COVID-19 Impact on Biking in the US

When COVID-19 caused the US to lock down, many were forced to adapt the way they live their life. This meant no gyms, no going out to eat, no traveling and the list goes on. Those that were fortunate to still work, had to do so from offices in odd locations of their homes, garages, unfinished basements, bedrooms, kid’s playrooms, again, the list goes on. As I am sure many of you can relate, our lives were turned upside down. For many of us, we had to reinvent ourselves, turning to new hobbies to help us cope with isolation. For me, I turned to biking outdoors. Turns out I was not the only one.

This blog is a final project for my Data Visualization Nanodegree at Udacity. For this project I needed to identify a current data visualization, critique it, map out improvements to be made, and implement those improves in creating a new visualization. Fortunately I found a dataset that hits close to home and helped me add a couple more pieces to my puzzle I was trying to solve in April of 2020, when I was trying to buy my wife and I each a bike. That was, “where the hell are all the bikes?”. There were rumors of what might have caused this, things like, manufacture haven’t been able to produce bikes with COVID-19 shutting them down for a couple months, or that everyone all of the sudden picked up biking. Well, I found data backing one of the two mentioned above. Let’s review, but first let’s take a look at the dataset about data collected from numerous bike trails from

Let’s start by taking a look at the initial visualization.

This visualization seems fine as it is somewhat simple to read, however there are still improvements that need to be made. That is why I chose to change the visualization as seen below.!/vizhome/CapstoneBikingdata/Dashboard1

What went well with the original visualization?

1. The colors are visible for those that are color blind

2. It is simple, not a lot of chart junk

3. You can see change over time

4. Legend provided to show the difference between the two lines

What could be improved with the original visualization?

1. It is not clear exactly what it’s counting, provide title for y axis to give additional context

2. Analyze monthly to see how time of year might impact people’s decision to ride, or even events like COVID 19

3. Provide annotation to highlight COVID 19 period

4. After reviewing the data, the title “The Great Bicycle Boom of 2020” is misleading. The data they provided in visual is data from both trail walkers and bikers.

Pre-processing done on the dataset:

1. Downloaded dataset (Excel file) on

2. Reviewed data to make sure it was understood and to identify any missingness in the data

3. Uploaded the file into Tableau

4. Manipulated data to show months instead of weeks. The data only provided the end date of each week. Had to convert that and add each week for each month

How did I improve the visualization?

1. Improved the title

2. First visual was misleading by having title “The Great Bicycle Boom of 2020” and have the data in the chart included both walking and biking, without clearly separating the two.

3. Improved the x and y axis titles

4. Reduced chart junk by removing “week” in every week #

5. Provided annotation to draw viewers attention on what the data set was trying to address. The effects of COVID-19 had on biking

6. Provided additional chart to show the percentage change year-over-year

7. Included text annotation to get viewer more context

Final Thoughts and Conclusion

In this blog we reviewed how we could improve the original visualization and improver the readers experience. I think it’s important to mention that this article was written solely for comparison purposes.

Here is a list of all link and sources used in this article:

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