Journal
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
Volume 35, Issue 4, Pages 289-296Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compenvurbsys.2010.11.001
Keywords
Urban cellular automata; Stochastic cellular automata; Urban simulation; Monte Carlo method
Ask authors/readers for more resources
Urban cellular automata models have proved useful tools in urban growth prediction because of their simplicity and their ability to reproduce complex emergent dynamics. Complex emergent dynamic systems involve processes that are difficult to predict, in which randomness plays a key role. In view of the fact that randomness is particularly relevant to complex processes, the aim of this paper is to analyze the sensitivity of the results of urban cellular automata models to the different methods used to incorporate the stochastic component in the models. The urban growth patterns obtained using different stochastic components are analyzed and compared using a number of spatial metrics. The results show that the differences observed in the simulated patterns are sufficiently relevant to justify the need for this type of analysis, which allows for the selection of the stochastic component that best suits the dynamics of the area. (C) 2010 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available