Data Visualization Foundation

1 - About

Data visualization is the process of mapping quantitative data to visuals (shapes, color, position, etc) to create a graph made of geometric object.

Information visualization:

  • is defined as “visual representations of abstract data to amplify cognition”.
  • is not useful when the information is specific (for a single instance)
The greatest value of a picture is when it forces us to notice what we never expected to see.
John Tukey, 1977
A picture is worth a thousand words
The Purpose of computing is insight, not numbers.
Richard Hamming (1962)

See (Image|Picture)

Giving shapes to data !

2 - Document structure

A scene graph is generally the document containing all graphic information.

3 - Data Type

3.1 - Characters

3.2 - Numeric

Graphical methods class:

  • diagram techniques,
  • chart techniques,
  • plot techniques,
Type Description
Data Visualization - Map Geographical data
Data Visualisation - Voronoi Map
Data Visualisation - Heat Map For a lot of data
Data Visualisation - Scatterplot For a lot of data
Tree - Treemap Chart hierarchy data
Data Visualization - Stream graph
Data Visualisation - Histogram (Frequency distribution) distribution
Data Visualization - Box Plot summary of distribution
Data Visualization - Bar Chart ranking, comparison
Data Visualization - Line Chart deviation, trend
Data Visualisation - Area Chart ratio
Table Table
Ring Charts hierarchy data
Flow Charts Data or Business Processing

4 - Choosing

4.1 - Software

4.1.1 - Real-time

In realtime chart, you try to repaint only a part of the chart and not to repaint it completely.

Generally updating completely once per second is fine, but updating multiple times per second results in high CPU load.

4.1.2 - Best practices / Fact

  • Use common scales to be able to compare across the graphs
  • Proportions are difficult to interpret
  • Avoid pie charts – Angular and curvature comparisons are hard to interpret.
  • Let it simple. Do not use 3-D charts, shading. Limit border, …

4.2 - Quality

5 - Foundation Vis Papers and Books

6 - Documentation / Reference

Data Science
Data Analysis
Data Science
Linear Algebra Mathematics

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