CS-GY 6313: Information Visualization
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  • Introduction
  • Defining Information Visualization
  • Why Use Visualization?
  • Popular Visualization Sources and Tools
  • Why Use a Graphical Representation?
  • The Problem with Statistics
  • Why Use a Computer to Visualize Data?
  • Why Use Interaction?
  • Assessing the Quality of a Visualization
  • Data Abstraction
    • Types of Datasets
    • Types of Attributes
    • Attribute Semantics
    • Data Abstraction to Visualization
    • Data Profiling
  • Fundamental Graphs
    • Alternate Representations
    • Visualizing More Than 2 Attributes
    • Faceting
  • Data Transformation
  • Graphical Components and Mapping Strategies
    • Marks
    • Channels
    • Graphical Decoding
    • Evaluating the Quality of a Visual Encoding
    • Contextual Components
  • Color
    • Color Perception
    • Color Specification
    • Color Use
      • Quantitative Color Scales
      • Categorical Color Scales
      • Diverging Color Scales
      • Highlighting
    • Perceptual Issues with Color
  • Geo Visualization
    • When to Use Maps
    • Geo Visualization Techniques
      • Dot Maps
      • Heat Maps
      • Hexbin Maps
      • Choropleth Maps
      • Graduated Symbol Maps
      • Summary of Map Types
    • Issues with Maps
    • Visualizing Geo Data with Time
  • Visualizing Temporal Data
    • Time Structures
    • Visualization Methods
    • Increasing Visual Scalability
    • Beyond Using Position
  • Networks and Trees
    • Visualizing Network Data
      • Node-Link Diagrams
        • Clutter Reduction
      • Matrices
    • Visualizing Trees
      • Node-Link Diagrams
      • Special Kinds of Trees
      • Space-Partitioning and Containment
        • Sunburst and Icicle Plots
  • Interaction and Multiple Views
    • Single and Multiple View Methods
      • Single View Methods
      • Multiple (Linked) Views Methods
    • Common Scenarios
  • Exploring Data
  • Animation, Pacing and Exposition
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  • The Desired Properties of a Categorical Color Scale
  • Uniformity
  • Discriminability

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  1. Color
  2. Color Use

Categorical Color Scales

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Last updated 5 years ago

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The Desired Properties of a Categorical Color Scale

A categorical color scale is designed to represent categorical information. It has 2 main properties: uniformity and discriminability.

Uniformity

In the context of categorical color scales, this refers to uniform saliency i.e. nothing stands out.

The best way is to use a different color for each category, while making sure that no color is brighter or more saturated than the others, to prevent a single color from drawing more attention to itself and causing an imbalance in the importance given to the categories.

To create a uniform categorical color scale, we must keep saturation/chroma and luminance constant, while uniformly sampling colors across different hues.

Discriminability

In the context of categorical color scales, this again refers to having as many distinct (perceptually distinguishable) values as possible, to be able to clearly depict the categories.

It is recommended to use between 5-10 distinct color hues, depending on the categories to be visualized. Using too many colors can become confusing, and it may become harder to distinguish between colors.

The following 12 colors are recommended, since they are easily distinguishable from one another:

Note: Some colors like red and green may have certain semantics linked to them (red: bad, green: good). In certain cases, it can help to make use of these semantics, since it could make the visualization understandable (perhaps even without a legend). However, in certain cases, it can be counter-productive and mislead the viewer. ANother commonly used semantic association is for the color gray. It is usually perceived as no color and is therefore used to depict an item that belongs to an unspecified/unimportant category.