Advances And Prospects of Cellular Automata in Simulating Spatial Expansion of Metropolitan Areas

Abstract

This study systematically reviews research advances in Cellular Automata (CA) modeling for spatial expansion simulation. Bibliometric and model evolution analyses reveal an evolutionary trajectory of Cellular Automata models transitioning from basic morphological simulation to "multi-factor coupling, uncertainty quantification, and intelligent optimization". Methodological innovations in resolving the "high-density agglomeration–resource overload" paradox are examined, focusing on three directions: ecological constraint integration, deep learning optimization, and uncertainty quantification. Following the logical framework of "technological evolution–scale adaptation–paradigm innovation", future breakthroughs are proposed addressing bottlenecks in scale effects and social mechanism integration, thereby providing scientific support for territorial spatial planning. In addition, the review emphasizes the necessity of integrating cross-disciplinary methods to enhance the explanatory power of CA models. It also highlights the importance of policy-oriented applications in guiding sustainable urban expansion. Ultimately, the study aims to bridge theoretical modeling with practical governance, ensuring CA research contributes effectively to real-world planning challenges.