Masked Miles

Our project explores the connection between travel patterns within the U.S. and COVID-19 case rates during the pandemic. To accomplish this, two main datasets were used to complete our research. Our first data set was provided by the U.S. Department of Transportation Bureau of Transportation Statistics, which includes information on trips categorized by distance for each state during the COVID-19 pandemic. Our second data set is from the Centers for Disease and Control Prevention, which covers the weekly United States COVID-19 cases and deaths by state. Additionally, we incorporated a dataset from GitHub by The New York Times on COVID cases in FL counties and a dataset from CA Health and Human Services on COVID cases in CA counties. This enabled us to visualize and compare the percentage of COVID cases across counties in both states.

To complement our datasets, we utilized a variety of books, journal articles, and credible websites to provide broader context and depth to our analysis. These sources offered valuable insights into U.S. travel behavior during the COVID-19 pandemic, helping us understand how patterns in mobility, public health policies, and the pandemic’s progression intersected. These sources provide information on the many social factors involved in the progression of COVID-19 cases and public response, allowing us to further understand the relationship between cases and travel. Drawing on Trouillot’s discussion of the power dynamics involved in the creation of knowledge, we considered how data representation can shape narratives about events like this. This wide variety of perspectives informed our analysis and ensured a more nuanced exploration of the relationship between COVID-19 cases and travel in the U.S., allowing us to approach our research with greater depth and critical awareness.

Our literary sources investigate how travel behaviors and tourism policies were influenced by the COVID-19 pandemic, focusing on CA and FL as case studies. These works compare shifts in mobility, long-distance travel, and regional tourism, while also examining the effects of government-imposed health interventions and economic recovery efforts on both tourists and residents. Considering Trouillot’s argument that historical narratives are shaped by what is included or excluded, we aim to address gaps in understanding how perceived risks, economic motivations, and state-level restrictions influenced travel patterns during this period. By critically analyzing these sources, our project seeks to determine whether COVID-19 outbreaks or travel restrictions may have had a greater impact on U.S. tourism, using the limited, but contrasting scope, of CA and FL as a viewpoint. Through this exploration, we aim to uncover the broader implications of these changes and deepen the understanding of travel behavior during this pandemic.

What makes our project unique is its comparative methodology, focusing on CA and FL to showcase how two states with distinct demographic profiles, policy approaches, and COVID-19 responses shaped travel behaviors and case progression. By examining these contrasts, our research provides valuable insights into the regional variations that defined the dynamics of the pandemic’s impact across the United States, offering a deeper understanding of how differing strategies influenced public health and mobility.

Each dataset from our sources was provided as an individual CSV file, which we exported and saved for our analysis. These datasets allowed us to clean and subset the data, focusing on COVID-19 years and specifically tailoring it to CA and FL for a comparative analysis. Having access to these CSV files was particularly helpful when creating our maps and visualizations.

The datasets were relatively large. The “Trips by Distance” dataset includes daily travel frequency for each U.S. state, while the “COVID Cases in the U.S.” dataset provides weekly data on COVID cases and deaths by state. We used Python to handle our large data cleaning, selecting the states and time intervals most relevant to our research and transforming the travel dataset to a weekly scale for consistency.

For our visualizations, we used a combination of Tableau and R. Tableau was our main tool for creating data visualizations because of its versatility and ability to easily craft visualizations that effectively conveyed our narrative. This ensured the data was presented clearly while minimizing the chance for misinterpretation. Tableau is a free, accessible tool that was introduced to in class, making it ideal for all team members to contribute. Some team members also used R for visualization because of their familiarity with the language.

Our maps were created with Flourish and Tableau which were key in showing the geographical and temporal contexts of the relationship between travel and COVID-19, especially when examining California and Florida. These visualizations were useful in highlighting how contrasting policies and attitudes toward the pandemic influenced travel patterns and COVID-19 rates during this time.

To bring our project and research to life, we used WordPress to build and design our website. WordPress is a popular content management system that is free, easy to use, and offers a wide variety of themes and plugins for customization. Additionally, WordPress allows for seamless integration with various hosting services and also allows for the combination of visualizations from outside resources like Tableau and Flourish.