DIGH 5000: Data visualization & analysis Presentation


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DIGH5000 class presentation on Information Visualization at Carleton University on February 24, 2014

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DIGH 5000: Data visualization & analysis Presentation

  1. 1. Data Visualization & Analysis Danuta Sierhuis MA in Art History candidate Specialization in the Digital Humanities Carleton University
  2. 2. Lev Manovich “What is Visualization?” ▪ “Information visualization is becoming more than a set of tools, technologies and techniques for large data sets. It is emerging as a medium in its own right, with a wide range of expressive potential.” Eric Rodenbeck (Stamen Design), keynote lecture at Emerging Technology 2008 [March 4, 2008.]
  3. 3. Lev Manovich “What is Visualization?” ▪ What is Information Visualization (Infovis)? – Hard to make an encompassing definition – Provisional Definition: ▪ A mapping between discrete data and a visual representation ▪ Does not cover the distinctions between static, dynamic (i.e. animated) and interactive visualizations ▪ “Information visualization utilizes computer graphics and interaction to assist humans in solving problems.”
  4. 4. Google Search Infovis vs. Scientific Visualization The majority of images returned by searching for “information visualization” are two dimensional and use vector graphics - points, lines, curves, and other simple geometric shapes. The majority of images returned when searching for “scientific visualization” are three-dimensional; they use solid 3D shapes or volumes made from 3D points.
  5. 5. Key Elements of Infovis ▪ Reduction – Infovis uses graphical primitives, like points, lines, curves, geometrical shapes, to stand in for objects or relations between them – It reveals patterns and structures in the data = they focus our attention on the complex structures and concepts that arise out the interaction of simple elements ▪ Spatial variables – Position, size, shape, curvature of lines and movement = represent key differences in the data and reveal important patterns and relations – “This principle can be rephrased as follows: infovis privileges spatial dimensions over other visual dimensions. In other words, we map the properties of our data that we are most interested in into topology and geometry. Other less important properties of the objects are represented through visual dimensions – tones, shading patterns, colors, or transparency of the graphical elements.” (Manovich,
  6. 6. Spatial Privileging = Not New in Visual Culture ▪ Raphael Santi, La Disputa, fresco, St anza della Signatura, Vatica n, Rome.
  7. 7. Why are spatial variables privileged in infovis?
  8. 8. Infovis: Direct Visualization ▪ These are information visualizations that preserve the original form of the data in the final representation ▪ They do not reduce the data to points or lines in a graph ▪ He also calls this media visualization
  9. 9. Infovis: Direct Visualization Text as Text Books Everyone Should Read http://www.informationisb eautiful.net/visualizations/ books-everyone-shouldread/
  10. 10. Infovis: Direct Visualization Images as Images Jason Salavon , Every Playboy Centerfold, The Decades (normalized), Digital Cprints, Ed. 5 + 2 APs. 60″ x 29.5″, 2002. (From left to right: 1960s, 1970s, 1980s, 199 0s)
  11. 11. What are some reasons why we would want to preserve the original form of the data in the infovis representation? Compare and contrast the pros-cons of infovis with reduction and without reduction to simple graphical elements.
  12. 12. Do infovis need to be beautiful to provide effective results? ▪ Horoscoped ▪ http://www.in formationisb eautiful.net/v isualizations/ horoscoped/
  13. 13. PROJECT: SelfieCity http://selfiecity.net/#
  14. 14. YOUTUBE CLIP: Aaron Koblin & Visualizing Global Texting trends in NYC ▪ http://www.youtube.com/watch?v=-SETcTrdcU4
  15. 15. In your disciplines why would you want to use infovis? (Perhaps we could talk a bit about the assignments from this week at this point)
  16. 16. My experiment with NodeXL Twitter Interactions/Reach & @lostottawa NodeXL graph showing 50 followers of the Lost Ottawa Twitter page and 1.5 levels of interaction between them NodeXL graph showing 50 followers of the Lost Ottawa Twitter page and 2 levels of interaction between them. This graph shows the immediate followers and a portion of the potential reach that Lost Ottawa could achieve if someone retweeted one their posts.
  17. 17. My experiment with Voyant ▪ “Aesthetics of Information Visualization” by Warren Sack http://danm.ucsc.edu/~wsack/Writings/wsack-infoaesthetics.pdf ▪ http://voyeurtools.org/?corpus=1393263201539.677 ▪ http://docs.voyant-tools.org/tools/
  18. 18. Tom Corby: Infovis as Art Practice ▪ There is an experiential side of information visualization ▪ Information visualization can be considered an art practice ▪ Artistic Visualization: – Visualizations of data done by artists with the intent of making art – Based on real data – Not concerned with making the data beautiful in the end result
  19. 19. Artistic Visualization ▪ There is the “work-thing” and then the “aesthetic object” ▪ The work thing = the thing we can touch, preserve, etc. ▪ The aesthetic object = laid down in the collective consciousness
  20. 20. Aesthetics ▪ The field of study that examines issues of sensation and perception and seeks to understand why something is emotionally/sensually moving ▪ Usually aesthetics is tied to issues surrounding embodiment or disembodiment – Again, in relation to infovis, this begs the question of why we map information. Why do we need to give information a body in visual form? ▪ Aesthetics of information visualization = to investigate the judgement used to decided what about the work is valuable, according to the senses or, in general, the body.
  21. 21. Issues of Aesthetic Interpretation ▪ There is always aesthetic noise that colors our interpretation of a visual image ▪ Biases: education, personal experience, knowledge of the subject at hand, and other social and historical contexts ▪ Issue of multiple meanings – T.S. Eliot said, “No poet, no artist of any art, has his complete meaning alone.” – QUESTION: How do we reconcile this?
  22. 22. Is it possible to accept the results of infovis as authoritative and truthful? What are the issues surrounding this?
  23. 23. Corby: Infovis as sensual and critical art medium Abigail Reynolds, Mount Fear East London (2003)
  24. 24. Art Historical Precedents Synthetic Cubism: Braque, Bottle and Fishes, Oil paint on canvas, 1910-2 Formal experiments of Russian Constructivism Aleksandr Rodchenko, Composition, gouache and pencil on paper, 1918.
  25. 25. Conclusion ▪ Lev Manovich, “The anti-sublime ideal in data art,” 2002 – If Romantic artists thought of certain phenomena and effects as unrepresentable, as something which goes beyond the limits of human senses and reason, data visualization artists aim at precisely the opposite: to map such phenomena into a representation whose scale is comparable to the scales of human perception and cognition.
  26. 26. END