Data Rep, ah! I redid the sketch with Jer’s help and after class, I understand a bit more about the data— it’s the mention of the word “elections” in the NYT archive since 1986.
What a mystery! Now the “periodicity” of the data makes more sense— as you can see in the way the colors lay out through the grid. The colors correspond to the numbers we were given— so the higher the number, the brighter the color on the HSB range.
These screenshots / pics are different attempts to visualize the big string of numbers we got in class. I look forward to meeting with Jer as I was not able to coordinate distinguishing between the numbers, counting the number of times they occur, etc… hopefully the next post will show a more successful attempt.
The last screenshot with the ellipses on the black background may be the most successful as it uses Jer’s “Your Random Numbers” code that makes sense given the string of random numbers.
Some standouts from the readings this week:
1. Art Against Information: Case Studies in Data Practice by Mitchell Whitelaw
Data is blurred with information.
- Only when organised and contextualised by an observer does this data yield information.
- Data is raw. Information is meaning derived from data.
“Indexes of reality”
Dragulescu’s juxtaposition of source and artefact: the dissonance of spam plants and spam architecture.
- “Anything is anything”
Jevbratt’s data bending: the network data is partly about hidden information.
Borevitz, Salavon use “overdetermined content as source material: the too familiar, the most highly produced, the most redundant and banal.”
Notions of data:
- As matter or stuff
- As concrete and objective
(Rather than contingent and relational)
2. Propositional Density in visualization by Moritz Stepaner
Stepaner gives us several useful definitions:
- Surface proposition: salient, perceptible properties (fedex logo is purple)
- Deep propositions: underlying meaning (fedex is on the go because of the arrow)
- Propositional density: number of deep propositions/number of surface
propositions it conveys
Data Representation: Assignment 1: Readings
Some notes/quotations from the readings:
Systems Esthetics by Jack Burnham
Art by telephone:
“In this instance the recorded conversation between artist and manufacturer was to become part of the displayed work of art.”
Systems
“From man the maker (of tools and images) to man the maker of esthetic decisions.”
Anti-Sublime Ideal in New Media by Lee Manovich
Simon: computation- alternative reality.
Jevbratt: internet- the web, visualized
Data vis: “see patterns and structures behind the vast and seemingly random data sets”
Favorite Data Representations:
1. Eames’ Power of 10 video
3.
Charles Joseph Minard’s statistical graphic of 1869:
Edward Tufte
I went to Edward Tufte’s One Day Course today, to hear his perspective on The Visual Display of Quantitative Information, Beautiful Evidence, Visual Explanations and Envisioning Information.
- Do whatever it takes to explain something: don’tdecide what medium you will use beforehand
- Metaphor is the map: nothing can be erased from a map because it is 100% content
- Reduce optical clutter: no boxes around words! Tufte remarked that the Surgeon’s Warning has such a strong box around it that the outline overwhelms the characters of the warning.
- Good design is self-effacing. 100% content. Get design out of the way.
- Users scan, scroll and then screen change (drilling down into a subject).
