How Gestalt design principles can improve your Data Visualisation

As promised, this is the second post on the topic of Gestalt, in this one I share how Gestalt design principles help your Data Viz work.

You may recall from my previous post that I introduced both a working definition of the German word, Gestalt and a few principles. With no direct translation, our working definition of Gestalt is “whole” or “pattern“. Based on a book focussed on Gestalt coaching, those principles include:

• We are hard-wired to ‘join the dots‘ when we see partial evidence for familiar patterns.
• We are sensitive to being aware of a prominent ‘figure‘ which draws our attention so that other data is pushed into the background ‘ground‘.
• We desire completion, through what is termed the cycle of continuous experience, so balance, completion, closure bring pleasure.

Those aspects of Gestalt thinking that were so relevant for coaching leaders can also help inform our Data Visualisation practice. They guide what have become well established design principles. In this post I will share some great online resources to help us understand them.

A summary of Gestalt Design Principles for Data Viz

Numerous experiments with visual perception have identified the following common principles when applying Gestalt thinking to visual perception. These are like the practical outworking of the above theory on what we perceive (or experience) when seeing certain patterns in data visualisations.

Here are the six most widely recognised Gestalt Design Principles that apply to Data Visualisation. I briefly describe each in simple terms and support each with an image from a helpful post by Alan Trow-Poole.

1) Proximity

Items that are placed closer together are assumed to be related.

So, be careful (for example) that you intend bar charts to be seen as paired.

This can also help your layout to be more intuitive when intended. A dashboard can benefit from related metric/graphs being located nearby on the page.

2) Similarity

Items that look similar, for example by shape or colour will be assumed to be related.

So, be careful in your use of colour hues for categories that they are different enough if categories are not related.

This can help when relationships are intended, e.g. for meta-categories with a range of related hues for sub-categories.

3) Figure & Ground

Items that appear more prominent or important will draw focus and others will be pushed to the background. Focus will be on figure that emerges with less focus on other data points.

So, be careful where unintended use of colour, emboldening or chart junk might draw the eye to focus on less important data.

This can help when making use of a single accent colour to draw focus to more important data in context of paler grey other data points (as advocated in Storytelling with Data).

4) Continuation

Items that could be seen as continuing (lines or to complete a familiar shape) are seen as such despite gaps or insufficient evidence.

So, be careful that assumed continuation of lines (for example) does not imply an inaccurate view of future or missing data.

This can help when only needing to include part of the data to imply a continuation (e.g. a flat line for budget or target that is assumed to continue into future on a chart).

5) Closure

Items that represent part of a familiar shape or pattern will be perceived as complete, whilst insufficient attention is given to gaps.

So, be careful that your viewers are not passing over gaps or variations that matter, in order to complete a familiar pattern. You may need to use highlighting or annotation to try and avoid this.

Such perception can help when you can rely on the viewer to notice trends or patterns in your layout, whilst avoiding being overly cluttered.

6) Common Fate

Items that appear to move or trend in the same direction will be assumed to be related.

So, be careful when creating animated data visualisations that such common movement does not suggest a relationship supported by the data.

It can help when it is accurate for trends in the data to be perceived as related to a common cause or outcome.

Resources to help you apply Gestalt design principles in practice

I hope that brief introduction whetted your appetite to explore further how these principles can help you predict how viewers will read your charts. When applied well, they can help you produce data visualisations that the viewer finds more intuitive.

To help support your CPD on this topic, below I share a few resources that provide much more detail and worked examples of such application.

Applying Gestalt principles to Data Viz design

My first recommended resource is this excellent post from Elijah Meeks, published on his really useful Medium blog. In this post, Elijah introduces and links to a 4 part series guiding us on how to apply this thinking to Data Viz advice. He expands on the 6 principles above and for each shows how it applies to design decisions for your Data Viz. It’s like a training course.

An infographic to act as an aide-memoire

Another helpful post (this time not requiring access to Medium) is this post by Alan Trow-Poole. It includes the infographic from which I copied snippets to illustrate the principles above. It’s a really handy aide-memoire to remind us of those principles. In this infographic, for each of the above, Alan also shares more detail on how this can be applied in your design.

Watch an expert apply these principles

I mentioned in my debrief from the first virtual #datavizlive event how much I enjoyed Emma Cosh’s presentation on user-centred design. In her talk, Emma shared some practical examples of how visual design principles can improve dashboards & charts. This includes her explanation of how Gestalt design principles guide her design decisions. If you haven’t already joined #datavizlive, you can watch her video during a free trial here:

How are you applying Gestalt in your Data Viz design?

I hope that summary and those resources are helpful for you. Just like being aware of those visual elements that are pre-attentive, being aware of these principles can often help you design better.

What has been your experience? Have you applied Gestalt thinking & principles in your visualisations of data? What has that meant for your charts? Do you have any tips (including other recommended resources) to share with other readers? Feel free to comment below.