BIG DATA VISUALIZATION METHODS -Part III
- Streamgraph
Streamgraph displaced around a central axis, resulting in flowing and organic shape, it is a type of a stacked area graph. Streamgraph shows the trends for different sets of events, the number of its occurrences and its relative rates, etc.
So, there can be a set of similar events, shown through the timeline on the image .
The method has two goals which is to show much individual time series, while also conveying their sum. It is possible to satisfy both goals at once by the heights of the individual layers add up to the height of the overall graph, it is possible to satisfy both goals at once. At the same time, this involves certain trade-offs.
There can be no spaces between the layers since this would distort their sum. Because of having no spaces between layers, changes in a middle layer will necessarily cause wiggles in all the other surrounding layers, wiggles which have nothing to do with the underlying data of those affected time series.
Streamgraph works only with one data dimension and it doesn’t support data variety criterion. But it can be applied to large datasets. After entering new data into the analytical system, it can be dynamically continued by new values. So, it meets the data dynamics criterion. There’s only one limitation in this method number of factors and this method can be used only for representation of quantity factors. Musical trends and cinema genre trends are examples of this method.
Method advantages:
(i) Effective for trends visualization.
Method disadvantages:
(i) Data representation shows only one data factor
(ii) The method depends on data layers (objects) sorting.
