ABSTRACT
Health surveillance and assessment are considered essential components of a functional public health system. The recent ubiquity of mobile devices and social media have created a wealth of behavioral data, and bring into existence new forms of population health monitoring. These new digital sources can provide direct and passive data for more detailed and nuanced health factors, and have expanded the human, spatial, and temporal scales at which these factors can be measured. In this project, I leverage digital trace data from tweets and mobile device location pings to explore population scale sleep loss, and nature exposure through park visitations in the United States. Both sleep and nature exposure are essential contributors to well-being, and have historically relied on either survey data or direct observation of individuals to measure. I begin by demonstrating the ability of Twitter data to passively reflect population-scale sleep loss at the state level. This is followed by an exploration of park visitation measured through mobile device GPS data. Changes in county-scale park visitation behavior at the onset of the COVID-19 pandemic are analyzed and comparisons are made using population density, employment sector, income, and voting records. In the final chapter I investigate the viability of predicting park visitation using demographic information from the surrounding neighborhood. I conclude with a brief discussion of the significance of measuring these behaviors, and the potential for health policy improvement. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
The COVID-19 pandemic disrupted the mobility patterns of a majority of Americans beginning in March 2020. 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, we analyze weekly visitation patterns for 8,135 parks across the United States. Using Bayesian inference, we identify regions that experienced a substantial change in visitation in the first few weeks of the pandemic. We find 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. Our study contributes to a growing body of literature using passive observations to explore who benefits from access to nature.
ABSTRACT
Health surveillance and assessment are considered essential components of a functional public health system. The recent ubiquity of mobile devices and social media have created a wealth of behavioral data, and bring into existence new forms of population health monitoring. These new digital sources can provide direct and passive data for more detailed and nuanced health factors, and have expanded the human, spatial, and temporal scales at which these factors can be measured. In this project, I leverage digital trace data from tweets and mobile device location pings to explore population scale sleep loss, and nature exposure through park visitations in the United States. Both sleep and nature exposure are essential contributors to well-being, and have historically relied on either survey data or direct observation of individuals to measure. I begin by demonstrating the ability of Twitter data to passively reflect population-scale sleep loss at the state level. This is followed by an exploration of park visitation measured through mobile device GPS data. Changes in county-scale park visitation behavior at the onset of the COVID-19 pandemic are analyzed and comparisons are made using population density, employment sector, income, and voting records. In the final chapter I investigate the viability of predicting park visitation using demographic information from the surrounding neighborhood. I conclude with a brief discussion of the significance of measuring these behaviors, and the potential for health policy improvement. (PsycInfo Database Record (c) 2022 APA, all rights reserved)