[-empyre-] visualization as the new language of theory

[Lev Manovich]

sorry - hit send accidentally before completing my post - here is the
right version:

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There are many ways in which cultural analytics differ from what
people call "formalist" approach, but given the constraints of the
post size, let me just point out a couple here.

1. We are now able to visualize - and therefore better understand -
gradual changes over time at a number of scales – from a few minute
video of a gameplay to a century of film history. Google Earth allows
you to navigate space across scales – from the view of the Earth as a
whole to a Street View that puts you in a position of a car driver or
a passerby looking from a street level. In the same way, we should be
able to navigate through time, moving from the scale of a single
cultural artifact or its parts (such as a film shot) to the scale of
decades and centuries.

Visualization of gradual changes in visual and media culture over
longer historical periods is an idea that appears to us particularly
timely. Humanities disciplines, critics, museums, and other cultural
institutions usually present culture in terms of self-contained
cultural periods. Similarly, the most influential contemporary
theories of history by Kuhn (“scientific paradigms") and Foucault
(“epistemes") also focus on stable periods rather than transitions
between them. In fact, relatively little intellectual energy has been
spent on thinking about how cultural change happens. Perhaps this was
appropriate given that, until recently, cultural changes of all kinds
were usually very slow. However, since the emergence of globalization
in, not only have these changes accelerated worldwide, but the
emphasis on continual change rather than on stability has became the
key to global business and institutional thinking expressed in the
popularity of terms such as “innovation” and “disruptive change.” It
is time, therefore, for us to start treating “change” as a basic unit
of cultural analysis – rather than limiting ourselves to discrete
categories such as to “period,” “school” and “work.”

Here are couple of examples:

http://www.flickr.com/photos/culturevis/4038137889/in/set-72157622525012841/
http://www.flickr.com/photos/culturevis/3951496507/in/set-72157622525012841/
http://www.flickr.com/photos/culturevis/3179623793/in/set-72157612327698034/
http://www.flickr.com/photos/culturevis/3179623621/in/set-72157612327698034/

Thus, if we for instance take the hypothesis that in contemporary
anime characters move mush less than in the earlier works, not only we
can actually test this hypothesis to see if it true by quantifying and
measuring movement but we can also see how this parameter changed over
the years across dozens of works.

2. Another crucial advantage of using data analysis and visualization
is that now for the first time we can adequately describe many aspects
of art and media which previously we could only talk in a very vague
way. For instance, for a 100 years filmmakers, animators, critics and
theorists talked about movement in films and cartoons. But the natural
languages only give a few categories to describe movement - "slow,"
"fast" and a few others. In other words, natural language map
continuous qualities (such as movement) into few discrete categories.
Data analysis and visualization give a much better language for
describe such continuous qualities.

Here are two examples. The first visualizes movement patterns across
Vertov film annotated using linguistic categories:

http://www.flickr.com/photos/culturevis/4114328078/in/set-72157622608431194/

The second visualization uses measurements of movement - and reveals
all kinds of amazing patterns in the film which were hidden when we
use natural language to annotate movement:

http://www.flickr.com/photos/culturevis/4117658480/in/set-72157622608431194/