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

Abstract: 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.

Álvaro Peraza Garzón, René Rodríguez Zamora, Mónica Avelina Gutiérrez Haros, Iliana Amabely Silva Hernández and Juan Francisco Peraza Garzón. “A Deterministic ANN–CA Computational Framework for Spatial Simulation Using Socioeconomic Data”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170377

@article{Garzón2026,
title = {A Deterministic ANN–CA Computational Framework for Spatial Simulation Using Socioeconomic Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170377},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170377},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Álvaro Peraza Garzón and René Rodríguez Zamora and Mónica Avelina Gutiérrez Haros and Iliana Amabely Silva Hernández and Juan Francisco Peraza Garzón}
}