USING THE SLEUTH URBAN GROWTH MODEL COUPLED WITH A GIS TO SIMULATE AND PREDICT THE FUTURE URBAN EXPANSION OF CASABLANCA REGION, MOROCCO
Keywords: Urban growth, Prediction, SLEUTH, Casablanca, Supervised classification, Cellular automata
Abstract. The rapid and sometimes uncontrolled acceleration of urban growth, particularly in developing countries, places increasing pressure on environment and urban population well-being, making it a primary concern for managers. In Casablanca city, Morocco’s economic capital, the rapid urbanization was a result of population explosion, rural exodus and the emergence of new urban centers. Therefore, a system for urban growth simulation and prediction to anticipate infrastructural needs became indispensable to optimize urban planning. The main aim of this work is to study the urban extension of the Grand Casablanca region from 1984 to 2022 and to predict urban growth in 2040 using the SLEUTH cellular automaton model. The methodology consists of calibrating the model using data extracted from a time series of satellite images with a resolution of 30 m acquired between 1984 and 2018, as well as vector data relating to the urban projects planned on the horizon of 2022. The supervised classification and digitization of these images, together with a DEM of the study area, provided the input data required by the model, including Slope, Land use, Exclusion, Transportation and Hillshade. This data was introduced into the model using ArcSLEUTH, a custom extension of ArcGIS to compile the SLEUTH model. The result is synthetic maps of urban growth in the study area up to 2040, as well as the expected percentage indicators of change. The result is an effective decision-support tool for decision-makers and planners to develop more informed development strategies for the region and its people.