Evaluation And Comparison Of Simulating Urban Growth Using The Markov Chains And Logistic Regression Models (The Case Of Beni-Suef–Menya–Asuit Cities)

As a result of urban growth, especially in developing countries and its negative effects its results are pressure on the natural environment, loss of biodiversity, loss of open spaces, climate changes and many other negative impacts. This research is interested in the identification of modern technological methods for the simulation of urban growth and the comparison between the two models (Cellular Automata-Markov Chain) and (Cellular Automata-Markov Chain-Logistic Regression) by application to the cities (Beni-Suef-Menya-Asuit).The research results showed that both models have a high ability to simulate and predict urban growth and therefore government must integrate new research methodologies and modern technologies into the process of urban modeling and simulating urban growth and land use changes. Also, what determines which of the two models is better is the goal of the study. If the goal of the study is predict urban growth only the model (Cellular Automata - Markov Chain) is better to use. However, if the goal of the study is to predict urban sprawl and determine the degree of influence of each of the independent factors in the process of urban growth, the model (Cellular Automata-Markov chain-Logistic Regression) is better to use. The results also showed that when studying the factors influencing the urban growth process for each of the study cities, we find that each city has a different degree of influence from one city to another. Therefore, the influencing factors must be studied for each city separately due to the difference in social and economic environments.