Spatial correlation networks characteristics and influence mechanisms of the resilience of Beijing–Tianjin–Hebei urban agglomeration: a complex network perspective

Urban resilience forms a multi-level, multi-node spatial correlation network in terms of risk complexity and regional collaboration. However, few studies have explored urban agglomeration resilience in terms of the spatial network. Therefore, a resilience evaluation index system for the Beijing–Tianjin–Hebei urban agglomeration (BTHUA) is constructed in this study based on the driving force-pressure-state-response (DPSR) framework. To this end, the resilience and change characteristics from 2014 to 2022 are analysed using an entropy weight-TOPSIS method and kernel density estimation. Further, the correlation strength and structure of the resilience spatial correlation network in BTHUA are explored using a modified gravity model and social network analysis method, and a quadratic assignment procedure model is used to explain the correlation and mechanism of the influencing factors of the resilience spatial correlation network. The following results were observed: (1) A “core–periphery” pattern in the BTHUA resilience spatial correlation network, with Beijing and Tianjin as pivotal nodes, Shijiazhuang and Tangshan as intermediary connectors, and Zhangjiakou, Xingtai, and Hengshui as weak nodes. (2) Variations in the closeness of the resilience spatial correlation network of BTHUA, with the network exhibiting strong connectivity. However, network centrality shows non-equilibrium characteristics, indicating obvious hierarchical differentiation. (3) Geographic distance, openness to the outside world, economic development level, technological innovation, and urbanisation are identified as influencing factors affecting the resilience spatial correlation network, which show both temporal and spatial heterogeneity. This study provides insights into the collaborative governance of urban agglomerations from the perspective of spatial correlation networks.