Module 4: Visualizing Time Series Data

Hi everyone!

This week we explored time series data and the basics of statistics. The given data set is a Monthly Modal Time Series of public transit data for many U.S cities including information such as vehicle revenue hours from 2014 to 2019.

I chose to keep the city, year, UZA sq miles (urban area in miles), ridership (total population that rode public transit), vehicle revenue hours (travel hours), and vehicle revenue miles (travel miles) columns in this dataset for my analysis.

There are too many cities in this dataset, so I ordered the cities from largest to smallest Primary UZA Sq Miles to focus my visualization on only the top 5 urban areas which are Atlanta, Boston, Chicago, New York, and Philadelphia. I chose the top 5 because they have the most potential to use public transit making an interesting visualization.

My line graph showcases each of these major cities' percent difference in average ridership every year from 2014 to 2019 relative to the ridership in 2014. The line graph shows Chicago had a sharp increase in people utilizing public transit in 2018 while Boston and Philadelphia saw slight increases in ridership compared to 2014. Interestingly, Atlanta saw a consistent and dramatic percent decrease in ridership every year compared to 2014 with 2019 showing about a 90% decrease. Through this visualization, we can easily see which major cities are growing in public transit popularity versus others.


An important factor to utilize public transit is how far and long the transit is readily available. To understand how long each city's vehicle revenue hours are, I created a stacked bar chart visualization showcasing the same 5 cities and their average vehicle revenue hours for each year from 2014-2019. While overall 2018 was the year with the highest hours spent on the road, we can see Chicago spent the most time every year which corresponds to their increased ridership. New York is rather stable in its revenue hours which conflicts with the sharp decrease in ridership seen in 2019 in the graph above.


I found using Tableau to create these visualizations made the analysis process so much easier. Without writing code for filtering and calculations, I could choose percent difference calculations on my columns to create these descriptive graphs!

-Ramya's POV

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