Chart Experiment: Stripe Sparkline

 

Stripe sparkline is a chart I thought of when I was creating my submission for #IronViz 2021. This chart type may not be new, but I have yet to see it popularly used or widely discussed.

I will be using a different format for this round of Chart Experiment. Instead of a tutorial on how to create the chart on Tableau, I will discuss the pros and cons of the chart, along with some of my thoughts on it. For how the chart is done, readers can refer to how to create warming stripes here, a small multiple/trellis chart here, or a sparkline chart here.

According to AnyChart (image above from AnyChart), there are 4 types of sparkline charts. They are 1) line sparklines, 2) sparklines, 3) column sparklines, and 4) win/loss sparklines. Initially, I am thinking that a combination of column and win/loss sparklines will be a combination that tells a more complete story. However, I have realised that the combination will just be a bar chart, and squeezing more information inside a sparkline kind of misses the point of the sparkline. 

As noted on Wikipedia, a sparkline "is a very small line chart, typically drawn without axes or coordinates. It presents the general shape of the variation (typically over time) in some measurements, such as temperature or stock market price, in a simple and highly condensed way... Whereas the typical chart is designed to show as much data as possible and is set off from the flow of text, sparklines are intended to be succinct, memorable, and located where they are discussed."

Taking a second look at win/loss sparklines, I see that the colour codes and direction of each square tell the same story. Hence, I think that reducing the direction of each square still means that the story is still kept. This idea is also inspired by the warming stripes by Ed Hawkins (as seen in the image above). Warming stripes are traditionally used to show temperature. It is self-explanatory in the context of showing temperature changes. For more information on the warming stripes that have become art or sort, Wikipedia does provide a good deal of details. Nonetheless, I think the concept of the warming stripe can be applied outside of visualising temperature. For example, I have used it for a submission (seen in the image below) for #DiversityinData on female CEOs in Fortune 500 companies.

By combining all the above inspirations, I have visualised employment growth data in the same way. In the end, I have gone with 2 colours. There are 2 reasons. First, it is because of how little readers can actually tell the differences of each shade on a colour gradient. Next, it is due to how the colour gradient is controlled by the minimum and maximum values. Using my submission for #DivsersityinData above as an example, despite having a dark shade of pink, the difference between white and dark pink is less than 10%. With that, I have decided to get the best elements of all the above charts and come out with the visualisation below.