Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling

Abstract

Predictive modeling and land use/land cover change studies in complex systems are well advanced. Cellular automata (CA)-Markov chain (MC) can be defined as one frequently preferred method for this purpose. This paper aims to adapt the CA-MC model to the simulation of residential areas in the city. The proposed method was tested in the city center of Kastamonu, Türkiye, using four time periods: 1985, 2011, 2015, and 2021. Spatio-temporal change maps were produced using ArcGIS 10.0 software. Land use simulation of the urban center, including residence units for 2031 and 2057, was performed using the integrated CA-MC technique. The method’s suitability was demonstrated with the Kappa index of agreement values (Kstandart: 0.93; Klocation: 0.98; Kno: 0.98; and KlocationStrata: 0.95). Within the scope of the study, two different scenarios were designed as short term (S1) and long term (S2). According to the predictions for 2031, there was a residential area increase of 15% in S1 and 29% in S2. When we reach 2057, these growth values were measured as 50% according to S1 and 72% according to S2.