A Deterministic ANN–CA Computational Framework for Spatial Simulation Using Socioeconomic Data

Hybrid approaches combining Cellular Automata (CA) and Artificial Neural Networks (ANN) have been widely applied to spatial simulation; however, most implementations rely on stochastic components that limit reproducibility and interpretability. This study proposes a deterministic ANN–CA computational framework in which the stochastic perturbation term of a constrained CA model is replaced by ANN-derived classification values based on socioeconomic variables. The framework integrates data preprocessing, ANN training, transition coefficient generation, and CA-based simulation into a unified workflow. A multilayer perceptron is trained using spatialized socioeconomic indicators (age, education, sex, and income) to generate deterministic transition potentials at the pixel level. Experimental evaluation using multitemporal land-use data shows that the proposed ANN–CA model achieves a moderate improvement in global spatial association (Cramer’s V: 0.5622 → 0.6016), while pixel-level agreement (Kappa: 0.6589 → 0.6595) remains nearly unchanged. These results indicate that the proposed approach primarily enhances structural coherence and spatial organization—reducing fragmented growth and improving corridor-oriented expansion—rather than significantly increasing pixel-wise predictive accuracy. By replacing stochastic behavior with data-driven deterministic rules, the proposed framework improves reproducibility and provides a more interpretable linkage between urban growth patterns and socioeconomic drivers. This work contributes a transparent hybrid modeling approach suitable for spatial simulation and planning-oriented applications.