A Tool for Visualization and Analysis of Neighbourhoods, Clusters, and Indicators during the COVID-19 Pandemic

Abstract

Visual analytics tools for spatiotemporal analysis can be used to manage and monitor the propagation of an epidemic. The problem is that dashboards encountered in the literature do not take into consideration how the geolocation characteristics, such as socioeconomic indicators, influence the infection risk or other epidemic variables. This analysis can support health officials in managing the outbreak to consider information about indicators in compartment models for propagation prediction and intervention simulation. The objective of this work was to bring widgets that offer a more profound exploration and analysis of the impact of the pandemic on socioeconomic indicators. In our approach, the association of epidemic variables and indicators can be explored with commonly adopted visualization plots. Also, we propose a way to gather the specialist’s risk perception, and as a result, a risk heatmap is produced, allowing the reduction of time series data cluttering. Finally, it is possible to use the risk heatmap information to compare neighbourhoods and socioeconomic indicators by ranking them according to a severity score. Some use cases were performed to demonstrate the use and capability of the proposed widgets.