Spatial scale increment identification and dynamic simulation of network resilience disturbances in landscape infrastructure: A comprehensive approach for optimizing regional planning

Abstract

Human-driven material production has brought social and economic benefits. However, it has also exacerbated environmental degradation, posing considerable challenges to the functional attributes and network structures that some communities rely on and threatening their service supply. The current development model continues to compress limited landscape space, and infrastructure has become a characteristic feature of high-density cities, urgently requiring integrated solutions to plan them synergistically. Landscape Infrastructure (LI)-oriented regional planning offer the possibility of integrating social, cultural, and ecosystem services into urban planning. Therefore, this study utilized Genetic Algorithms (GA) to optimize LI distribution, combined with network node testing to assess the rationality of spatial configuration and dynamically simulated urban disturbance scenarios. Using Chongqing as a typical case, the optimized LI area increased by 47.5%, and network efficiency and connectivity indicators significantly improved. While completing the scale increment and structural stability requirements, the model calculations revealed a regional network resilience threshold of 0.36, effectively guiding priority areas for construction and protection. The study provides a replicable Landscape Infrastructure System (LIS) framework, integrating GA and network theory to develop a multi-objective optimization approach. The results support targeted spatial management policies and contribute to sustainable urban development and resilience of socio-ecological systems.