# 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