2. visualization timeline:
- development of key techniques: by 1830s
- scientiﬁc visualization: 1988-
- information visualization: 1998-
- artistic visualization: 2001-
- visualization is featured in Design and
Elastic Mind at MOMA: 2008
- Obama campaign site uses sophisticated
- visualization become ubiquitous (you have
to include in your social web site): 2011
6. IAC Building, NYC: outside view; the lobby with the visualization showing
global users accessing web properties owned by IAC.
Dynamic visualization as a new logo of a company/institution -- representation
of dynamic process rather than static identity.
7. New York Times building: visualization of text drawn from NYT archives.
Artists: Ben Rubin and Mark Hanson.
22. visualization + big cultural data:
NEH Offﬁce of Digital Humanities (created 3/2008)
Humanities High Performance Computing initiative:
“Humanities High-Performance Computing (HHPC) refers to the use of high-
performance machines for humanities and social science projects. Currently, only a
small number of humanities scholars are taking advantage of high-performance
computing. But just as the sciences have, over time, begun to tap the enormous
potential of HPC, the humanities are beginning to as well. Humanities scholars often
deal with large sets of unstructured data. This might take the form of historical
newspapers, books, election data, archaeological fragments, audio or video contents,
or a host of others. HHPC offers the humanist opportunities to sort through, mine,
and better understand and visualize this data.”
23. January 2009:
NEH and NSF announce DIGGING INTO DATA
Grant amounts: up to 300K USD.
"The creation of vast quantities of Internet accessible digital data and the
development of techniques for large-scale data analysis and visualization have led
to remarkable new discoveries in genetics, astronomy, and other ﬁelds, and—
importantly—connections between academic disciplinary areas."
"With books, newspapers, journals, ﬁlms, artworks, and sound recordings being
digitized on a massive scale, it is possible to apply data analysis techniques to large
collections of diverse cultural heritage resources as well as scientiﬁc data. How
might these techniques help scholars use these materials to ask new questions about
and gain new insights into our world?"
24. Usually visualizations of cultural data do not
show the actual objects (texts, images, etc.)
but only show patterns in metadata (dates,
names, text features, geo locations).
In our Software Studies Initiative at UCSD
(softwarestudies.com) we developed tools
to visualize the actual large collections.
Images can be sorted in a variety of ways
using both existing metadata and automatic
measurements of their visual attributes.
27. Upper part of the visualization (closeup
view) contains pages with full range of gray
tones, and lots of texture and details
Bottom right part of the visualization
(closeup view) contains pages in
black and white with little detail and
28. Image plot of 393 Mark Rothko’s paintings (1927-1970). Hao Wang and Mayra
Vasquez (undergraduate UCSD students). 2011. X-axis = date (year). Y-axis =
average brightness (mean). Writings about Mark Rothko often emphasize the
dark paintings created in the last years of his life. However, visualizing 393
paintings (1927-1970) shows that this period represents a culmination of a trend
29. Image plots of selected paintings by six impressionist artists.
X-axis = mean saturation. Y-axis = median hue.
Megan O’Rourke (undergraduate UCSD student), 2012.
31. our free software tools for analyzing and
visualizing large image and video
collections, publications and projects: