From Bateman to Munzner
Generally, we are about abstracting the data
The enhanced pictures embrace the meaning of the data
Data Types (a taxonomy)
Useful to abstract data types – similar kinds of data, similar approaches
Useful for understanding how to display the information
The Structure of the Elements
- Fields vs. Tables (what is the index of the elements)
- Tables, Networks (Graphs), Trees (relationships)
- Trees are a special case
- Networks can be encoded in a table
- Text and other freeform information
- Video, images, audio, …
- Streams of signal
- Semantics Vs. Types
Attribute Types (the types of elements)
- Set Size
- Continuous vs. Discrete
- Bounded vs. Unbounded
- Categorical (not-ordered)
- nominal, ordinal, interval
- Ordered and Ordinal (rankable)
- Quantatative (can do artihmentic)
- Do Ratios make sense? (does it have a zero?)
Transformations
Issues Not in Munzner
Sampling
Quantization
Binning and histogramming
Aliasing
Use 1D events (analog to snow)
overdraw, binning (histograms), kernel density estimates, pareto chart, rotation (look at spaces), other designs
in hindsight – you can know what’s right
The Design Challenge
See text notes