# Exam

- InfoVis × SciVis × DataVis
	- InfoVis – use of computer-supported, interactive visual representations of data to amplify cognition
	- SciVis – datasets have a given spatialisation
	- DataVis – web-based, communication
	- challenges: diversity, scale
- visual information-seeking mantra
	- overview first
	- zoom and filter
	- details on demand
- visual perception is a two stage process
	- parallel extraction of low-level properties … retina
	- sequential goal-directed processing … brain
- Gestalt psychology
	- one stimulus, two perceptions
	- there is a difference between stimulus and perception
	- emergence – we need to see the whole picture, not just parts
	- reification – perception contains more spatial information than the stimulus
	- multistability – ambiguous stimuli can generate different perceptions but they cannot coexist simultaneously
	- invariance – objects are recognized independently of various variations (transformations, lightning, …)
	- laws of grouping – law level perceptions are grouped into higher-level objects
		- good Gestalt
- information visualization pipeline
	- source data → data tables
		- data transformations
	- data tables → visual abstraction
		- visual mappings
		- transition from data form to visual form
		- we use data attributes to create (visual) marks
	- visual abstraction → views
		- view transformations
		- result … actual pixels
- taxonomies of data types
	- “what comparisons can I make?”
	- “how can I aggregate the data?”
	- nominal … only equality (=)
		- aggregation … mode (we show the most frequent one) or top-k
			- if we have a taxonomy (hierarchy), we can group the data
	- ordered … ordering and equality (<, =)
		- aggregation … median, quantiles, histogram (count per bucket)
	- quantitative … “how much smaller is it?”
		- → intervals … $v-v'$
		- → ratios … $v/v'$
			- only if there is a meaningful zero on the scale
		- aggregation … mean, std dev, skew
- properties of visual channels
	- association $(\equiv)$
		- does the visual channel play well with the other visual channels?
		- size does not provide association – the other visual variables are more difficult to see for smaller sizes
	- selection $(\neq)$
		- can you focus on a specific subset in this visual channel?
		- color provides selection
	- order $(O)$
		- items can be ordered according to this variable, without relying on a lookup to a legend
	- quantity $(Q)$
		- the difference between two items can be quantified
- channels (according to Bertin)
	- position $(\equiv,\neq,O,Q)$
	- size $(\neq,O,Q)$
		- beware: for quantity judgement, our perception is biased
			- Stevens' Law: $\frac{p(x_1)}{p(x_0)}=\left(\frac{x_1}{x_0}\right)^\beta$
			- $p$ … perception
			- $\beta$ is different for length, area, and volume
			- in 2D, we underestimate large sizes (in 3D it's even worse)
		- note on depth
			- depth is perceived mainly because it impacts size
			- using the third dimension introduces ambiguity: is it small or is it far away?
			- but we can keep all the objects the same size and only apply motion parallax & skew instead of perspective
	- value $(\neq,O)$
		- lightness of color
		- light marks are harder to see
	- texture $(\equiv,\neq,O)$
		- usually black and white (dark and light color) – we can mix it with color channel
		- probably underused
	- color $(\equiv,\neq)$
		- we don't perceive the axis of wavelengths
		- colors are usually used as labels
	- orientation $(\equiv,\neq)$
		- only for some shapes (not circles)
	- shape $(\equiv)$
		- does not provide selection → it's better to use color for grouping
- Mackinlay: suitability of variables, possible combinations
	- distinguishes more visual channels
	- ordered lists – the best channels on top
	- systematic approach to choosing visual channels
	- position is the best visual channel
- Card & Mackinlay table
	- Variable – name of the variable
	- D (data type)
		- N (nominal)
		- O (ordinal)
		- Q (quantitative)
		- QX, QY (quantitative and intrinsically spatial)
		- QXlon, QYlat (geographical)
	- X, Y, Z, T (position in space and time)
		- P (point)
		- L (line)
		- A (area)
	- R (retinal encoding)
		- C (color)
		- S (size)
	- — (connection)
	- \[] (enclosure)
	- CP (control processing)
		- text
- interesting visualizations
	- multi-dimensional data
		- scatter plot matrix
		- parallel coordinates
	- time series
		- horizon graph – example of composite visual mapping (one attribute → multiple graphic variables)