Still under the influence of the formal and numerical zeitgeist from the mid-1930s on, data visualization began to rise
from dormancy in the mid 1960s, spurred largely by three significant developments:
- In the USA, John W. Tukey, in a landmark paper, ``The Future of Data Analysis''
[294], issued a call for the recognition of
data analysis as a legitimate branch of statistics
distinct from mathematical statistics; shortly, he
began the invention of a wide variety of
new, simple, and effective graphic displays, under the rubric of
``Exploratory Data Analysis'' (EDA). Tukey's stature as a statistician
and the scope of his informal, robust, and graphical approach to
data analysis were as influential as his graphical innovations.
Although not published until 1977, chapters from Tukey's EDA book
[297] were widely circulated as they began to appear
in 1970-1972, and began to make graphical data analysis both
interesting and respectable again.
- In France, Jacques Bertin published the monumental Semiologie
Graphique [26]. To some, this appeared to do for
graphics what Mendeleev had done for the organization of the chemical
elements, that is, to organize the visual and perceptual elements
of graphics according to the features and relations in data.
- But the skills of hand-drawn maps and graphics had withered during
the dormant ``modern dark ages'' of graphics (though every figure in
Tukey's EDA [297] was, by intention, hand-drawn). Computer
processing of data had begun, and offered the possibility to construct
old and new graphic forms by computer programs.
True high-resolution graphics were developed, but would take a while to enter
common use.
By the end of this period significant intersections and collaborations would begin: (a) computer science research (software tools, C language,
UNIX, etc.) at Bell Laboratories [16]
and elsewhere would combine forces with
(b) developments in data analysis (EDA, psychometrics, etc.) and (c) display
and input
technology (pen plotters, graphic terminals, digitizer tablets, the mouse, etc.).
These developments
would provide new paradigms, languages and software packages for
expressing and implementing statistical and data graphics. In turn, they would lead to an explosive
growth in new visualization methods and techniques.
Other themes begin to emerge, mostly as initial suggestions:
(a) various visual representations of multivariate data;
(b) animations of a statistical process;
(c) perceptually-based theory (or just informed ideas) related to how graphic
attributes and relations might be rendered to better convey
the data to the eyes.