The Bay Area Rapid Transit (BART) system connects five Bay Area counties. Prior to the COVID19 Pandemic, ridership consisted of over 400,000 trips on an average weekday (source Bart.gov). Starting in March 2020, BART ridership decreased dramatically due to the social distancing mandates.

As part of CS416 Data Visualization, a graduate level course at the University of Illinois Urbana-Champaign's Master of Computer Science program, I created two visualizations in the Summer of 2022 to explore the relationship. I used tableau to create a dashboard as well as JavaScript to create a narrative visualization. The goal of both visualizations is to put county-level COVID data in communication with station-level ridership data to allow the end user to explore the story of how COVID and public transit in the Bay Area.

1. Narrative Visualization
2. Tableau Dashboard
3. Data Sources
   1. COVID Data from California Department of Public Health
   2. BART Ridership Data
   3. BART Station Location Shape Files
   4. California County Shape Files
4. A Note on the Formal End of COVID-19 Public Health Emergency

Read more about the design of the visualization in this report.

The Narrative Visualization was last refreshed on 5/6/22 with data through 3/31/23.

Read more about the design of the dashboard in this report.

The tableau dashboard was last refreshed on 6/5/22 with data through 5/23/22.

Url: CA CDPH

Key Data Documentation Notes:

• "Data is from the California COVID-19 State Dashboard at https://covid19.ca.gov/state-dashboard/"
• "NOTE: Data is being updated on Tuesdays and Fridays."
• "Data on cases, deaths, and testing is not reported on weekends or state holidays. This data is reported on the first day following the weekend or holiday. All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included."

Clean Up Requirements:

1. Remove State level sums (tableau will calculate this manually)
2. Keep only Bay Area counties with Bart Stations
3. Roll up to the daily level

Used in: Tableau Dashboard and JavaScript Narrative Visualization

Url: Bay Area Rapid Transit > Hourly Data

Key Data Documentation Notes:

• "For those of you looking to take a deeper dive into BART’s data - check out our hourly trip datasets. These files will allow you to analyze trips between all stations in the BART system by hour. The data is organized in the following columns: Date, Hour (24-hour clock), Origin Station, Destination Station, Number of Exits. All stations are abbreviated using the 4-Letter station codes, please refer to the station name abbreviations (.xls) for translation."

Clean up Requirements:

1. Crosswalk between station code and the Station Name
2. Include Station County as a linkage variable
3. Roll up to the monthly level (at the exit-entrant level)

Used in: Tableau Dashboard and JavaScript Narrative Visualization

Url: Bay Area Rapid Transit > KML Format

Key Data Documentation Notes: n/a

Clean up Requirements:

1. Crosswalk between Ridership Data (Station Code) and geographical Bart Station location

Used in: Tableau Dashboard Only

Url: CA Data.gov > CA County Boundaries

Key Data Documentation Notes: n/a

Clean up Requirements:

1. Spatial join in Tabelau

Used in: Tableau Dashboard Only

On February 28, 2023, The COVID-19 State of Emergency ended in California(Source: Office of the Governor of California). At the federal level, the Public Health Emergency for the COVID-19 Pandemic ended on May 11, 2023 (Source: Kaiser Family Foundation [KFF]).

With changes planned to the CDC's COVID data reporting requirements following the end of the Public Health Emergency (Sources: Washington Post, University of Minnesota's Center for Infectious Disease Research and Policy), some experts anticipate there will be changes in the quality (or availability) of publicly accessible COVID-19. Additionally, the funding from both the state and federal government ends, the hospitals and health care providers may not be able to maintain the data at the granularity they previously had.

In the end of 2022, with the widespread use of at home tests as well as attention towards COVID precaution decreasing, many experts already had concerns about the COVID case data being a poor indicator of community transmission. Other data sources like waste water data, which can track prevalence of viral load, or COVID deaths may be better data sources for future analyses.

At this time, I do not plan to conduct future refreshes of the visualizations following the end of the COVID-19 Public Health Emergency. While case count in late 2022 and early 2023 is not the indicator it used to be at the beginning of the pandemic, I still believe there was value in creating the visualizations that put case count in conversation with public transit ridership as part of understanding a historic moment.