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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Defining Information Visualization

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Last updated 5 years ago

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Information Visualization refers to the use of computer-supported, interactive, visual representations of abstract data to amplify cognition.

Interaction is an important aspect of any efficient visualization. A user must be able to change what is visualized as well as how it is visualized. The end goal is a better understanding of the phenomenon.

The term abstract data refers to data that has no obvious/natural visual representation. Information Visualization is particularly important for such data.

To amplify cognition could mean any of the following:

  • to improve understanding

  • to solve problems in a shorter time with less effort

  • to solve problems more accurately

  • to do things that would be impossible to do without a computer and a graphical representation

A cognitive artifact is a tool that helps one think better. But how does it help? For example, if we try to multiply two numbers in our mind, it is tougher to do so when compared to using pen and paper. This is because, when we use pen and paper, we store the intermediate results in the real world, and do not have to retain them in our memory. This is why it is easier to multiply using pen and paper.

This is related to the concept of distributed cognition: it refers to the fact that our cognitive system doesn't only comprise of our mind and sensors, but also includes the artifacts and the environment that surrounds us, that we use to store and manipulate information.