Abstract
The widespread COVID-19 pandemic fundamentally changed many people’s ways of life. With the necessity of social distancing and lock downs across the United States, evidence shows more people engage in outdoor activities. With the utilization of location-based service (LBS) data, we seek to explore how visitation patterns to national parks changed among communities of color during the COVID-19 pandemic. Our results show that visitation rates to national parks located closer than 347 km to individuals have increased amidst the pandemic, but the converse was demonstrated amongst parks located further than 347 km from individuals. More importantly, COVID-19 has adversely impacted visitation figures amongst non-white and Native American communities, with visitation volumes declining if these communities are situated further from national parks. Our results show disproportionately low-representations amongst national park visitors from these communities of color. African American communities display a particularly concerning trend whereby their visitation to national parks is substantially lower amongst communities closer to national parks.
| Original language | English |
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| Article number | 13398 |
| Journal | Scientific Reports |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2022 |
Funding
The authors would like to thank the University of Warwick Library for their support to this publication. The second author acknowledges financial support from the Social Science Research Institute, the Institute for Computational and Data Sciences, and the Center for Social Data Analytics of Penn State University for conducting this research. The authors would like to acknowledge the following individuals for programming support: Thomas Zhao, Carol Li, Hojin Ryo, Benjamin Crooks, Evan Menendez, Jinhee Lee, and Xueshi Bai. The project was initiated by the Learning Factory in the College of Engineering of Penn State University. The authors will also like to thank the anonymous reviewers for their comments. The authors would like to thank the University of Warwick Library for their support to this publication. The second author acknowledges financial support from the Social Science Research Institute, the Institute for Computational and Data Sciences, and the Center for Social Data Analytics of Penn State University for conducting this research. The authors would like to acknowledge the following individuals for programming support: Thomas Zhao, Carol Li, Hojin Ryo, Benjamin Crooks, Evan Menendez, Jinhee Lee, and Xueshi Bai. The project was initiated by the Learning Factory in the College of Engineering of Penn State University. The authors will also like to thank the anonymous reviewers for their comments.
| Funders |
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| Pennsylvania State University |