Cellular Automata-Based Suitability Analysis for Dense Urban Areas: The Case of Istanbul

Abstract

Cellular automata (CA) have emerged as one of the most popular approaches used in recent years to evaluate and predict the development and transformation of cities. Cellular automata approaches have made the complex interaction between urban dynamics and urban sustainability effectively understandable. These models provide a deeper understanding of the complex relationship between land-use changes and urban sustainability. This understanding enables governments, planners, and stakeholders to predict and evaluate the potential consequences of future policy alternatives. It is essential to create scenarios in determining urban policies. The fact that cellular automata models create what-if scenarios makes it an approach that can be used frequently for urban transformation. Thus, the study focuses on the urban development paradigm by interpreting the urban transformation concepts in the historical coastal areas of Istanbul with geospatial techniques, a CA-based urban growth model, and land use data. Reliability is vital for using CA models as decision-support tools in this context. Testing the reliability of CA models, one of the study’s aims, is an essential parameter in this respect. For this purpose, the CA model was created by collecting population density, focal points, distance to roads, land uses, and land slope data from different periods (1994 and 2006). The results demonstrated that urban simulation models are effective decision-support tools, promising a more inclusive and explicit planning process.

Keywords

• Decision support systems
• urban growth
• cellular automata
• GIS

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