Lecture Notes
CSE 442
Week 2 Tuesday: Data and Image Models
- Task, data, and domain \(\to\) processing algorithms and mapping \(\to\) image
Data Models / Conceptual Models
- Data models are formal descriptions
- Conceptual models are mental constructions
- Schneiderman 1996: one-dimensional (sets and sequences), temporal, two-dimensional (maps), three-dimensional (shapes), n-dimensional (relational), trees (hierarchies), networks (graphs)
- Nominal, ordinal, quantiative (interval, ratio)
- Dimensions: discrete, usually independent, variables describing data
- Measures: dependent variables, data values that can be aggregated (usually quantiative)
Relational Data Model
- Represent data as a table or relation
- Relational algebra (Codd 1970) – operations on data tables, table in table out
- Roll-up (data along desired dimensions) and drill-down
Tidy Data (Wickham 2014)
- Every variable forms a column
- Every observation forms a row
- Every type of observational unit forms a table
- ‘Normalized forms’in database theory
Common Data Formats
- CSV
- JSON
Image Models
- Jacques Bertin – The Semiology of Graphics, using imagery to encode information, the first theoretical work in visual encoding
- Visual language is a sign system
- “Resemblance, order, and proportional are the three signfields in graphics.”
- Visual encoding variables: position, size, value, texture, color, orientation, shape, tlransparency, blur/focus, length/area/volume
Formalizing Design
- Assuming \(k\) visual encodings and \(n\) data attributes
- We want to pick the best encoding among \((n+1)^k\) possible encodings
- Principle of consistency, principle of importance ordering
- Design criteria (Mackinlay 1986): expressiveness and effectiveness
- “tell the truth and nothing but the truth”
- Effectiveness of encodings by data type
- Mackinlay’s design algorithm, 1986 “A Presentation Tool”; user formally specifies data model and type, tests expressiveness, and generates encodings that pass test
Week 4 Tuesaday – Interactive Design
- Tukey, PRIM-9, data exploration – discovery in multiple dimensions
- Brushing and linking across views
- Interaction techniques can allow us to make input/output relationships to discover new insights
- Direct manipulation: visual representation of objects and action, rapid and reversible actions, selection by pointing / not typing
- Grammar of graphics