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Modelling of Urban Growth and Planning: A Critical Review

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Document pages: 18 pages

Abstract: For thousands of years, cities have been the center of civilization. According to that, detecting, monitoring and controlling urban growth became the most urgent need in urban planning and urban development process to get the expected results that can build a concrete base for decision makers to drive the polices toward best track. The issue of this paper is about urban growth and planning models and techniques such as geographic information system (GIS), cellular automata (CA), genetic algorithm (GA), regression model (R model) and etc. The main objective of this paper is to summarize the 70 scientific papers concern about urban growth to make a review and find out the most important objective, factors, techniques and results for best approach to studying urban growth. The criteria of choosing the papers are that each paper should focus mainly on urban growth modeling and techniques, also, using wide variety of data and factors. This paper aims to fill the gap of absence of the best methods for studying urban growth, as there is a diversity in the methods used, and there is also an absence of exemplary methods or optimal methods for using analytical tools to study urban growth. So, this paper tries to make it easy for researcher to mix the suitable techniques to get acceptable result for their hypothesis. The results assert combining two or more than two techniques and model to assure that the simulation or prediction models can give real and right approaches. However, most researches focused on combining specific techniques with models such as Cellular Automata CA-Markov Chain MC Model-Logistic regression or Cellular Automata CA-Markov Chain MC model or GIS-MCDM or GIS Based AHP etc. Although, in many references some of these techniques were combined together to extract best result. However, the rule that defines the best combination relies on project criteria, the infinite factors, analysis tools, the nature and quality of these models. On the other hand, whether the project needs a simulation or prediction models, all these models can achieve better result when integrated with quantitative models such as analytic hierarchy process (AHP), the Markov chain analysis or multi-criteria decision making (MCDM) techniques. Also, using remote sensing, satellite images and land use and land cover maps as basic data for analysis were the most common factors according to this review.

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