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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  • Horizon Chart
  • Sparklines

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  1. Visualizing Temporal Data

Increasing Visual Scalability

PreviousVisualization MethodsNextBeyond Using Position

Last updated 5 years ago

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As we increase the number of objects being represented, the visualization may become more and more difficult to comprehend.

Horizon Chart

Using a Horizon Chart is a scalable method to represent a large number of line charts.

Consider the following Horizon Chart:

Steps to obtain a Horizon chart from a Line chart:

Consider the following line chart:

Sparklines

Sparklines are small, intense, word-sized graphics with typographic resolution.

They can be placed anywhere words or graphics or numbers are placed: in sentences, maps, graphics and tables.

They are used when we don't have a lot of space to represent data and can be very effective.

Use a diverging color scale with 2 color hues to represent values above and below a certain value (say 0, or the average):

Then, flip the negative values:

Finally, for each band, collapse and stack them one on top of each other.

This leads to a scalable chart as shown above.