정보시각화/통계적시각화 분야에서 가장 권위있는 사람을 한 명 뽑으라면 아마 Edward Tufte일텐데요, 번역서도 없고 원서 읽기도 쉽지 않아서 국내에 소개된 내용이 많지 않은 것 같습니다. 아마도 "데이터 잉크의 비율을 극대화하라" 정도의 내용이 간간히 인용되는 것 같습니다.

다음은 E.T의 연작 중 "The Visual Display of Quantitative Information" 및 "Visual Explanation"에서 이와 유사한 종류의 원칙들만 뽑아서 정리한 내용입니다.

Visual Explanation 독후감에서 밝힌 바와 같이 Colin Ware의 책(Visual Thinking for Design, 혹은 더 깊은 내용을 원하면 Information Visualization: Perception for Design)과 함께 보시길 권합니다.


Lie Factor

The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numerical quantities represented.

Lie Factor = size of effect shown in graphic / size of effect in data

--p54, The Visual Display of Quantitative Information

Data variation vs. Design variation

Show data variation, not design variation.

--p61, The Visual Display of Quantitative Information

Another way to confuse data variation with design variation is to use areas to show one-dimensional data. ... The number of information-carrying (variable) dimensions depicted should not exceed the number of dimensions in the data.

--p71, The Visual Display of Quantitative Information


Maximize Data-ink ratio

Data-ink ratio = data-ink / total ink used to print the graphic.

--p93, The Visual Display of Quantitative Information

* Above all else show the data.
* Maximize the data-ink ratio, within reason.
* Erase non-data-ink, within reason.
* Erase redundant data-ink, within reason.
* Revise and edit.

--p105, The Visual Display of Quantitative Information


Multi-functioning Graphical Elements

The principle, then, is: mobilize every graphical elements, perhaps several times over, to show the data. --p140, The Visual Display of Quantitative Information

Examples:

* Stem-and-Leaf Plot (p140, The Visual Display of Quantitative Information)
* Data-based Grids (p145, The Visual Display of Quantitative Information)
* Data-based Labels (p149, The Visual Display of Quantitative Information)

Data Density of a Graphic

Data density of a graphic = number of entries in data matrix / area of data graphic

--p162, The Visual Display of Quantitative Information


Small Multiples

Well-designed small multiples are

* inevitably comparative
* deftly multivariate
* shrunken, high-density graphics
* usually based on a large data matrix
* drawn almost entirely with data-ink
* efficient in interpretation
* often narrative in content, showing shifts in the relationship between variables as the index variable changes (thereby revealing interaction or multiplicative effects).
* Small multiples reflect much of the theory of data graphics:

For non-data-ink, less is more.

For data-ink, less is a bore.

--p175, The Visual Display of Quantitative Information


The principle of data/text integration

Data graphics are paragraphs about data and should be treated as such.

--p181, The Visual Display of Quantitative Information


The smallest effective difference

Make all visual distinctions as subtle as possible, but still clear and effective. ... In designing information, then, the idea is to use Just Notable Differences(not Just Noticeable Differences), visual elements that make a clear difference but no more - contrasts that are definite, effective, and minimal.

--p73, Visual Explanations

Quantitative measures of the informational performance of a screen

The proportion of space on the screen devoted to content, to computer administration, and to nothing at all; character counts and measures of typographic density (making comparisons with printed material as well as computer interfaces); the umber of computer commands immediately available (more are better, if clearly but minimally displayed).

Applied thoughtfully, these measures may hep to restrain the spatial imperialism of operating systems and of interface metaphors - and thereby enhance the richness of content displayed.

--p150, Visual Explanations

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