정보 시각화 특강 - Esantomi/digital-humanities GitHub Wiki

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특강 정보

  • 제목 : Information Visualization and Visual Analytics - Introduction
  • 강의일 : 2022-11-17 (14:00-)
  • 강의자 : 서울대학교 컴퓨터공학과 서진욱 교수님

1. Visualization

  • 시각화(Visualization)
    • The use of computer supported, interactive, visual representations of abstract data to amplify cognition
      • Stuart Card, Jock Mackinlay, Ben Schneiderman, 1999
    • 시각화의 두 분야
      • 과학적 시각화(Scientific Visualization)
        • 구상 데이터
        • 실제 대상을 실제 대상과 최대한 유사하게 그래픽적으로 구현하는 연구
          • 예) 토끼, 뇌 등
        • 코딩을 못해도 도구를 활용해 구현 가능
      • 정보 시각화(Information Visualization)
        • 추상 데이터(Abstract data)
        • 어떻게 구현해야 한다는 정답이 없음
          • 예) 삼일운동
    • 외적 표상을 쓰는 이유? (Why use an external representation?)
      • Finding the artificial memory that best supports our natural means of perception (Bertin, 1983)
      • 두 자리 수 곱셈을 암산 대신 수식을 쓰며 하는 이유는?
        image
        • 빠르고 쉽게 해결 가능!
  • 시각화의 특징?
    • Statistical characterization of datasets is a very powerful approach
      • losing information through summarization → hide the true structure
    • Why show the data in details?
      • identical descriptive statistics → very diffferent structures
      • what about features hidden in larger and/or more complex datasets?
    • Same stats, different graphs
      • Generating datasets with varied appearance and identical statistics
      • 똑같은 통계 값을 다르게 시각화할 수 있음
  • InfoVis Reference Model
    image
    • InfoVis is interdisciplinary
      • Graphics : drawing in real time (< 100ms)
      • Cognitive psychology : appropriate representation
      • HCI : using users and tasks to guide design and evaluation
  • Historical Examples
    • William Playfair
      • “charts communicated better than tables of data”
      • 여러 시각화 차트를 고안
    • Advance of Napoleon's Grande Armée into Russia in 1812
      image
      • Charles Joseph Minard, 1861
    • 1854 London Cholera Epidemic
      image
      • John Snow : 물이 문제일 것! 지도 위에 시각화
    • Rose-petal diagram(= Nightingale's diagrams)
      image
      • Florence Nightingale's diagram showing the dramatic reduction in death rates in the hospitals of Scutari following the changes she introduced.

2. Perception

  • Perception for InfoVis
    • Relative perception
    • Relative vs Absolute Judgements
      • Luminance contrast - Simultaneous Brightness Contrast
      • Luminance perception is based on relative judgements
  • Steven's Power Law
    • an empirical relationship in psychophysics between an increased intensity or strength in a physical stimulus and the perceived magnitude increase in the sensation created by the stimulus.
  • Weber's Law
    • “the minimum increase of stimulus which will produce a perceptible increase of sensation is proportional to the pre-existent stimulus.”
  • Preattentive processing
    image
  • Treemap
    • 데이터의 계층 구조를 보여주는 사각형 배열
      • 컴퓨터 하드 드라이브에 저장된 파일의 구조와 크기를 보여 주는 방법으로 고안됨
        image
    • SequoiaView

3. Design Principles

  • Two criteria for evaluating graphical designs
    • Expressiveness
      • vis idiom should express all of, and only, the information in the dataset attributes
    • Effectiveness
      • Most important attributes should be encoded with the most effective channels
        • ranking of channels (channel에 따라 effectiveness가 상이함)
          • Effectiveness of Visual Encoding
            image
          • 즉, 똑같은 데이터 셋도 어떻게 표현하는지에 따라 다르게 전달될 수 있음
            • 1D, 2D, 3D 중 3D가 가장 비직관적?
  • Schneiderman, Tufte
    • Schneiderman's Guidelines : Visual information seeking mantra
      • overview first, zoom and filter, details on demand
      • 큰 그림을 먼저 보여 주고, 확대하고 필터링할 수 있게끔 하고, 요구에 따라 세부 사항을 볼 수 있게 함
    • Tufte's Design Principles
      • Tell the truth
      • Do it effectively with clarity, precision...
    • The Feynman-Tufte Principle
      • “Simple design, intense content”
      • April 2005 Scientific American
  • others
    • Measuring Misrepresentation
      • Visual attribute value should be directly proportional to data attribute value
      • Lie factor = Size of effect shown in graphic / Size of effect in data
    • Maximize data-ink ratio
      • Data-ink ratio?
    • Avoid chartjunk
      • extraneous visual elements that detract from information
    • Use small multiples
      • repeat visually similar graphical elements nearby rather than spreading far apart
      • The same graphical design structure is repeated
      • Learn once and compare → invite comparisons
      • Reveal, all at once, a scope of alternatives, a range of options → overview
    • Use narratives of space and time
      • Tell a story of position and chronology through visual elements
    • Power of negative space
      image
      image