1. Introduction to Information Visualization

InfoVis vs. SciVis

Scientific Visualization

• Visualization that deals with physical data.
• Ex: MRI scans, climate models
• has smaller design space (bound to physical space)

Information Visualization

• Visualization that deals with abstract data
• Ex: Treemap, hierarchical cluster …
• has larger design space

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Power of Visualization

• Visualization can reveal interesting structures that tabular data can’t.
  • Correlations, outliers, skewness…
• Statistical characteristic of dataset is powerful approach….but
  • losing information through summarization. → Hide the true structure.
  • Over-simplified!

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Definition of Visualization

The use of computer-supported, interactive, visual representations of abstract data to amplify cognition

Finding the artificial memory that best supports our natural means of perception.

Provide tools that present data in a way to help people understand and gain insight from it

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InfoVis Reference Model

• RawData → DataTables (Data transformation)
  • 엑셀에서 데이터 attributes 선택
  • Derive
  • Filtering, aggregating
  • formatting
• DataTables → Visual Structures (Visual Mappings)
  • Position, size, color…
  • e.g. map ‘circle radius’ to ‘population’ in a bubble chart
• Visual Structures → Views (View transformations)
  • Zoom & Pan
  • Brushing, sorting…

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History

1. William Playfair (1759~1823)
   • Invented pie, line, bar, area charts
2. Napoleon’s Chart
3. 1854 London Cholera Epidemic
4. Rose-petal diagram

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Perception for InfoVis

Relative Perceptions

• 사람들은 상대적인 값으로 감각을 인지함.
• Do not let context(distortions) affect decision-making.

Two Criteria of Evaluating Graphical designs

Expressiveness

• Vis idiom(=chart) should express all of and only the information in the dataset attributes.
• 더도말고 덜도말고 표현할 것만 표현해라

Effectiveness

• Most important attribute should be encoded with the most effective channels
• 가장 중요한 특성은 가장 효과적인 채널을 쓰도록.

→ Steven’s Power Law

\[\frac{p_1}{p_2} = \left( \frac{a_1}{a_2} \right)^\alpha\]

$\alpha$가 1에 가까우면 정확한 perceived. 1보다 작으면 실제 비율보다 더 작게 perceived됨. (e.g. 면적과 부피는, 길이보다 $\alpha$값이 작음)

Effectiveness of Visual Encoding

1. Position
2. Length
3. Angle, Slope

… (Area, Volume, Color, Density)

Channel effectiveness varies by data types(ordinal, quantitative, nominal)

• Quantitative
  1. Position
  2. Length
  3. Angle
  4. Slope
• Ordinal
  1. Position
  2. Density
  3. Saturation
  4. Hue
• Nominal (Categorical)
  1. Position
  2. Hue
  3. Texture
  4. Connection

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Which Representation is best?

→ It depends on task

(e.g.

• Users need to know exact value → Number
• Users need to know trend → Line chart
• Users need to know hierarchical data ratio → Treemap)

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Weber’s Law

크기가 큰 것을 비교할 때는 차이가 커야한다.

→ 크기 차이를 강조하려면 시각적으로 작아야한다.

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Preattentive Processing

Cognitive operations done preattentively, less than 200ms!

• Popout effects
• Segmentation effects

Preattentive task가 되려면

• Easily detected regardless of number of distractors
• vs. Tiime-consuming visual search

Task 종류 → Target detection, segmentation, region tracking, counting (like sevens)

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Surrounded colors do not pop out!

→ need to use simple hues (only two)

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Laws of Preattentive display

• Must stand out on some simple dimension
• Lessons for highlighting - one of each

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Design Guidelines

• Visual Information seeking Mantra
  • Overview first, zoom and filter, details on demand

Tufte’s Design Principles

• Tell the truth (Graphical integrity)
• Do it effectively with clarity
• Simple design, intense content

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Lie factor

• Visual attribute value는 data attribute value에 비례해야한다.
• Lie factor = (Size of graphic) / (size of data)

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Data-ink ratio

• We need to maximize this
• Data-ink ratio = (Data ink) / (Total ink used in graphic)

→ Avoid chartjunk (extraneous visual elements)

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Use small multiples

• Repeat visually similar graphical elements

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Utilize narratives of space and time

• Story-telling!
• Tell a story of position and chronology through visual elements

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Negative spaces

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