Characterizing the spatial correlation network of cultivated land system resilience: Nonlinear relationships between interregional production factor flows and node centrality

Against the backdrop of intensifying uncertainty shocks and increasingly frequent cross-regional factor flows, the spatial governance of cultivated land system resilience (CLSR) has become an increasingly important issue. Existing studies have largely treated CLSR as a localized attribute confined to specific administrative units, while paying limited attention to the evolution of its spatial correlation network. To address this gap, the Vertical and Horizontal Scatter Degree Method, Social Network Analysis, and the XGBoost-GeoShapley model were employed to examine the spatiotemporal patterns of CLSR and interregional flows of labor, capital, and technology across Chinese cities, and to identify the nonlinear relationships between interregional production factor flows and node centrality in the CLSR spatial correlation network. The results show that CLSR in China increased steadily and exhibited a clear east-west gradient, with higher levels in the east and lower levels in the west. Interregional flows of labor, capital, and technology continued to intensify, while spatial agglomeration and differentiation coexisted. The CLSR spatial correlation network in China was characterized by overall connectivity but sparse linkages. Network density fluctuated only between 0.10 and 0.14, indicating that substantive intercity linkages in CLSR remained relatively weak. All node centrality measures displayed long-tailed distributions, and cities such as Beijing, Shanghai, Tianjin, Suzhou, Chongqing, and Qingdao occupied advantageous positions in terms of connectivity, accessibility, and bridging functions. Further analysis reveals a significant nonlinear relationship between interregional production factor flows and node centrality in the CLSR spatial correlation network. Overall, low levels of interregional production factor flows were associated with positive contributions to node centrality, whereas high levels were associated with negative contributions, which gradually stabilized at the upper end of the flow distribution. Moreover, fiscal support, geographical location, and their interaction emerged as important factors explaining heterogeneity in node centrality. From the perspective of relational geography, this study extends the analytical framework of CLSR and provides a scientific basis for the cross-regional coordination of CLSR enhancement and the optimization of resource factor allocation.