Modeling the spatial factors of COVID-19 in New York City
Abstract
This paper investigates spatial factors influencing COVID-19 transmission in New York City, utilizing ordinary least squares regression and geographically weighted regression. Findings suggest that medical density, green space density, mean distance traveled, male percentage, and commuting modes correlate with higher rates of COVID-19 cases, while areas with high percentages of remote work and white population exhibit lower case rates. Distinct associations are observed across different zip code areas or clusters. The study emphasizes the importance of public sanitation, particularly in areas with high public transportation demand, and underscores the significant impact of travel reduction in delaying outbreaks. Policymakers are advised to tailor policies, prevention measures, and reopening strategies based on localized situations to effectively manage COVID-19 outbreaks. Keywords: Geographically weighted regression, COVID-19.
This project was supported by the Good System Grand Challenge and the Cooperative Mobility for Competitive Megaregions (CM2) center both at The University of Texas at Austin.
