Covid-19 Travel Analysis
How did Covid-19 affect travel in The United States?
In this post we are going to showcase how Hal9 can be used to process travel data from the TSA and visualize multiple years of travel information simultaneously. The following chart shows the first half of 2020 severely impacted compared to previous years, and that travel in 2021 has not yet fully recovered to 2019 levels.
What is Hal9 Doing?
The Hal9 pipeline uses web-scraping to retrieve data from the TSA checkpoint travel numbers web page, this data helps us begin creating the visualization. You can view this link from Hal9 to view the full pipeline of the data visualization.
Once the TSA's web table is loaded into Hal9, the ‘Map’ function is used to clean the data so that the visualization can be created. For the TSA data, we needed to pull the date, and how many travelers passed through the TSA in 2019, 2020, and 2021. By using the ‘Filter’ functions, we are able to select dates for which the traveler data from all three years is available. This allows us to set up the traveler data as three separate lines mapped simultaneously to create this overlaid visualization.
What can you do?
Using Hal9, we are able to pull a data table and quickly visualize and compare data over several years. Users of Hal9 would similarly be able to utilize data and simultaneously compare multiple data points on a graph simultaneously. Comparing products, tracking users, purchases, are all just some of the possibilities similar to the visualization above.
If you’re ready for a bigger challenge, you can create entirely new data sources, transformations, visualizations or predictive models, and contribute them to our open source GitHub repo.
What do you think of this visualization? What kinds of visualizations would you want to be explained to you by our Hal9 Team? Let us know!