In summarized the conceptual idea of remapping in this prezi presentation. In this post I will update the current state of the capstone proposal in practical terms.
Basically, I spent my last 2 weeks dealing with data scraping and analysis with processing. Right now I have not only the basic attributes of the individual footprints (scale, area, perimeter, etc.) but also most of the analysis were implemented. The buildings are embedded with shape metrics such as dispersion, “rectangularity” and ortogonality. Besides, I also collected the neighborhood average income (by zipcode) and the elevation of the building. Along the next week I intend to implement similarity, in order to allow the buildings to find similar buildings.
The basic motion and collision functions are also defined, so the footprints can move around an dispute the space. In the video below, there is a simple example of their animation.
The next step is to use the attributes and analysis as the drivers of the movement, enabling a goal-oriented remapping. These maps will indicate spatially one or two criteria such as a similarity clustering or a income x size mapping. I hope that using the footprints as agents will not only enable these remapping but also will create a very interesting battle for space, emphasizing the differences and similarities of the city map.
Finally, I will try to make it more efficient, in order to enable remapping thousand of buildings. Probably, I will try to change collision detection – that right now is a brute force algorithm.