Abstract
Focusing on the regional resources–population–economy coupling system, this study systematically reviews the multi-scale interaction mechanisms between urban agglomerations and peripheral areas, with particular attention to the complex spatial coupling and evolutionary characteristics of resource endowments, population mobility, and economic structures. In light of advancements in big data and artificial intelligence, this highlights the advantages of multi-source heterogeneous data in the dynamic monitoring of coupling systems, and emphasizes the critical role of machine learning algorithms in uncovering nonlinear coupling patterns and enabling dynamic prediction and simulation. By constructing a multi-center structural model and an integrated framework of “scenario prediction–feedback regulation–dynamic evaluation,” this paper reveals the underlying mechanisms and technical pathways for promoting regional coordination and intelligent governance. The study aims to provide theoretical support and technical guidance for optimizing regional resource allocation and enhancing the synergistic development capacity of population and economic systems, thereby contributing to the achievement of high-quality and sustainable development goals.
