolution of the pandemic for various purposes of communication, analysis and decision-making (Muller and Louwsma, 2021). A lot of these visualizations propose to explore the distribution of COVID-19-related events, such as cases’ occurrences and deaths along time and space, into multi-view “dashboard” environments where the temporal and spatial components of the data are visualized in dedicated synchronized windows (Kraak and Ormeling, 2011), such as the John Hopkins dashboard or the World Health Organization dashboard tracker. The temporal component of COVID-19 data is visualized in these environments through one (or several) temporal diagram(s): representation of the linear evolution of cases at a global scale through a linear curve or a histogram; representation of the linear evolution of the number of cases at different spatial locations through a collection of temporal diagrams; branching time representations, highlighting decisive events corresponding to the birth of new versions of the virus. In order to transcribe the dynamics of the phenomena, some environments propose animation techniques, allowing the user to successively visualize the spatial component of data for different timesteps
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