Abstract
New York City (NYC) was deeply impacted by COVID-19 in spring 2020, with thousands of new cases daily. However, the pandemic's effects were not evenly distributed across the city, and the specific contributors have not yet been systematically considered. To help investigate that topic, this study analyzed the interaction of people with neighborhood businesses and other points of interest (POIs) in parts of three NYC neighborhoods in the spring of 2020 during the peak of the first COVID-19 wave through anonymized cellphone data and direct the observation of 1313 individuals leaving healthcare facilities. This study considered social vulnerability index (SVI) levels, population density, and POI visit behaviors from both cellphone data and firsthand observations of behavior around select NYC health facilities in different boroughs as various proxies. By considering equivalent businesses or groups of businesses by neighborhood, POI visits better aligned with COVID-19 infection levels than SVI. If tracking POI visit levels proves a reliable direct or relative proxy for disease transmission when checked against larger datasets, this method could be critical in both predictions of future outbreaks and the setting of customer density limits.