Spatiotemporal dynamics of urban-rural integration and resilience: A multi-layer network study in the Chengdu-Chongqing urban agglomeration

Against the backdrop of accelerated new-type urbanization and urban–rural integration, clarifying the multidimensional interaction between urban–rural integration and regional resilience is crucial for achieving coordinated regional development. This study examines the spatiotemporal evolution of urban–rural integration in the Chengdu–Chongqing Urban Agglomeration from 2009 to 2022 and analyzes its impact on regional resilience. A multilayer network framework is constructed, incorporating four subsystems—economic, social, environmental, and governance—to capture structural coordination and dynamic coupling. Results show that overall regional resilience increased by 63.4 %, with some peripheral cities experiencing growth rates exceeding 100 %. However, due to policy preferences and resource concentration, core cities experienced earlier improvements in infrastructure, governance capacity, ecological investment, and public service provision, resulting in a widening resilience gap between core and peripheral cities—from 0.27 to 0.45. This gap exhibits significant spatial heterogeneity across subsystems and city types, particularly in governance and social dimensions. Further analysis identifies employment opportunities, infrastructure investment, and governance synergy as key driving factors. Accordingly, targeted strategies are proposed, including strengthening factor mobility, improving service systems in peripheral areas, and prioritizing green infrastructure development to promote balanced resilience enhancement. This study reveals the spatial heterogeneity and underlying mechanisms of resilience evolution under policy-driven integration, providing empirical support for understanding uneven regional development in the context of multidimensional integration. • Develops multi-layer network indicators for urban-rural resilience. • Innovates with MIM to quantify nonlinear environmental indicators. • Models dynamic resilience indicators across 2009–2022 periods. • Uses Geodetector to assess spatial heterogeneity in eco-indicators. • Guides eco-management with urban-rural integration indicators.