Using the Sleuth Urban Growth Model Coupled With a Gis to Simulate and Predict the Future Urban Expansion of Casablanca Region, Morocco

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.

Simulating Urban Growth Using Cellular Automata Approach (SLEUTH)-A Case Study of Praia City, Cabo Verde

“…A proper urban growth monitoring comes in handy to urban planners by helping them control development in a sustainable way. With advances in technology such as remote sensing and Geographic Information Systems (GIS) [4], new horizons for analyzing spatiotemporal alterations of Land Use Land Cover (LULC) on a regional and global scale [5], urban/regional planning [6], [7] and the analysis of urban evolution through the implementation of specific models [8] can be carried out. In light of that, several urban growth models such as machine learning and statistical methods, cellular automata-based methods,…”

“…The model was first developed and applied to forecast urban sprawl in San Francisco and the Washington/ Baltimore regions [30] but has since been calibrated and applied to model the urban growth of other regions in the United States and the world. Some examples are, California counties [34], [35], Honolulu, Hawaii [38], Gdansk, Poland [39], Chiang Mai, Thailand and Taipei, Taiwan [40], Tijuana, Mexico [41], Alexandria, Egypt [42], Yaounde, Cameroon [43] Sydney, Australia [44] Adana, Turkey [4], Sana'a metropolitan city, Yemen [45], Tainan, Taiwan [46], Casablanca region, Morocco [8], Lisbon and Porto, Portugal [48] and the most recent Jinan City, China [47]. These studies have shown and reaffirmed the importance of combining SLEUTH, GIS and remote sensing in the study of urban growth at different times and places.…”

Modelling Urban Growth for Bangkok and Assessing Linkages With Road Density and Socio-Economic Indicators

“…A review paper on UGPM by Triantakonstantis and Mountrakis (2012) reported that out of 156 manuscripts by different researchers, almost 83% used the CA model for urban growth predication. The SLEUTH (slope, landuse, exclusion, urban extent, transportation and hillshade) is one commonly used CA-based UGPM with many reported studies (Al-shalabi et al, 2013;Bihamta et al, 2015;Clarke and Gaydos, 1998;Herold et al, 2003;Mallouk et al, 2019;Saxena and Jat, 2019). In the SLEUTH model, urban growth rules are applied on a cell-by-cell within a uniform geographical lattice thus making it highly ideal for spatial growth predictions.…”

Modelling and assessing linkages between urban growth and road network for Southeast Asian cities

“…The Landsat data derived urban expansion revealed that all nine studied SEA cities experienced significant spatial urban expansion over the period 1987 to 2017 (Figure 5- 4). Strong radial urban expansion from the city centers is observed for Bandung, Bangkok, Ho Chi Minh, Jakarta, Manila and Medan, whereas the expansion is in the form of scattered patches for Kuala Lumpur and Singapore.…”

“…A UGPM review paper by Triantakonstantis and Mountrakis (2012) reported that 83% of 156 manuscripts on urban growth prediction used the CA approach. The SLEUTH (Slope, Landuse, Exclusion, Urban extent, Transportation and Hillshade) is one commonly used CA-based UGPM with many reported studies (Al-shalabi et al 2013;Bihamta et al 2015;Clarke and Gaydos 1998;Herold et al 2003;Mallouk et al 2019;Saxena and Jat 2019).…”