This research examines one type of government non-pharmaceutical intervention (NPI) in the COVID-19 pandemic: government mandates that closed or placed limitations on business establishments and community organizations. Pandemics demand that governments respond to the public health crisis to curb the spread of the virus and prevent excess death. The aim of this project is to see if these closures affected people?s visits to establishments, and in turn, whether their visits slowed or accelerated the spread of the SARS-CoV-2 virus, cause of COVID-19 disease. To study this, cell-phone data that recorded visits to a variety of neighborhood establishments from February 1, 2020 to February 28, 2022 are used. First examined is whether local governments? closures of businesses affected people?s visiting patterns and if this was contingent on the political atmosphere, built environment, and composition of the metropolitan area and neighborhoods. Second examined is whether visits to establishments both within and outside the neighborhood relate to case rates within the neighborhood over time. The results of this research will inform public health departments and local governments on the effectiveness of this kind of NPI and the role that these visits to establishments play in the spread of an infectious disease like SARS-CoV-2. This study examines twelve urban areas with different pandemic profiles, i.e., those that had few and multiple NPIs and high and low infections during the study period. Data examined are case rates at the zip code level and NPIs from various web sources that describe cases and initiatives at the zip, city, county, and state levels. These data are paired with data from the Census Bureau?s American Community Survey (ACS) on neighborhood demographics and weekly SafeGraph data on visits to neighborhood establishments and travel patterns across neighborhoods. The latter are based on the presence of cell phones at the sites of specific establishments within an urban zip code and also tell how long patrons visited these establishments. The SafeGraph data also have information on the ?home? block group of those who visited establishments. This enables knowledge of where residents of a given zip code traveled during the pandemic and if their visiting habits subsequently affected case rates in their home zip code. Weekly data on 29 different types of establishments are included. Analysis is through application of a fixed-slope multilevel model with spatial dependence error term. This research permits elucidation of how different kinds of public health decisions, especially with regard to business closures, relate to health outcomes across areas. Since closures are so impactful for the economy and personal well-being, understanding which types of organizations contributed more to the spread of SARS-CoV-2 throughout the pandemic will help inform data driven public health decisions that limit the activity of different businesses and organizations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.