As COVID-19 became a pandemic in the United States, both counties and states began implementing their own measures to slow the spread of the virus. The springboard for this project was the COVID-19 Spatial Response project led by the University of Arizona. Our team was tasked with the objective to contribute to the project by aggregating intervention data to reveal how COVID-19 interventions, specifically Stay at Home orders (SAH), were contributing to the flattening of the curve of infections across the United States.

This objective, along with other COVID-19 related data science projects are widespread, but a robust, verified, and highly cited dataset is less ubiquitous. Data was gathered from a variety of sources including Google, The New York Times, other crowdsourced intervention databases, and various local news agencies. The information that lies in the data is validated through these sources and is provided in a publicly accessible table through Google. Analysis was undertaken to highlight changes and trends in county mobility, cases, and SAH interventions. Visualization is provided through plots, and graphs to better understand the trends of the data under various conditions (date, cases, political party, and population). Built off of R , this project provides a diverse set of analysis, datasets, and visualization techniques.