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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  • Telling Stories with Data
  • Story Structure Types
  • Annotation, Narration and Story
  • Repetition, Pictures and Story

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Exploring Data

PreviousCommon ScenariosNextAnimation, Pacing and Exposition

Last updated 5 years ago

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Telling Stories with Data

Based on the video:

When we create a visualization, we are not telling a story; we are just making a point. A story needs to have emotion and a meaningful climax. The climax need not even be fully comprehensible, but it is vital to a story.

7 types of stories (according to the book 'The Seven Basic Plots' by Christopher Booker):

  • Overcoming the Monster

  • Rebirth

  • Rags to Riches

  • Voyage and Return

  • Comedy

  • Tragedy

  • The Quest

Story Structure Types

  • The Hero's Journey (Joseph Campbell)

  • Kurt Vonnegut's Story Structure

  • Freytag's Pyramid

    This is usually used to show the structure of a dramatic work, sich as a play or film, but isn't suited for storytelling through data. This is because 'Falling Action' and 'Resolution' do not make sense in the context of data.

Annotation, Narration and Story

These are three terms that must not be used interchangeably.

Annotation is when labels are added to visualizations to help the viewer better comprehend the visualization.

A narration guides the viewer through a visualization or a series of visualizations.

A story builds on top of a narrative and has emotions and a meaningful climax.

Repetition, Pictures and Story

Repetition of key aspects transfers the concepts from short-term memory to long-term memory in the minds of the viewers.

Pictures provide visual impact and aid visual memory: people remeber not just what they heard, but also what they saw.

The story is the most important part, it conveys a message, makes the viewer feel emotions and has a meaningful climax.

Derived from

https://www.youtube.com/watch?v=TWASJxLVdY0
https://www.youtube.com/watch?v=PoQ2_vpnjE4