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)