Applying visual design principles increases data comprehension. Audience perception follows a few basic rules.
When you want to improve a graph, Knaflic (2015) suggests putting everything into the background first, making all the colors gray, and then removing all the labels. From there, you can add in different elements or change attributes as needed to make the point of the visual evident. This process helps us avoid causing the audience excessive cognitive load.
Design choices should be made deliberately. This is even more important when data visuals are made by our software using default settings. Be explicit as much as possible.
There are two groups of concepts that are used in visual design. The gestalt principles of visual perception and pre-attentive attributes. These are visual techniques that allow us to work with our audience members’ brains instead of against it.
The gestalt principles of visual perception include:
- proximity, where associated elements are physically close to each other;
- similarity, where associated elements have the same color or shape;
- enclosure, where associated elements are enclosed in a box or area of some kind; and
- connection, using physical lines to connect associated elements.
There are other gestalt principles, but I felt these were the most relevant to visualizing data.
To help grab attention or focus it on specific features, we can use patterns known as pre-attentive attributes, which are basically a marked difference in a specific attribute. Some are better used for quantitative data, such as
- intensity of color, where a darker or bolder element grabs attention first;
- line length, where a longer or shorter land stands out;
- spatial position, where an element’s position shows its relative value;
- motion, where speed or distance shows a higher or lower value; and
- saturation, where bolder and more muted colors can indicate higher or lower value.
Other pre-attentive attributes are better for qualitative or categorical data, including
- shape, where elements have a shape designated for their category (for example, ♂ and ♀ symbols on a scatter plot for men and women, respectively); and
- hue, or color, where different categories use different colors;