Stream Graphs are ideal for displaying high-volume datasets, in order to discover trends and patterns over time across a wide range of categories.
For example, seasonal peaks and troughs in the stream shape can suggest a periodic pattern. A Stream Graph could also be used to visualise the volatility for a large group of assets over a certain period of time.
This type of visualisation is a variation of a Stacked Area Graph, but instead of plotting values against a fixed, straight axis, a Stream Graph has values displaced around a varying central baseline. Stream Graphs display the changes in data over time of different categories through the use of flowing, organic shapes that somewhat resemble a river-like stream. This makes Stream Graphs aesthetically pleasing and more engaging to look at.
In a Stream Graph, the size of each individual stream shape is proportional to the values in each category. The axis that a Stream Graph flows parallel to, is used for the timescale. Colour can be used to either distinguish each category or to visualise each category’s additional quantitative values through varying the colour shade.
The downside to Stream Graphs is that they suffer from legibility issues, as they are often very cluttered with large datasets. The categories with smaller values are often drowned out to make way for categories with much larger values, making it impossible to see all the data. Also, it’s impossible to read the exact values visualised in a Stream Graph, as there is no axis to use as a reference.
Therefore, Stream Graphs should be reserved to audiences who don’t intended to spend much time deciphering the graph and exploring its data. Stream Graphs are better for giving a more general view of the data. They also tend to work significantly better as an interactive piece rather than a static or printed graphics.