Lecture

• best channel to convey information = vision
  • highest bandwidth of all the senses
  • extends memory and cognition – we use drawings to “think more clearly”
  • people think visually
• pre-attentive perception
  • the time to find the blue dot is constant – no matter the total number of dots
    • assuming we can distinguish the colors
• Anscombe's Quartet – four datasets with the same statistical properties
• when computing average, we assume that it represents the underlying distribution well
• information visualization = use of computer-supported, interactive visual representations of data to amplify cognition
• scientific visualization – the datasets have a given spatialisation (the coordinates already exist), continuous
• data visualization – 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
  • sequential goal-directed processing
• retina performs parallel processing of different attributes
• brain performs sequential processing (object segmentation, identification)
• readings: Graphs in Statistical Analysis & The Eyes Have It; viewing: Hans Rosling’s TED talks
• density of receptors decreases with the distance from the center of vision (so the peripheral vision is not that sharp)
• peripheral vision is mostly colorblind
• http://xkcd.com/1080/
• 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
• example: gapminder (data attributes → visual channels)
  • income → x
  • life expectancy → y
  • country → details on demand
  • population → size
  • region → color
• interactions
  • the user can manipulate 1) data transformations, 2) visual mappings, 3) view transformations
  • zoom … view transformation
  • filtering … data transformation
  • in gapminder, we can also edit visual mappings
• 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