COVID cases are commonly visualized across multiple dashboards as quantile choropleth maps or graduated points around state centroids. County-level visualizations are rare but when viewed, show a dramatically more nuanced and detailed pandemic landscape. We visualize data at both state and county-scale as total cases, deaths, and population-weighted rates to provide a richer understanding of the pandemic. Case information can be explored by clicking on county or state areas to generate pop-up windows, or to change graphs of confirmed cases.
A temporal slider allows the current view to be updated over time, providing users the ability to watch the pandemic emerge over time. See this animation of COVID population centered clusters over time as an example here.
The CSDS team identifies hot spots two ways: using the total number of confirmed cases and by adjusting for population. Because of the infectious nature of COVID, high numbers of cases anywhere will be of concern. At the same time, identifying areas that have a disproportionately high number of cases within the population is needed to locate areas hit hardest by the pandemic. The team further differentiates hot spot clusters and outliers: clusters are counties that have a high number of cases, and are surrounded by counties with a high number of cases. Outliers are areas that have a high number of cases within the county and fewer cases in neighboring counties, highlighting an emerging risk or priority for containment. We utilize local spatial autocorrelation statistics (LISA) using queen-contiguity spatial weights and 999 Monte Carlo permutations to generate these metrics.
The Github for our project is public and available at github.com/GeoDaCenter/covid. Contributions are welcome. Volunteers are encouraged to start on the “Issues” page and identify “good first issues” to maximize help efforts. A public release of the application will continue to be updated with new data and features on a regular basis.
Known limitations exist regarding actual fatality rates, for example, as we may not be aware of the total population infected until testing is expanded. County estimates are in the process of being validated across multiple sources, and may still not be the most updated. Local health departments continue to serve as the most accurate, up-to-date sources. Our team continues to grow new collaborations to better identify the metrics needed for this rapidly changing situation. Issues are encouraged to be posted at github.com/geodacenter/covid/issues and/or by emailing email@example.com directly.