Hanyang Wang

Global climate change and rapid urbanization have intensified urban heat risks, particularly in cities such as Jinan that face pronounced heat-related environmental challenges. This study takes Jinan’s main urban area as a case example, integrating the Local Climate Zone (LCZ) framework with the Hazard–Exposure–Vulnerability–Adaptability (HEVA) model to develop multi-temporal heat risk maps. The results indicate the following: (1) High-risk zones are primarily concentrated in the densely built urban core, whereas low-risk areas are mostly located in peripheral green spaces, water bodies, and forested regions. (2) Heat risk shows clear diurnal patterns, peaking between noon and early afternoon and expanding outward from the city center. (3) LCZ6 (open low-rise), despite its theoretical advantage for ventilation, exhibits unexpectedly high levels of heat hazard, exposure, and vulnerability. (4) SHAP-based analysis identifies land surface temperature (LST), floor area ratio (FAR), impervious surface area ratio (ISA), housing value, building coverage ratio (BCR), and the distribution of cooling facilities as the most influential drivers of heat risk. These findings offer a scientific foundation for developing multi-scale, climate-resilient urban planning strategies in Jinan and hold significant practical value for improving urban resilience to extreme heat events.

The iterative process of urban development often produces fragmented renewal zones that disrupt the continuity of urban morphology, undermining both cultural identity and economic cohesion. Addressing this challenge, this study proposes a generative design framework based on Deep Convolutional Generative Adversarial Networks (DCGANs) to predict and regenerate urban morphology in alignment with existing spatial contexts. A dataset was constructed from highly urbanized city centers and used to train a DCGAN model. To evaluate the model performance, seven landscape pattern indices—LPI, PLAND, LSI, MPFD, AI, PLADJ, and NP—were employed to quantify changes in scale, shape, compactness, fragmentation, and spatial adjacency. Results show that the model accurately predicts morphological patterns and captures underlying spatial logic in developed urban areas, demonstrating strong sensitivity to local form characteristics, and enhancing the feasibility of sustainable urban renewal. Nonetheless, the model’s generalizability is constrained by inter-city morphological heterogeneity, highlighting the need for region-specific adaptation. This work contributes a data-driven approach to urban morphology research and offers a scalable framework for form-based, sustainability-oriented urban design.

Coastal traditional rural settlements face increasing challenges from human activities and natural disasters driven by global climate change and rapid urbanization. Ensuring their spatial stability is essential for ecological security, economic development, and social sustainability. This study addresses the lack of unified methodologies for assessing the resilience of regional traditional village clusters by proposing a “network construction–spatial simulation–resilience assessment” framework based on complex network theory. Using the Haicao housing village cluster in Rongcheng City, China, as a case study, a spatial network model was constructed, and resilience was evaluated under both current and future scenarios using eight resilience indicators. The results show that the current spatial network structure exhibits clustering with weak interconnections among subgroups. Key nodes significantly influence network metrics, resulting in low overall resilience. In future scenarios, protective measures targeting the top 15% of villages with high development potential enhanced social connections, reduced reliance on key nodes, and improved network resilience. This study provides a quantitative method for assessing the resilience of traditional village clusters and identifies critical pathways and nodes to optimize regional spatial structures. The findings offer new perspectives for guiding the preservation and sustainable development of traditional villages.