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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  • Spatial and Temporal Semantics
  • Sequential, Diverging and Cyclic
  • Hierarchical Attributes

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  1. Data Abstraction

Attribute Semantics

It is important to understand the meaning of the attributes:

Spatial and Temporal Semantics

This refers to attributes that describe something about space or time, respectively. For example, an attribute 'region' is categorical and spatial, 'latitude' and 'longitude' are quantitative and spatial, 'order date' is quantitative and temporal.

Sequential, Diverging and Cyclic

These are used to describe the order of attribute values.

A sequential attribute has values in a certain sequence. Ex. age, height, weight.

A diverging attribute is one for which we can determine a middle value (or zero-value) such that all the values above it are greater than it (or positive) and all the values below it are less than it (or negative). Ex. temperature, altitude.

A cyclic attribute has values that repeat in a period of time. Ex. hour, week, year.

Hierarchical Attributes

This refers to attributes that may have an internal hierarchical structure.

Ex. product sub-categories, dates, spatial regions, taxonomies.

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Last updated 5 years ago

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