CS-GY 6313: Information Visualization
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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
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Data Abstraction

PreviousAssessing the Quality of a VisualizationNextTypes of Datasets

Last updated 5 years ago

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This section describes the role of data in information visualization.

Data is factual information (such as measurements or statistics) that is used as a basis for reasoning, discussion, or calculation.

Raw data may not be directly useful while creating visualizations; we might need to transform the data first to be able to use it to create a visualization.

The image below shows a modified pipeline, including the data transformation task. It shows how data is first collected, then transformed and finally encoded to create a visualization.

Data Abstraction refers to describing the data in a way that is meaningful and useful to visualization design. It allows us to decide what operations and encoding methods are available and appropriate.

It is called data 'abstraction' because it provides a way to recognize common structures in data coming from very different domains.

For example:

  • Network visualizations can be used for representing Facebook friendships, interactions between proteins, connections between criminals etc.

  • Spatial distributions can be used to visualize animal movements, election results by region, simulation of airflow in an aircraft etc.

In other words, we can define data abstraction as the process of abstracting away from the domain the characteristics of the data that are useful to decide what visual representations are available and appropriate.