RESUMEN
The health and societal impacts of COVID-19 have created tremendous interest in the scientific community, resulting in interdisciplinary research teams that combine their expertise to provide new insights into the epidemic. However, spatial computation, exploratory data analysis, and spatial data exploration tools have yet to be integrated into these dashboards. Despite the availability of these tools, many of the existing COVID-19 dashboards have provided a limited set of data (i.e., last week's total cases), which limits the user's ability to interact with or customize the data visualization. We present a Spatial Online Analytical Platform that integrates spatial analysis tools that enable users to explore and learn more about spatial patterns of COVID-19. We present three interaction classes designed to support users' needs for knowledge about COVID-19 data trends. Our first interaction class allows users to apply user-defined data classifications (i.e., quantile, equal interval, user-defined) and map color choices. The second interaction class applies a risk index across the time series, informing users of the recent temporal trends. The third interaction class allows users to hypothesize about the presence of spatial clusters and receive results on demand. Our SOLAP platform supports the data analysis and exploration needs of big spatial-temporal data.