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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  • Transformations Related to Temporal Attributes
  • Transformations Related to Spatial Attributes
  • Binning
  • Rescaling/Re-Expression
  • - Normalization
  • - Percentages

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Data Transformation

As discussed earlier, the first step in creating a visualization is data selection. This refers to choosing from the data the attributes needed for the visualization.

Once we select the attributes, the next step is usually to perform aggregation or other transformations in order to be able to create appropriate visualizations, that communicate the required information effectively.

Common aggregation functions include SUM, MAX, MIN, AVERAGE, MEDIAN and STDDEV.

Transformations Related to Temporal Attributes

These involve transformations at different levels/resolutions: seconds, minutes, hours; days, weeks, months, years etc.

Transformations Related to Spatial Attributes

These involve transformations at different levels/resolutions: zip code, county, city, state, country etc.

Geo coding/decoding refers to going from the name of a place to its geo coordinates and vice versa.

Binning

It is the process of transforming a quantitative attribute to an ordinal attribute. Ex. restaurant inspection scores from numbers to letters.

Rescaling/Re-Expression

There are few ways to accomplish this:

- Normalization

This refers to bringing all the values to a scale of say [-1, 1].

- Percentages

This refers to converting quantitative values to percentages, which makes it easier to make comparisons.

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