This is just one example, but if you have other types of data, you will need a guide in order to determine which visual encoding method is best for you. Take a look at the image below, it provides a neat priority guideline by which your data should be mapped.
Across the board, any time you can use positional data it is in your best interest. However, positional data must not be taken lightly as you can see in the example below. In the first chart, we see a visualization trying to indicate cars being sold across various countries, but there is a problem. In this case, a nominal attribute (country) being mapped by length, which does not help us understand the data very well. Let’s try mapping this data another way.
Below, you can see that both attributes have been mapped by position, which allows us to learn more about the data. This is much better. It also allows the reader to interpret new possibilities, unlike our previous example, which is always a good thing.
One other asset you may be familiar with is our guide to choosing the right visualizationfrom my first blog post. For a popular chart like a scatterplot, if you were to map data this way, it would make more sense (using the data guide you see three images above) to utilize the size of the dots over multiple colors when looking at interval/ratio data. There are many more other factors to consider, but you will be in good shape if you remember the following:
For Nominal data: No one value is more important than the next: while position is best, circles and squares will can be helpful to display your data.
For Ordinal data: Because you are trying to map data with an inherent ranking, the light and dark tones of shading will further emphasize your data’s importance.
For Interval/Ratio data: You are looking to map numerical values, therefore the best way to measure those values is through position or length.
I hope that these guides and graphics have been helpful for you. Be sure to stay on the lookout for my next post that addresses the Third (and final) Pillar of Mapping Data to Visualizations: Usage.