Module 2—My Geographic Map of COVID-19 Case Rates

For this week’s assignment, I was instructed to find a dataset and create a geographic map using Tableau. I selected a COVID-19 dataset from Data World, which is based on the Johns Hopkins University Coronavirus Case Tracker. The website summarizes the data as such:

“This dataset combines Johns Hopkins CSSE coronavirus data with U.S. Census population data and urban/rural designations, providing case and death rates per 100,000 people.”

At first, I had some difficulty finding a dataset that included everything I needed because many sources required a subscription. After a bit of research, I found this open dataset and thought it would be interesting to work with. Since this was my first time using Tableau, I also had to learn how to connect the dataset, geocode the fields, and experiment with different ways to visualize the data.

County-Level COVID-19 Cases

This map displays all U.S. counties from the dataset using the County field. Each green point represents a county where COVID-19 data is reported. At this level of detail, clusters emerge around more populated regions, while more rural areas appear sparser.

 

COVID-19 Deaths by County 

In this map, counties are represented by circles. The size of each circle is proportional to deaths per 100,000 residents, meaning larger bubbles correspond to counties with higher death rates relative to population size.

This view provides a focused perspective on the pandemic’s severity by emphasizing geographic variations in mortality, allowing for quick identification of the hardest-hit areas.

  

Challenges I Faced

Because this was my first time using Tableau, I needed to spend time learning the basics of connecting data and formatting geographic fields. Another challenge was choosing the right visualization style; county level points gave a very detailed picture, but adding proportional bubbles helped highlight mortality patterns more clearly. 

Reflect on Visual Grammar

Color scales: Adding a clear legend would help the audience interpret differences between higher and lower values.

Proximity: Clusters of bubbles in certain regions reveal outbreak patterns that are easy to spot visually.

Tooltips: Interactive tooltips would let viewers explore exact case or death rates without cluttering the map.

Labels: Keeping labels minimal avoids overwhelming the visual, while hover-based labels could provide more detail when needed.