The outcasts series: motion
When one starts to turn data into visualization and stories told in a visual form, sooner or later she meets with the famous Bertin matrix, that describes the relationship between data variables and visual variables. Since 1967 many authors have created their own matrix and added or removed several from the original. Mostly added.
Thus surface, volume and motion were added to the list. In this article I’ll examine the last one of these, motion. The original idea to add it to the list comes from Linda Ruth Bartram (Bartram, 1998), and it is a really important addition. The reason behind why Bertin didn’t care much about this variable is as he states his work is applicable only within certain conditions: paper, certain amount of light, certain viewing distance. As computers became more and more common, and the screen resolution became better, these constraints started to disappear and the motion variable was also included in the list.
As with quantitative values we are always interested in comparison: how many times of “X”, how much greater is that? When using the motion visual variable, we formulate questions such as: how fast is that compared to “X”? Although a visually appealing representation because of it’s deep connection to our vision, it is not really easy to use it for exact measures. I’m not sure if anyone can tell how many times faster something is than something else. This is just out of scope for our comprehension — unlike length for example, that we can use to make such judgements.
To demonstrate the real power of this visual variable I’ve chosen a topic that is affecting our lives for more than a year now: COVID19. We kind of got used to the charts that cleverly show how infection rates change, how the vaccination process goes. But these are line graphs that depict a static view of aggregated values. We can use them as a basis for our discussions on trends and successful or unsuccessful epidemic-strategies, but we cannot see how fast this infection is.
Maps are great if we want to show geographical distribution of a statistical variable — in this case the confirmed COVID19 cases — so we can see the overall status for all countries around the globe at a glance. The one above from WHO is a great example of that.
One can use charts to depict trends over time, show how waves appear over months and affect the global population. We can see quite many different versions of these charts, for example this one from Átló, where they show how the vaccination process goes in Hungary.
The above charts are considered being exemplary, as they show different angles of the story in different lights. But they have something in common: they don’t show transition, they are not personal. How can one make COVID19 data personal? One way that I thought of it is that it is possible to show the transition time by which one person gets infected, or worse: dies. If it is possible to show the infection rate in a way that is understandable to the reader, then maybe it is easier to understand why COVID19 is so dangerous. When observing the problem I decided to make a visualization in P5.js where one can utilize different tools to create interactive custom visualizations to show how fast is the above mentioned rate by country.
Here you can find a short “movie” about the process, but you can also check the visualization here: https://masikmisi.github.io/covid/
I just included a few countries for now, so there’s room for improvement.
Hope it ends soon.
