Pedro M Cruz's master thesis
Getting back to the Lisbon only dataset, I have 1534 taxis to map during October 2009. How will it look like using the same production scheme as the last artifact? Eye candy, eye candy. Not much to extract. Also, I’m noticing that I have to go into more complex data filtering and clustering schemes, as the taxis’ routes seem to climb some walls…
When you start in a field, it’s not a bad idea to start imitating some reference work. Aaron Koblin’s Flight Patterns are a reference in information aesthetics. Is this work going to evolve in something closer to Koblin’s Flight Patterns? I plan to differentiate, but I don’t mind to pass through it as natural work path.
Continuing to explore. The dataset with more than 3 million entries for two months, “only” reports to 496 unique taxis. Each one is represented by a single random color. I see how much the metropolitan areas are colorful comparing to the rest of the territory. I also notice how certain routes are stuck to one color, meaning that taxi drivers operate at a regional level (uuh, what an elaborate conclusion…). Looking further.
Looks like I couldn’t avoid myself in taking a look into traffic data (taxis) for all over Portugal, between November and December 2008.
I started exploring some data relative to taxi traffic in Lisbon. Totaling more than 2 million entries, the data reports to October 2009, and for each information received from a taxi, I draw a point in its location.
There is also some data reporting to November and December 2008, of more than 3 million entries. It seems to result in a more organic look that much pleases me. Nevertheless, the data has 2 years old and is more scattered along time and space, which could be good, but as I’m focusing in Lisbon it traduces in a smaller data density. I’m gonna stick with the first dataset for now. Experimenting. How sweet is that I can identify taxi stands?
The actual theme for my master thesis is coming closer to be defined. For now I can tell that my future work path will be something about visualizing books content — very exciting! Having that, I’ve decided to start experimenting on text analysis. As a starting point, the analysis itself was very raw — I limited myself to analyze word frequency in the text.
The text chosen was a very well know Portuguese epic poem — Os Lusíadas. I chose this poem mainly as a provocative towards what it seems a banalized intellectual status among the inhabitants of the Kingdom of Portugal. The result was a collection of 10 static pieces about each of the 10 most frequent words in the poem. All the work was done in Processing, so it wouldn’t be hard to think in some kind of interaction. Although, the main purpose as I stated was to start analyzing text. I wanted to keep the graphical output as simple and elegant as my knowledge allowed.
Os Lusíadas is a Portuguese epic poem by Luís Vaz de Camões first printed in 1572.
The poem consists of ten cantos and 1102 stanzas.
At the left are the ten most frequent words in the poem by descending order of occurence.
This piece showcase one of those ten words.
Above is an area that directly represents the frequency of that word in each canto.
Each canto has a corresponding list of the ten most frequent words in that canto sorted by descending order of occurrence.
The length of the vertical lines for each canto represents its extension in number of verses.
The counted words were filtered before presentation by two factors — 1st No word with less than 10 occurrences in the whole poem would be taken into account. 2nd As you can imagine the most common words in Portuguese language weren’t considered like adverbs and pronouns. Some verbs and other words without a specific relevancy for the extrapolation of any concept, weren’t taken into account either.
As you should see, number ten was the magic number chosen for this composition. Each canto is composed by eight verses. The result was exported as pdf too – I’ll get this piece printed for sure!
For a matter of curiosity, here is a previous study that originated the final concept. This study displays each of the 50 most frequent words in Os Lusíadas along with the position of each occurrence in the text. Here are the first 21.
Manuel Lima, who’s behing visualcomplexity.com has published an Information Visualization Manifesto.
The article is very interesting from the discussion that led to the urge of creating a manifesto, to the main principles that should direct the information visualization design process.
default to public is a project dealing with the discrepancy between people’s feeling of privacy on the web and the physical world. It consists of an ongoing series of objects and interventions linking the physical world to the online world in unexpected and narrative ways to create awareness for self-exposure.
All works follow a simple, yet powerful principle: Information from the twitter network (standing for information on the web) are displayed in another public environment, the documentation of this process is fed back into the digital public sphere and the authors of the information are notified of that abduction. Two public spheres are temporarily linked, creating repercussions of communication in the digital public sphere, which seems to be regarded as less public than the physical world, although it has a far wider reach than classic media, plus it never expires or is written over.
A very simple, yet almost provocative, piece of conceptual work.
More interesting is the act of bringing the tweets to the paper. The words on tweeter were born on a medium where there is no linearity and no readers — we have users — searching for information, impacient for consumption. The paper traditionally supports a more contemplative and patient reading. Although, the format in which the paper is brought to the physical world seems to embrace the nonlinearity of the web medium, maybe all the user metaphor — as contemplative as reading a tweet in a thin strip of paper can get.