Cities and Complexity: Understanding Cities with Cellular Automata, Agent-based Models, and FractalsViewing urban dynamics in the context of complexity theory; models and examples in scales from the local to the regional As urban planning moves from a centralised, top-down approach to a decentralised, bottom-up perspective, our conception of urban systems is changing. In Cities and Complexity, Michael Batty offers a comprehensive view of urban dynamics in the context of complexity theory, presenting models that demonstrate how complexity theory can embrace a myriad of processes and elements that combine into organic wholes. He argues that bottom-up processes - in which the outcomes are always uncertain - can combine with new forms of geometry associated with fractal patterns and chaotic dynamics to provide theories that are applicable to highly complex systems such as cities. Batty begins with models based on cellular automata (CA), simulating urban dynamics through the local actions of automata. |
Contents
Complexity and Emergence | 17 |
The Rudiments of Computation | 67 |
Laboratories for Growing Cities | 105 |
Copyright | |
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Other editions - View all
Cities and Complexity: Understanding Cities with Cellular Automata, Agent ... Michael Batty No preview available - 2007 |
Cities and Complexity: Understanding Cities with Cellular Automata, Agent ... Michael Batty No preview available - 2007 |
Common terms and phrases
activity agent-based models agents aggregate applications associated attraction automata available land average Batty behavior cellular cellular automata central chapter cities clusters complex computed constraints defined density destinations Di(t diffusion diffusion-limited aggregation distance distribution dynamics edge cities effect emerge equation example explore fractal dimension function geometry global gradient grid grow growth process growth rate ideas illustrate implies increases initial interac interaction introduced involves kind landscape logistic growth measure ment Moore neighborhood morphology move movement nodes noise origin parade parameter values paths patterns percent period phase transition physical Pi(t pixels population positive feedback potential power law random randomly redevelopment returns to scale routes scale seed self-organized criticality shown in figure simulation spatial structure street symmetric theory threshold tion tracks transition rules types urban development urban growth urban systems von Neumann neighborhood walk waves