Monthly Archive for March, 2010

This post presents several experiments (piked between a total of more than 20 generated artifacts) that map 1534 vehicles, during October 2009 in Lisbon, leaving route trails and condensed in one single day. Therefore, the artifacts are animations of the traffic evolution in Lisbon during 24h (from 0:00 to 23:59). In the first artifact, when the traffic is slow, red circles are drawn – traffic clots.

The following artifact is in its essence and aesthetic deviation that brings a visual emphasis on the main traffic routes. Pencil and paper. Darker arteries mean more intense traffic.

In the next artifact the color of the arteries change with the speed of the vehicles. Rapid transit arteries are drawn with greenish and cooler colors, while the sluggish ones are reddish and hotter. Nevertheless, traffic intensity is mapped in the thickness and brightness of the arteries.

The last artifact brings together the ideas of the previous one and the pencil and paper one. The intention was the bring a bigger emphasis for the areas that have major traffic problems (slower routes). The white dots represent the vehicles themselves. Eye candy, eye candy.

These visualization artifacts were developed in the context of CityMotion and my master thesis at DEI/FBA.

Getting back to the Lisbon only dataset, I have 1534 taxis vehicles 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…

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.

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?