Using Big Data to Assess Park System Performance during the COVID-19 Pandemic

Abstract

Parks provide essential services to urban dwellers, but the global COVID-19 pandemic significantly disrupted park usage. Despite this, little is known about the adaptation of visiting behaviors by the public and how visitation patterns vary across different types of parks. In this study, we utilized SafeGraph cellular human movement data to compare park visits in Tucson, Arizona (USA) before and during the pandemic (2019 vs. 2020). We reviewed park management measures in response to the pandemic alongside park visit data. Furthermore, we conducted a GIS analysis to compare the changes in park visits across different park types throughout various days and months. Results indicate that (1) fluctuations in park visits are strongly correlated with COVID-19-related measures; (2) different types of parks experience vastly different processes of visit decline and recovery; (3) river and linear parks maintain their appeal, likely due to the perception of reduced virus transmission risk associated with their primary activities, such as walking and bicycling; and (4) the contrast between weekend and weekday visit patterns reflects the extent of the pandemic impact. These findings offer valuable guidance for park management and park usage, attendance prediction, and design adaptations for future pandemics. We conclude that SafeGraph big data are effective for evaluating park system performance on a broader scale.