Designing Data Visualizations

Chapter 1. Classifications of Visualizations

There are several ways to categorize and think about different kinds of visualizations. Here are four of the most useful. The first two are unrelated to the others; the last two are related to each other.

Complexity

One way to classify a data visualization is by counting how many different data dimensions it represents. By this we mean the number of discrete types of information that are visually encoded in a diagram. For example, a simple line graph may show the price of a company’s stock on different days: that’s two data dimensions. If multiple companies are shown (and therefore compared), there are now three dimensions; if trading volume per day is added to the graph, there are four (Figure 1-1).

Figure 1-1. Four data dimensions are shown in this graph. Adding more points within any of these dimensions won’t change the graph’s complexity.

This count of the number of data dimensions can be described as the level of complexity of the visualization. As visualizations become more complex, they are more challenging to design well, and can be more difficult to learn from. For that reason, visualizations with no more than three or four dimensions of data are the most common—though visualizations with six, seven, or more dimensions can be found.

Note

Adding more volume or data points of the same data dimension doesn’t increase complexity. Showing ...