Let's recap what you have read in this module.

Gestalt principles describe how human perception helps us understand relatively complex and detailed visual material. There are numerous Gestalt principles, but the nine we present here are perhaps the most relevant to the field of research data visualisation.

9 principles: Similarity, proximity, connection, enclosure, closure, continuity, common fate, symmetry, figure-ground.

Image by Karolinska Institutet University Library. Creative Commons BY SA

By applying these Gestalt principles in a thoughtful way you might help your audience 'navigate' your visualisation more easily. For instance, our sense of perception is highly sensitive to deviations and irregularities in graphic material. An effective way of highlighting and directing the viewer's attention towards an object is therefore to let that object stand out in some way from the surroundings (a surrounding which might comprise of, for example, a set of elements that are arranged symmetrically or in the same direction).

Grouping and commonality can be communicated in many ways. For example, we tend to consider objects that are similar in shape or size as belonging to the same group, and often performing the same function. Furthermore, we tend to consider objects placed near eachother as belonging to the same group. We also tend to consider objects that are connected to one another, for instance by a line, as belonging to the same group. Enclosure is yet another a way of asserting commonality. Lastly, considering that it is possible to communicate information in several dimensions within a visualisation, it is important to understand the relationship between figure and background in an image, and how these aspects interact.

In visualisation work, use of Gestalt principles often occurs automatically and unconsciously. It feels natural to colour nearby nodes in a network diagram with the same colour, or to connect a node with associated nodes with a line in another cluster composed of a different colour, and so on. However, it is in the tension between the principles, or when the principles are thwarted for some reason, that we discover new relationships and connections. In the colour diagram below (figure 1), for instance, if all the fields in all the columns were not drawn as rectangles of the same size, the differences in colour would not be as obvious as they are now.

Ett färgdiagram som beskriver mönster i genuttryck spritt över flera prover

Figure 1. The first third of "Gene set 1" (sample 1, 2 and 3) describes a higher gene expression than the remaining two-thirds of the same set.

Source: Thomas Shafee, Wikimedia Commons.

Creative Commons BY SA

The same applies to the example of the iris species below (figure 2). In this example, It is thanks to the creator's use of colour and emphasis on the similarity principle that the form-separated mixture of species in the sub-clusters becomes clear.

Figure 2. Cluster visualization using Ronald Fishers classic dataset about three flower species, "The Fisher Iris Data".
Source: Alboukadel Kassambara, Datanovia.
Creative Commons BY SA

Recommended reading

If you would like to learn more about the role of the Gestalt principles in visualising research data:

Colin Ware, Information visualization: perception for design, Morgan Kaufmann, 3rd Ed. 2012.
Leland Wilkinson, The grammar of graphics. SpringerLink, 2005.
Elijah Meeks, Gestalt principles for data visualization, 2015.
- Part 1: Similarity, Proximity & Enclosure,
- Part 2: Common Fate, Parallelism and Connectedness,
- Part 3: Proximity and Past Experience with Network Visualization,
- Part 4: Figure/Ground and Metastability
Dejan Todorovic, "Gestalt principles," Scholarpedia, 2008.