A cellular automata-based approach for spatio-temporal modeling of the city center as a complex system: The case of Kastamonu, Türkiye

Abstract

Cities are located at the intersection of global interactions and analytical modeling of space is an essential progression to understand the organizational structure of today's cities, which consist of complex networks and self-organizing processes that affect their nonlinear development. The cellular automata-Markov chain (CA-MC) modeling is a preferred method in predictive modeling and land use change studies of complex systems. It is widely used in modeling land use/land cover change. In this paper, the land use change between 1985-2021 in the Kastamonu city center has been examined within the framework of complexity theory. It is aimed to develop a quantitative model for the comparative measurement of temporal complexity variation. In this context, scenarios were designed with two basic approaches; self-organizing and planned city center development and simulations were made for the years 2031 and 2057. The agreement of the model was tested with Kappa statistical values, which resulted to be >0.9438. • The planned city center scenario is modeled as more complex than self-organizing scenario in terms of functionality. • In the self-organizing city center, the level of morphological complexity is higher. • The Kappa statistic (K Location : 0.9861) indicates the suitability for complexity modeling.