Intro to CAA 2012 session "Visualization as a Method in Art History"


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My introduction to CAA 2012 session "Visualization as a Method in Art History"

February 24, 2012

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Intro to CAA 2012 session "Visualization as a Method in Art History"

  1. 1. Information Visualizationas a Research Method inArt HistoryCAA 2012 sessionIntroduction by Lev Manovich 1
  2. 2. visualization timeline:- development of key techniques: by 1830s- scientific visualization: 1988-- information visualization: 1998-- artistic visualization: 2001-- visualization is featured in Design andElastic Mind at MOMA: 2008- Obama campaign site uses sophisticatedvisualization: 2008- visualization become ubiquitous (you haveto include in your social web site): 2011 2
  3. 3. Aaron Koblin, Flight Patterns. 2005. 3
  4. 4. Alex Dragulescki (UCSD). Spam Architecture. 2005. 4
  5. 5. Visualization of books flow in Seattle Public Library. Artist: George Legrady, UCSB 5
  6. 6. IAC Building, NYC: outside view; the lobby with the visualization showingglobal users accessing web properties owned by IAC.Dynamic visualization as a new logo of a company/institution -- representationof dynamic process rather than static identity. 6
  7. 7. New York Times building: visualization of text drawn from NYT archives.Artists: Ben Rubin and Mark Hanson. 7
  8. 8. 8
  9. 9. 9
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  11. 11. 2008: Graphs of statistical data are beginning to be integrated in consumer softwareand webware. Example: stats for activity on individual user account on Flickr. 11
  12. 12. 2009: stats for videos on YouTube. 12
  13. 13. 2009: stats for videos on YouTube. 13
  14. 14. the development of “cultural visualization”:graphing cultural patterns 14
  15. 15. History Flow 15
  16. 16. History Flow 16
  17. 17. Stefanie Posavec.Literary Organism. 17
  18. 18. Lee Byron. Personal listening history using data. 18
  19. 19. Blue pictures are by locals. Red pictures are by tourists. Yellow pictures might be by either.Base map © OpenStreetMap, CC-BY-SA  19Visualization by Eric Fischer
  20. 20. Mapping global culture using “digital traces.” Google Trends, 2007. 20
  21. 21. 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 asmall number of humanities scholars are taking advantage of high-performancecomputing. But just as the sciences have, over time, begun to tap the enormouspotential of HPC, the humanities are beginning to as well. Humanities scholars oftendeal with large sets of unstructured data. This might take the form of historicalnewspapers, 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.” 22
  22. 22. January 2009:NEH and NSF announce DIGGING INTO DATACHALLENGE.Grant amounts: up to 300K USD."The creation of vast quantities of Internet accessible digital data and thedevelopment of techniques for large-scale data analysis and visualization have ledto 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 beingdigitized on a massive scale, it is possible to apply data analysis techniques to largecollections of diverse cultural heritage resources as well as scientific data. Howmight these techniques help scholars use these materials to ask new questions aboutand gain new insights into our world?" 23
  23. 23. Usually visualizations of cultural data do notshow 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( we developed toolsto visualize the actual large collections.Images can be sorted in a variety of waysusing both existing metadata and automaticmeasurements of their visual attributes.
  24. 24. imageplot of 1 million manga pages organized by visual characteristics
  25. 25. Upper part of the visualization (closeup view) contains pages with full range of gray tones, and lots of texture and detailsBottom right part of the visualization(closeup view) contains pages inblack and white with little detail andno texture
  26. 26. Image plot of 393 Mark Rothko’s paintings (1927-1970). Hao Wang and MayraVasquez (undergraduate UCSD students). 2011. X-axis = date (year). Y-axis =average brightness (mean). Writings about Mark Rothko often emphasize thedark paintings created in the last years of his life. However, visualizing 393paintings (1927-1970) shows that this period represents a culmination of a trend
  27. 27. Image plots of selected paintings by six impressionist artists.X-axis = mean saturation. Y-axis = median hue.Megan O’Rourke (undergraduate UCSD student), 2012.
  28. 28. our free software tools for analyzing andvisualizing large image and videocollections, publications and