All of the tree(maps) in the writings of Dumas…

Posted on Oct 31, 2013 in Computational Media
All of the tree(maps) in the writings of Dumas…

George Perec and the Spaces of Writing

Although I can’t claim to have done anything particularly interesting with strings this week (damn midterm-induced cold), I did manage to stick some Dumas (and Thoreau for testing) into a treemap library. I was hoping, actually, that I might be able to grab some of the analysis and bring it together with a project I did last fall on the writings of Dumas, via Perec, but I think that would take weeks, if not longer, at this rate. The project itself (here) started from Perec’s observation:

“The space of a sheet of paper (regulation international size, as used in Government departments, on sale at all stationers) measures 623.7 sq. cm. You have to write a little over sixteen pages to take up one square metre. Assuming the average format of a book to be 21 by 29.7 cm, you could, if you were to pull apart all the printed books kept in the Bibliotheque Nationale and spread the pages carefully out one beside the other, cover the whole, either, island of St. Helena or of Lake Trasimeno.

You could also work out the number of hectares of forest that have had to be felled in order the produce the paper needed to print the works of Alexander Dumas (pere), who, it will be remembered, had a tower built each stone of which had the title of one of his books.”

My project (incomplete) actually began to peel through Perec’s errors (paper from rags, not trees) and from there look into the entirely other social spaces and material relays that had also been repressed between the nationalistic project of 19th century french literacy and today’s standardized paper sizes. I’ll put some of those boards below and you can read the entire original paper here, but I think trying to create a mini-version to re-run the numbers has made me more frustrated than anything else.

the treemap of the three musketeers


Speaking in terms of code and structures, I began by looking through Shiffman’s hashmap examples (dracula vs. frankenstein) and then pulled in some of the larger code structure from Fry’s Data Visualization. In principle, this should be sound copy & paste maneuvering, but the more time I spend with Fry’s Processing 1.5 examples, the less satisfied I am with them. Granted, I tend to run materials once to see how they’re working and try to reverse engineer a bit, comment a bit, and sub in my own data (like Dumas). Then I try to code from the ground up and sub in my own, working knowledge of functions ( riding the bike without training-wheels). Which is fine, but Fry’s work, because of its age, often uses functions or structures that don’t pop up in reference and only seem to appear in the very, very old comment posts. He’s also not a prolific comment writer; much of the Data Vis book presume that a reader has already successfully conquered Processing or Getting Started with Processing, etc. Maybe I should just be thankful for happy accidents, but it’s driving me a bit insane that I’m this far into class and feel like I’m learning more by reverse engineering some of my classmates amazing work than by trying to reverse engineer the creator of Processing’s work.  Clearly I need to spend more time and develop more work-arounds. Perhaps, some of my frustration is the cold talking (like being cranky from low-blood sugar), but I just need a way to get my head back into things. And trying to extend or co-opt old projects is just making me more frustration, mostly because I can’t image getting an interactive sketch to work or look as great as my older explorations (not to even begin thinking about the social/material research they let me do). Alright, cranky cold signing off here. Code can be downloaded here.

older boards

specious_1 specious_1 specious_1

An Aside:

I’ve been nothing if not radically inconsistent with ICM work. Something about hosting the Aerial Arts exhibit and seminars last month (October) entirely drained me of extra time (or time management). Add on the AV group project, a bottomless pit of passive-aggressive shadenfreude, and both my sense of time and confidence as a functional adult were obliterated. I like most type A, former production-staff members respond to interpersonal social pressure, or, in this case, disorganized peer pressure. It derail ambitions, intentions, and even long term priorities (like thoroughly learning code structures).  I’ll be sneaking moments to revisit the early weeks of October where I was completely bowled over with other pressures. For the most part, there are still a very large array of structures, functions, and classes I wanted to create and stock-pile for myself in order to game, with massive data inputs, industrial ecology scenarios. Granted teaching responsibilities, an academic lecture, and prepping for my spring seminar is going to get in the way, but I promise to catch up!

Seriously, I came here to code and construct scenarios, but I’ve been way too busy/distracted to feel like I’m getting anything out of the program. And, alas, I knew the skill gap would be frustrating, but I miss making things and making research visible. Part of the problem is me and this fall specifically: I’ve chosen to do the lecturing/marketing/fundraising side of exhibition work and literally haven’t had an excuse to work on graphics and my book projects since August. I’m sure an array of fellow ITPers feel this too: my future as an adjunct/lecturer in landscape urbanism really depends on both integrating these more conceptual, computational modeling skills and producing traditional, academic research (and figuring out how to teach). These aren’t your juggling problems, but are rather my juggling problems. I’m not sure what’s gonna give, but I hope I get a much better handle on api’s and classes before my house of cards implodes.

That said, tonight is the night for re-skimming Industrial Ecology and Sustainable Engineering. Hopefully I’ll have grabbed a few interesting models to start working with for the final ICM assignment. Hurrah, gaming energy, environmental, and social scenarios.