simulation of physical growth of bandar abbas city using cellular automata models and markov chain

simulation of physical growth of bandar abbas city using cellular automata models and markov chain

the aim of this study is to prediction physical growth and land use changes of bandar abbas located in hormozgan province at, 55° 48' 11" to 56° 32' 16" e and 27° 01' 8" to 27° 27' 27" n, using the combination of remote sensing technology, cellular automata (ca) model, logistic regression and markov chain in a 36-year period. global transition probabilities obtained from the markov chain model and unique conditions map were derived from weights of evidence (woe).

local transition probabilities were estimated using infrastructural factors by two different probabilistic empirical methods: the woe approach, based on bayesian theory; and logistic regression. the final land use transition rules drove an urban-ca model, built upon basis of stochastic land use allocation algorithms. these urban-ca models drive a ca model based on eight cell moore neighborhoods.

after achieving simulations for the 2002-2008 and 2008-2014 time periods along the whole time series, forecast simulations were carried out up to 2050 and for various urban planning scenarios. for all simulation periods, the best results were obtained from a combined markov chain and logistic regression with 0.5 gama to derive the transition rules. different simulation outputs for the case study indicate their possible further applicability for generating simulation of growth trends both for iranian cities and cities world-wide.