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

Was this helpful?

  1. Graphical Components and Mapping Strategies

Evaluating the Quality of a Visual Encoding

PreviousGraphical DecodingNextContextual Components

Last updated 5 years ago

Was this helpful?

There are 2 principles when it comes to evaluating the quality of a visual encoding:

  • expressiveness

  • effectiveness

Expressiveness: The visual representation should represent all and only the relationships that exist in the data. For example, ordered data must not appear as unordered and vice versa. This principle, if followed, makes sure that all the necessary information is communicated and no misinformation is conveyed i.e. we do not convey any information that isn't present in our data.

Effectiveness: The relevance of the information should match the effectiveness of the channel. The image below shows the effectiveness of different channels to represent ordered (and quantitative) and categorical attributes.

It can be concluded that position (and spatial regions) are the most effective channels. Therefore, we must pay special attention while determining the position of objects in a graph.