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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  • Bar Chart
  • Line Chart
  • Scatter Plot
  • Matrix
  • Symbol Map

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Fundamental Graphs

PreviousData ProfilingNextAlternate Representations

Last updated 5 years ago

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When we are given a dataset, we must determine:

  1. What to visualize i.e. select and transform

  2. How to visualize it i.e. choose/design a representation

This section describes various fundamental graphs that can be used to visualize data, including:

  • Bar Charts

  • Scatter Plots

  • Matrix

  • Line Charts

  • Symbol Maps

These fundamental graphs are widely adopted, effective and useful. They solve a very large percentage of visualization problems, and serve as a good training ground for more sophisticated graphs.

Bar Chart

A bar chart is used to represent how a quantity distributes across a set of categories.

Every bar represents a category and the height of each bar denotes the quantity.

Therefore, it is suited for 1 Categorical/Ordinal attribute (on the X axis) and 1 Quantitative attribute (on the Y axis).

Line Chart

A line chart is used to visualize how a quantity changes in relation to another quantity (typically time).

Therefore, it is suited for Temporal and Quantitative attributes on the X and Y axes respectively.

Scatter Plot

A scatter plot is used to visualize how a quantity relates to another quantity.

Therefore, it is suited for Quantitative and Quantitative attributes on the X and Y axes respectively.

Matrix

A matrix can be used to visualize how a quantity distributes across two categories. Note that the sizes of the squares are proportional to the quantities.

Therefore, it is suited for 2 Categorical (or Ordinal) attributes and 1 Quantitative attribute (in the cells).

Symbol Map

A symbol map is used to visualize how a quantity distributes across two spatial coordinates. Note that the sizes of the symbols (colored areas) are proportional to the quantities.

Therefore, it is suited for Spatial and Quantitative attributes.