Learning Advanced Tableau Through DataCamp — Part 2

Continuing along with the course, the next section focused primarily on developing richer map visualizations. This was definitely the section I was most excited for in the course. From the initial glimpses of working with geographical data in the previous course, I was aware that Tableau allowed for greater detail and feature to maps that really help provide additional insights to our data.

I was originally planning to make this blog about the last two sections of the course, but the amount of information and features I learned warranted me to write more in detail. This section allowed me to delve deeper into mapping data and bring out insights that were hidden from my initial perspective.

Mapping Data:

The course continues to present its topics from the perspectives of Divvy, a bike share program based in Chicago. In this section of the course, we cover mapping and how it can be used to give insights and reveal trends. For example, mapping bikers’ activity can help to highlight which stations are most popular and are in need of more bikes. This section expands further on mapping visualization that Tableau allows for, such as creating density maps and using Pages to show changes.

The Pages aspect of Tableau helps to make mapping time-lapse data the most insightful and fascinating. It allows you to see how a specific field, in this instance time, affects the rest of the data. We can manually change the pages or have it automatically play and provide a moving visualization of how the data changed over time. For example, we can visualize bike trips for every month in 2019 and see how it changes throughout the year.

Visualization showing bike activity for January 2019 and June 2019. Note the increased bike activity in June by the color and size of the trip count bubbles.

Another handy feature with creating maps in Tableau is the ability to create layered maps. In Tableau, you can create a visualization containing two maps with different features, however you may want to plot multiple features upon the same map. You can create what are called dual-axis or layered maps in Tableau that will allow you to do so. Say that you wanted to see which bike stations are most in need of an increase in the number of bikes available. A way of going about finding this out would be to plot the number of docks available at each station as well as the number of trips made from there. If we notice a high number of trips in proportion to the number of docks, we know to allocate more resources to those stations.

Visualization showing the number of docks at each station and the number of trips made from that station. Note the color difference highlighting which stations are busiest and are most likely in need of more stations.

Another aspect of Tableau that was used in this section was quick table calculations. These are calculations that can be applied to all the values in a visualization, such as running total, percent of total, or rank. These calculations are great when we want to provide a visualization for questions such as what are the top 20 stations based on the number of trips.

Visualization showing the top 20 stations based on the number of trips out of the station.

I was blown away with all the visualizations and extra details I learned about in this section. Not only the ways we can tell a story about our data, but features like the extra settings for tooltips and details that help to make our visualizations pop. A great visualization is one that not only presents its point effectively, but one that is visually appealing to look at. I’ve greatly enjoyed what I’ve learned up to this point and I am interested at applying some of these map examples towards datasets that I’ve worked on in the past. From watching what these maps can reveal, I am excited to see what insights I may discover when looking at my datasets with this newfound knowledge.