Clusters, Cities, and COVID-19: The Impacts on Employment Centers in Virginia

This project sought to do two things—attempt to understand the still-unfolding impacts of the 2020 COVID-19 pandemic on the city itself, and the ways in which defining that city alters the outcome. More narrowly, I sought to  understand the impact of  COVID-19 on employment centers.  To do this, I employed a Hierarchical Density-based Spatial Clustering of Applications with Noise (HDBSCAN) to create spatial clusters to identify significant clusters of employment using from the U.S. Census Bureau’s synthetic employment data. I measured -COVID-19's impacts through variables such as wages, industry diversity, employment change and changes in sales volume. Throughout this process I sought to refine and explore the clustering methodology and understand its limitations and successes in context with the economic state of Virginia, as an example. The results varied by region, showing that both the impacts of COVID-19, as well as modeling and data, are spatially dependent. I find that there may be a loss in low- to mid-wage jobs, or a movement of wages upwards, that results in better outlooks for the number of high wage jobs post-COVID-19. I also find there is some relation to the impacts of COVID-19 and employment clustering, and there does seem to be potential movement out of centers and into sub- and outer centers, but that is also dependent on what centers they are, and where they are.