Essential Workers in Austin, Tx: Data Analysis on their Exposure to COVID-19

Dr. Justin Drake, Dr. Dave Semeraro, Dr. Kelly Gaither, Texas Advanced Computing Center, The University of Texas at Austin, 10100 Burnet Road, Austin TX 78758

The essential workers are being exploited and are more susceptible to exposure than stay at home workers. The essential workers are exposed when working and commuting to work. Furthermore, essential workers sustain the economy by keeping stores open. Recent research in China has demonstrated that structuring social distancing policies will reduce the spread of COVID-19. Overcrowded areas need to be enforcing these safety guidelines to prevent the spread of COVID-19 to the consumers and essential workers. The research questions are: (1) How far do essential workers travel to get to work? (2) Which locations are densely populated during the pandemic in Austin, TX? Hypothesis:  I expect to find that many essential workers are traveling far and working short hours due to COVID-19 and the majority of the community is following state policy, but there could be overcrowded locations.

Using X-mode data which is obtained through smart phone apps, from March to July 2020, I can track exposure of the essential workers by tracking how far they are commuting to work and how long they work. I created a graph analysis, plot the essential places open in Austin, TX and essential workers’ commute. From these graphs I can conclude that the most popular locations during this pandemic are supermarkets, malls-shopping centers and gas stations. Due to these locations being densely populated, it has a higher risk of exposure, if not following the 6 feet away rule. To conclude, the average travel distance of essential workers is 2.98km and the average working hours is 7.32. Essential workers are at a higher risk of contracting the virus because they are exposed while working and traveling to work.

Additional Abstract Information

Presenter: Mariana Duarte

Institution: California State University - Monterey Bay

Type: Poster

Subject: Computer Science

Status: Approved

Time and Location

Session: Poster 5
Date/Time: Tue 12:30pm-1:30pm
Session Number: 4034