Spatiotemporal Dynamics and Driving Factors of Urban Expansion Using GLC_FCS30D and CA‐Markov Modeling

Rapid urbanization in semi‐arid regions with complex geomorphological settings poses significant environmental and territorial challenges, particularly in developing countries where planning frameworks struggle to keep pace with spatial dynamics. This study investigates the spatiotemporal dynamics, geo‐environmental drivers, and future trajectories of urban expansion in the Wilaya of Constantine, northeastern Algeria, from 1985 to 2050. We used multi‐temporal land‐cover data from the GLC_FCS30D dataset (30 m resolution; 1985–2022) to quantify land‐use/land‐cover (LULC) change and urban growth. We extracted built‐up areas from the impervious surface class and analyzed areal expansion, growth rate, and spatial trajectory. To identify the geo‐environmental determinants of urbanization, 49,878 stratified sample points (24,939 urban and 24,939 non‐urban pixels) were generated, and eight predictor variables slope, elevation, topographic wetness index, drainage density, distance to streams, seismic hazard, geology, and soil type were incorporated into four machine‐learning algorithms. Model performance was assessed through five‐fold cross‐validation, and predictor contributions were interpreted via SHAP (SHapley Additive exPlanations) analysis. Future urban expansion for 2030, 2040, and 2050 was projected under a business‐as‐usual scenario using a Cellular Automata–Markov Chain (CA–Markov) model calibrated using LULC maps for 2000 and 2010, validated by simulating the 2020 land‐cover map and comparing it with the observed GLC_FCS30D 2020 map (Overall Accuracy = 95.7%, Kappa = 0.926, K_no = 0.994). Future projections used a transition matrix derived from the 2010 to 2020 period. Results reveal a near‐fourfold increase in urban area between 1985 and 2022 (+303%; Compound Annual Growth Rate (CAGR) ≈3.84%), with the most intense expansion phase occurring between 1995 and 2000. Random Forest (RF) achieved the highest predictive accuracy (AUC‐ROC = 0.906), with soil type, elevation, and seismic hazard identified as the dominant geo‐environmental predictors of urbanization. Under a business‐as‐usual scenario, the urban area is projected to expand from 113.8 km 2 in 2020 to 205.9 km 2 by 2050, with a plausible range of 124–243 km 2 across low, BAU, and high growth scenarios. The integrated ML/CA‐Markov framework proposed here constitutes a robust, transferable, and policy‐relevant approach for evidence‐based urban planning in rapidly urbanizing Mediterranean cities.