Closure

As explained in Everyday Data Visualization

A visual grouping principle in which perception completes incomplete forms by mentally filling gaps to create a whole.

Closure is the visual tendency to complete incomplete elements. When a shape has gaps, perception often fills them in so the parts read as one whole. section 2.2.5

Recognizing the missing pieces

Logo designs can suggest readable letters or recognizable figures without full outlines: IBM, WWF, Adobe, and USA Network rely on the eye to supply what is absent. In data visualization, a donut chart with a missing segment still reads as part of a complete circle. section 2.2.5

Use closure when:

• You want viewers to perceive separated marks as one recognizable form. section 2.2.5
• A deliberately incomplete graphic communicates completion, progress, or a larger implied whole. section 2.2.5
• You are coordinating closure with connection and continuity, which also help viewers infer relationships across gaps and paths. section 2.3

Figure 2.14 Principle of closure

View the visual demonstration of closure in a data-visualization context. section 2.2.5

How Everyday Data Visualization covers it

How deep it goes

Explains unconscious completion through examples, but stops short of explaining the underlying neuroscience of visual perception.

Examples

Uses IBM, WWF, Adobe, USA Network, and incomplete donut charts as examples of perceptual completion.

What you can do after

Apply closure when designing logos and data visualizations that suggest complete forms through deliberately missing elements.

How it connects

Connects closure with Gestalt principles, especially connection, continuity, enclosure, proximity, similarity, and symmetry.

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Related concepts

• continuitycontrasts with · A perceptual organization principle in which elements following a smooth or continuous path are seen as related.
• connectionused by · A visual grouping principle in which elements with connected forms are perceived as correlated and belonging to the same group.

This explainer was generated by Manning's knowledge graph from the text of Everyday Data Visualization. The section links point to the parts of the book it draws on.