CS-GY 6313: Information Visualization
1.0.0
1.0.0
  • 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
Powered by GitBook
On this page
  • The Desired Properties of a Quantitative Color Scale
  • Uniformity
  • Discriminability
  • Multi-Hue Color Scale

Was this helpful?

  1. Color
  2. Color Use

Quantitative Color Scales

PreviousColor UseNextCategorical Color Scales

Last updated 5 years ago

Was this helpful?

The Desired Properties of a Quantitative Color Scale

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

Uniformity

This means that the color scale must be perceptually uniform i.e. value difference = perceived difference

To create a uniform color scale, we must start with a single hue/color and then uniformly vary the luminance/lightness, keeping the saturation constant.

Discriminability

This refers to being able to represent as many distinct values as possible by having as many distinct colors as possible.

Multi-Hue Color Scale

The previous subsection depicted a single hue color scale. We can also use multi-hue color scales, starting with a dark color and ending with a light color, with several other hues in the middle, in uniformly increasing order of lightness.

Some common reasons to use multi-hue color scales:

  • aesthetics

  • a larger set of colors increases discriminability

  • allows us to communicate two types of information at once: we can depict quantity and also perform segmentation into regions that can be easily labeled (ex. the red region, the blue region, the yellow region etc.)