Results of the summarizing process for the arXiv paper: 2205.15937v1

The COVID-19 pandemic disrupted the mobility patterns of a majority of Americans beginning in March 2020, leading to changes in park visitation across the United States. Despite the beneficial, socially distanced activity offered by outdoor recreation, confusing and contradictory public health messaging complicated access to natural spaces. Working with a dataset comprising the locations of roughly 50 million distinct mobile devices in 2019 and 2020, Kelsey Linnell et al. analyzed weekly visitation patterns for 8,135 parks across the country to identify regions that experienced a substantial change in visitation in the first few weeks of the pandemic. Using Bayesian inference through their chosen method, BEAST (Bayesian Estimator of Abrupt Change, Seasonality, and Trend), they found that regions that did not exhibit a change were likely to have smaller populations and to have voted more Republican than Democrat in the 2020 elections. The authors also noted that preliminary examination of trends suggested changes in park visitation were not universal. To supplement their analysis on voting patterns and population size as factors affecting park visitation during the pandemic, Linnell et al. retrieved voting data at state and county levels from MIT Election Data and Science Lab as well as county-level population, income, and economic data from the US Census API. Overall, this study contributes to a growing body of literature using passive observations to explore who benefits from access to nature during times of crisis such as pandemics. Further research could investigate other potential factors influencing park visitation during COVID-19 beyond those explored here.

• COVID-19 pandemic disrupted mobility patterns of Americans, leading to changes in park visitation across the US.
• Confusing public health messaging complicated access to natural spaces despite outdoor recreation being a socially distanced activity.
• Kelsey Linnell et al. analyzed weekly visitation patterns for 8,135 parks across the country using a dataset comprising the locations of roughly 50 million distinct mobile devices in 2019 and 2020.
• They used Bayesian inference through their chosen method, BEAST, to identify regions that experienced a substantial change in visitation in the first few weeks of the pandemic.
• Regions that did not exhibit a change were likely to have smaller populations and to have voted more Republican than Democrat in the 2020 elections.
• Preliminary examination of trends suggested changes in park visitation were not universal.
• Linnell et al. retrieved voting data at state and county levels from MIT Election Data and Science Lab as well as county-level population, income, and economic data from the US Census API to supplement their analysis on voting patterns and population size as factors affecting park visitation during the pandemic.
• The study contributes to a growing body of literature using passive observations to explore who benefits from access to nature during times of crisis such as pandemics.
• Further research could investigate other potential factors influencing park visitation during COVID-19 beyond those explored here.

The Impact of the COVID-19 Pandemic on Park Visitation Across the United States

The COVID-19 pandemic has had a drastic impact on our lives, from how we work and learn to how we spend our leisure time. In March 2020, many Americans shifted their mobility patterns in response to public health messaging, leading to changes in park visitation across the United States. Despite outdoor recreation being a beneficial activity that allows for social distancing, access to natural spaces was complicated by confusing and contradictory public health messaging.

Kelsey Linnell et al. sought to identify regions that experienced substantial change in park visitation during the first few weeks of the pandemic using a dataset comprising locations of roughly 50 million distinct mobile devices in 2019 and 2020. To do this, they employed Bayesian inference through their chosen method BEAST (Bayesian Estimator of Abrupt Change, Seasonality, and Trend). They found that regions that did not exhibit a change were likely to have smaller populations and voted more Republican than Democrat in the 2020 elections.

In addition to voting patterns and population size as factors affecting park visitation during COVID-19, Linnell et al. retrieved voting data at state and county levels from MIT Election Data and Science Lab as well as county-level population, income, and economic data from the US Census API. Through their analysis they concluded that changes in park visitation were not universal.

This study contributes to a growing body of literature using passive observations to explore who benefits from access to nature during times of crisis such as pandemics.