What, Why and How

Visual Analytics Framework


John Alexis Guerra Gómez| ja.guerrag[at]uniandes.edu.co| @duto_guerra


http://johnguerra.co/lectures/visualAnalytics_24hours/02_What_Why_How/

Based onslides from Tamara Munzner

Idioms

Distinct approach to creating or manipulating visual representations

Idiom design space

The design space of possible vis idioms is huge, and includes the considerations of both how to create and how to interact with visual representations

Idioms

  • How to draw it: visual encoding idiom
    • Many possibilities for how to create
  • How to manipulate it: interaction idiom
    • Even more possibilities
    • Make single idiom dynamic
    • Link multiple idioms together through interaction

Why focus in tasks and effectiveness?

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

Why focus in tasks and effectiveness?

  • Tasks serve as constraint on design (as does data)
    • Idioms do not serve all tasks equally!
    • Challenge: recast tasks from domain-specific vocabulary to abstract forms
  • Most possibilities ineffective
    • Validation is necessary, but tricky
    • Increases chance of finding good solutions if you understand full space of possibilities

Why focus in tasks and effectiveness?

  • What counts as effective?
    • novel: enable entirely new kinds of analysis
    • faster: speed up existing workflows

Resource limitations

Vis designers must take into account three very different kinds of resource limitations: those of computers, of humans, and of displays.

Computational limits

  • Processing time
  • System memory

Human limits

  • Human attention
  • Memory
  • Retention

Display limits

  • Pixels are precious resource, the most constrained resource
  • Information density: ratio of space used to encode info vs unused whitespace
    • Tradeoff between clutter and wasting space, find sweet spot between dense and sparse

Visual Analytics Framework

Tables

  • Attributes
  • Items (rows)

Networks and Trees

  • Items (nodes)
  • Links
  • Attributes

Fields

  • Continuous
  • Sampled
  • e.g. Brain scan

Geometry

  • Spatial
  • 2D or 3D
  • Inherent shape

Example

SpaceTree

http://www.cs.umd.edu/hcil/spacetree/

SpaceTree

TreeJuxtaposer

https://www.cs.ubc.ca/~tmm/papers/tj/

TreeJuxtaposer

Exercise

Let's find in how many ways we can visualize two numbers 13 and 23

Marks and channels

What marks and channels?

What marks and channels?

What marks and channels?

What marks and channels?

Effectiveness and Expressiveness

Expressiveness

  • Visual encoding should express all of, and only, the information in the dataset
  • Ordered data should be shown in a way we perceive as ordered
  • Match channel and data characteristics

Effectiveness

Encode most important attributes with highest ranked channels

[Automating the Design of Graphical Presentations of Relational Information. Mackinlay. ACM Trans. on Graphics (TOG) 5:2 (1986), 110–141]

Where does the ranking come from?

  • Accuracy
  • Discriminability
  • Separability
  • Popout