GESTALT PRINCIPLES FOR DATA VISUALIZATION

Introduction:

Data visualisation involves more than just turning raw data into comprehensible graphs. Every person who excels at something has a beginning that served as the cornerstone of their knowledge.

What links these two concepts together? Gaining foundational knowledge is necessary to excel at data visualisation. There are psychological reasons for why some data visualisation strategies perform better than others. Whether you realise it or not, you must always use Gestalt Principles when working with data visualisation.

Gestalt, which in English means "unified whole," is frequently used to refer to the proposition that the whole is greater than the sum of its parts. It describes the patterns you see when a few graphical elements are presented to you. The Gestalt Principles, which include closeness, resemblance, continuity, closure, connection, and enclosure, are a set of principles that explain how the human brain processes visual information. People, especially designers, who are aware of these principles can create images that effectively convey information.

PROXIMITY:

We rationally assume that things belong to the same group the closer they are to one another. The simplest method to link together facts that you want to be visible is to do it this manner. Just enough white space will do to separate groups from the surrounding data.

Dashboard users are more likely to believe that the grouped visuals are in the same context when the visuals are placed close together. The user may unintentionally move their eyes from left to right and/or top to bottom depending on how the objects are arranged in relation to one another.

SIMILARITY:

The same group includes objects that are the same colour, size, form, and orientation, correct? Gestalt Principles also include our propensity to organise objects into categories based on these traits or characteristics. We relate categorical variables to characteristics like triangles for cats, red for loss, and green for profit.

This idea performs particularly well when used to distinguish across several datasets in a graph. The notion of resemblance can be used to create a link between data even when it is located in different places on a dashboard. As an illustration, numerous graphs might use the colour green to indicate revenue. This method might be helpful for promoting comparisons of any data in different places, like order count, order

ENCLOSURE:

A group of objects can be enclosed by anything that forms a visual border around them (for example a line or a common field of color). This enclosure causes the objects to appear to be set apart in a region that is distinct from the rest of what we see.

This principle is exhibited frequently in the use of borders and fill colors or shading in tables and graphs to group information and set it apart. Be aware that it does not take a strong enclosure (e.g. bright, thick lines or dominant colors) to create a strong perception of grouping.

CLOSURE:

Our eyes have a tendency to fill in any gaps in a familiar shape. We naturally see ambiguous objects that appear to be open, incomplete, or have an odd shape as closed or as a whole. The principle of closure states that whenever there is a means that we can reasonably do so, we view open structures as closed, complete, and regular.

This inclination can be used to comprehend complete dashboard systems, particularly when designing graphics. This theory, for instance, explains why, in a bar chart with the x and y-axis values visible, just two axes are necessary to determine the space in which the data appears on a graph, as opposed to full enclosure.

CONNECTION:

Our eyes have a tendency to fill in any gaps in a familiar shape. We naturally see ambiguous objects that appear to be open, incomplete, or have an odd shape as closed or as a whole. The principle of closure states that whenever there is a means that we can reasonably do so, we view open structures as closed, complete, and regular.

This inclination can be used to comprehend complete dashboard systems, particularly when designing graphics. This theory, for instance, explains why, in a bar chart with the x and y-axis values visible, just two axes are necessary to determine the space in which the data appears on a graph, as opposed to full enclosure.

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