Gestalt principles are fundamental to understanding how we perceive visual information. These principles explain how our brains organize and group visual elements, helping us make sense of complex scenes and data visualizations.

In data visualization, Gestalt principles guide how we design and interpret charts and graphs. By applying these principles, we can create more effective visualizations that highlight key insights, show relationships, and make complex data easier to understand.

Gestalt Principles of Perception

Gestalt Psychology and Visual Grouping

• Gestalt psychology is a theory of mind which states that the whole is greater than the sum of its parts
• Describes how humans tend to organize visual elements into groups or unified wholes based on certain principles
• The brain organizes and groups visual stimuli in a scene to make sense of what we see
• Automatically imposes structure and order on visual input

Key Gestalt Principles

• The principle of proximity states that elements that are close together are perceived as a group or pattern
  • Objects that are farther apart are seen as separate or belonging to a different group
• The principle of similarity asserts that elements that share similar characteristics are perceived as part of the same group
  • Similar characteristics include shape, size, color, texture, or orientation
  • Dissimilar elements are differentiated from the group
• The principle of continuity suggests that elements arranged on a line or curve are perceived as more related than elements not on the line or curve
  • There is a tendency to perceive a line as continuing its established direction
• The principle of closure states that elements are grouped together if they seem to complete a visual pattern
  • This occurs even if parts of the pattern are missing or not explicitly connected
  • The mind tends to ignore contradictory information and fills in gaps in the pattern
• The principle of figure-ground explains how the visual system separates an object from its surrounding area
  • The figure is perceived as the main focus and is more prominent, memorable, and meaningful than the background
• The principle of symmetry and order indicates that the mind tends to perceive objects as symmetrical, forming around a center point
  • Objects are seen as simple, complete, and balanced, versus complex, incomplete, or unbalanced

Gestalt Principles in Visual Interpretation

Holistic Visual Processing

• Gestalt principles are based on the idea that the brain is holistic
  • Looks for patterns and relationships between elements to create meaning
• Visual information is interpreted based on the context of the whole image rather than just individual elements
• The principles work together to create a visual hierarchy
  • Guides attention to the most important or salient information first
  • More prominent, ordered, and grouped elements are noticed before background or dissimilar elements

Grouping and Distinguishing Elements

• Proximity, similarity and continuity create associations between objects, making them seem more related
  • Allows quick distinction between different groups, patterns or categories of information in a design (map legend)
• Closure and figure-ground help identify objects and distinguish between an object and its surroundings
  • Occurs even if an image is ambiguous or incomplete
  • Missing information is filled in unconsciously based on learned patterns and expectations (hidden object puzzles)

Creating Visual Harmony or Tension

• Symmetry and order makes a composition feel more harmonious, unified and stable
  • Conveys a sense of balance and equilibrium
• Imbalance or asymmetry can create a sense of dynamism or tension that draws attention
  • Can be uncomfortable if overdone
  • Useful for highlighting important outliers or deviations

Applying Gestalt Principles to Data Visualization

Grouping Related Data Points

• Proximity can be used to show that data points are related or in the same group or category
  • Place related points, bars or slices closer together
  • Unrelated data should be placed farther apart to create separation between different data series (clustered bar chart)
• Similarity of color, shape, or size can tie together data that is meant to be associated
  • Use consistently to indicate related data (color-coded map legend)
  • Vary these attributes to distinguish between data types or ranges (heatmap)

Representing Trends and Patterns

• Continuity can be created by placing data points on a line or curve
  • Implies a trend or progression over time or another continuous variable
  • The smoothness of the line can affect perceived volatility (stock price chart)
• Closure can be applied by using data points to imply a shape or pattern
  • Encourages seeing the data as a unified whole (confidence interval around a trendline)
  • Leads to drawing conclusions from the overall shape

Highlighting Insights

• Figure-ground can be used to make a central insight stand out from the rest of the data
  • Give it a contrasting color or prominent placement
  • The most important data should be the figure, and supporting data the background (data callout box)
• Symmetry and order creates a sense of stability and harmony
  • Makes a visualization appear more authoritative or controlled
• Asymmetry can draw attention to key details
  • Highlights important outliers or deviations in the data (extreme value on a bar chart)

Evaluating Gestalt Principles in Data Visualizations

Identifying Gestalt Principles

• Determine which Gestalt principles are present in the visualization
  • Proximity, similarity, continuity, closure, figure-ground, symmetry and order
• Judge whether they are applied effectively to organize the visual information
  • Do they guide attention to insights?
  • Would applying them differently improve the visualization?

Evaluating Specific Principles

• Proximity: Are data points that are meant to be seen as a group close enough together to be associated?
  • Is there enough separation between different data series?
  • Would changing the spacing improve the grouping?
• Similarity: Are the same colors, shapes, or sizes used consistently to indicate data that is related?
  • Is there enough differentiation between data categories?
  • Would the associations be clearer with different visual treatments?
• Continuity: Are data points aligned in a way that creates a shape that is easy to follow?
  • Does the smoothness of the lines make sense for the data?
  • Would a different curve or positioning better represent the trend?
• Closure: Is the overall shape of the data suggestive of a pattern or conclusion?
  • Are gaps in the data filled in by the overall shape?
  • Does the visualization encourage seeing the data as a unified whole?
• Figure-ground: Does the most important data stand out clearly from the rest of the visualization?
  • Are supporting elements in the background given less visual prominence?
  • Would a different layout or treatment make the central insight more obvious?
• Symmetry and order: Does the visualization feel stable, harmonious and balanced?
  • Is there a sense of equilibrium?
  • Would more asymmetry or dynamism draw attention to key details or outliers in the data?