21 Jul Urban growth simulation
The self-organizing city
n a post-singularity scenario, cities will react to inhabitants’ stimulous real-time, allowing for a total interaction. In order for this to be possible, the project takes a serious study of emergence as point of departure. Technology is used to process data and create behavioural relationships which shape the city at all levels: as place of relations, knowledge, information flux, codes, regulations, entropy, scarcity -or its lack- and its own environment.
Numerous simulations have been created in order to understand the complexity of the system and feed back the process in an attempt to finally understand processes through which to apprehend space, territory and communication through knowledge and technology.
The model of the project intends to re-create phenomena present in emergent processes of the type III (strong, according to Jochem Fromm), in an attempt to identify emergent behaviour which could give a hint on urban phenomena.Several feedback loops are created at different levels of interaction: agent-agent, agent-environment, agent-system, system-environment.
The geometry of the new city can be understood as a mapping of the different variables involved in the process: density, program, clustering, massing and relationship to street agents. In that sense, the city becomes a disjoint network, meaning the result of several forces which do not sum up as a whole, but rather as a complex system. This complexification implies that the surfaces absorb the information of that system, allowing for differentiation in time and space, responding to changing local conditions throughout time. Nevertheless, this model should be understood as a design driver for the multiple agents involved lastly in the design and construction process.
The simulation of the city growth necessarily implies a hierarchy of growth: streets are first created (either randomly or under the userÂ´s criteria), which will cause program to appear. Both local and general program densities are constantly measured, influencing the behaviour of the system and forcing itself to readjust to the new conditions every frame.The program isable to spezialize and spatialize through subdivision once a certain local density has been reached, creating differentiated areas of knowledge, speed and users. Also, diverse clustering conitions can be reached by the system, allowing for a larger variety of simulations.
Some captures of the actual simulation responding to different boundary conditions.
Urban growth is understood as a never-ending process (in a non-linear system far from equilibrium). The city evolves into a more intelligent entity, capable of reacting to stimulous real time.
Experimental geometry and re-reading the system: the final geometry, though seeminlgy arbitrary, responds to very specifically-defined conditions, such as environmental influences, existing infrastructures or purely palnning tools implemented in the simulation. This allows for a re-understanding of the system behaviour through a simultaneous analysis of process and result.