[This is a guest post by Severino Ribecca*, as part of a series dedicated to each individual kind of chart that he has read into as part of his main research project.]
This will be the first in a series of posts I will be writing, which will look at each individual kind of chart that I’ve read into as part of my main research project. Through reading “A Look at Charts” post series, I hope it will extended your knowledge of data visualization and add to your toolkit when working with data.
I will begin by looking at Arc Diagrams – an attractive alternative to representing two-dimensional node-link diagrams. Arc Diagrams are constructed by placing nodes along a one-dimensional line with the arcs used to show connections between nodes. The thickness of the arc lines can be used to represent frequency or values from the source to the target node. This makes Arc Diagrams useful in finding the co-occurrence and relationships between links.
Martin Wattenberg in his project The Shape of Song (2001) popularized the use of Arc Diagrams by developing a visualization method to display the structure of musical songs, which generates beautiful forms like the one below:
Although, this use of connecting nodes on a singular line has been seen before in 1964 from Thomas L. Saaty’s work: The Minimum Number of Intersections In Complete Graphs. Here Arc Diagrams were used for solely mathematical purposes, such as investigating the crossing numbers of graphs.
Wattenberg may not have been the first to use this technique, but his work helped inspire a number of similar iterations of his version of the Arc Diagram:
As you can see from these examples, Arc Diagrams are Ideal for visualising networks, connections between pieces of information and the distribution of those connections.
What to Watch Out For
The downside to Arc Diagrams, is that they don’t show structure and connections between nodes as well as other diagrams such as a Network Diagram or Tree Diagram.
Also having way too many links can make an Arc Diagram difficult to get any information out of, due to over-cluttering. Arc Diagrams tend to be cluttered enough anyway, so don’t rely on them to easily make accurate comparisons between the connections, as the diagram itself is more suited to give a more overall “big picture” of the data.
If you’re planning to create your own Arc Diagram, then check out my reference page with links to source codes and Wattenberg’s paper how he developed his Arc Diagram.
*Severino Ribecca is a British graphic and information designer interested in data visualization. Currently he’s building an online library of different information visualization methods called The Data Visualisation Catalogue. You can follow the project’s updates on Twitter (@dataviz_catalog) and support further developments on the Patreon Page.