Multivariate Data Visualisation
Intuition
- 2 geometric (position) display dimensions
- For data sets with >2 variables, we must project data down to 2D
- Come up with visual mapping that locates each dimension into 2D plane
Two ways to present the data:
- Directly (Textually): Tables
- Limitations: Occupy large space, difficult to understand the relationships. Hard to see the overall picture, focus and see the context
- When to Use: Look up individuals values, compare , precise values
- Symbolically (Pictures): Graphs
- When to use: message contained in the shape of the values. Reveal relationships. Number of data points is huge
Multivariate Data Visualisation
Strategies:
- Avoid "over-encoding"
- Use space and small multiples intelligently
- Reduce the problem space
- Use interactions to generate relevant views
Rarely does a single visualisation answer all questions. Instead, the ability to generate appropriate visualisations quickly is key
Chernoff Faces
- Encodes different variables values in characteristics of human faces
Tables Lens
- Spreadsheet is certainly one hypervariate data presentation
- Make the test more visual and symbolic
Parallel Coordinate
- Encode variables along horizontal row
- Vertical line specifies different values that variable can take
- Data point represented as a polyline
Mosaic Plot
- Categorise multiple things at once
Visualisation Tools
Grammar of Graphics
- Programming by describing what, not how
- Separate specification from execution
- Faster Iteration Better visualisation Reuse Performance Portability Programmatic generation