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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  • Cannot Fit All Information on a Single Screen
  • Need to View Actual Values or Labels Contained in the Data
  • Need to Visualize Different Facets of the Data Simultaneously

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  1. Interaction and Multiple Views

Common Scenarios

PreviousMultiple (Linked) Views MethodsNextExploring Data

Last updated 5 years ago

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Cannot Fit All Information on a Single Screen

This is a common problem. One solution is to allow panning and scrolling.

The problem with this is that it is difficult to get an overview of the entire data, thereby the viewer may miss out on trends or patterns in the data. It also becomes difficult to make comparisons in the data.

The best solution is to use the overview+detail pattern.

One view shows an overview, and hovering over it shows the details.

Need to View Actual Values or Labels Contained in the Data

Adding labels in the visualization causes clutter.

One way to solve this issue is to use two separate views: one with the graphical representation and the other with a tabular representation (that contains the values/labels). This is the visualization+list/table pattern.

Another solution is to show values/labels upon hovering the data points.

Need to Visualize Different Facets of the Data Simultaneously

This issue often arises when we need to visualize who, what, when or where something is.

The solution is to use multiple views with different facets (attributes), and link them together using interaction.

An example: