This study analyzes the impact of weather factors on the resilience of airport flight operations, focusing on flight performance, economic outcomes, and transportation capacity. A Kolmogorov–Arnold Network (KAN) model was employed to identify key weather variables and establish the relationship between these factors and airport operational resilience. Xi’an Xianyang International Airport was used as a case study, with the weights of various routes determined using grey relational analysis, considering average daily flight volume, flight distance, and airport flow stability indicators. Flight operation records and weather data were utilized to assess the influence of critical weather factors on key operational resilience metrics. The findings reveal that routes in economically developed areas exert a more pronounced effect on flow stability. Temperature and wind speed emerged as the most influential factors, with importance values of 0.35 and 0.32, respectively, about flight operations and economic performance. Furthermore, changes in wind direction and wind speed had the greatest impact on transportation capacity, with importance values of 0.7 and 0.65, respectively. These results highlight the need for special attention to weather factors such as temperature and wind speed during flight scheduling and risk assessment to ensure operational safety, efficiency, and economic viability.

To analyze the impact of weather factors on the resilience of airport flight operations, particularly on flight operation, economic performance, and transportation capacity, Kolmogorov-Arnold Network (KAN) model was used to identify key weather features and establish the mapping relationship between weather factors and airport operational resilience. Taking Xi'an Xianyang International Airport as an example, the weights of each route were determined using the Grey Relational Analysis method considering average daily flight volume, flight distance, and airport flow stability indicators. Flight operation records and weather data were utilized to calculate the importance of key weather factors on the resilience of critical flight operation indicators. The results indicate significant differences in average daily flight volume, flight distance, and airport flow stability across different routes, with routes in economically developed areas having a greater impact on airport flow stability. Temperature and wind speed changes are the key factors affecting flight operation and economic indicators, with importance values of 0.35 and 0.32 respectively. Wind direction change and wind speed change have the greatest impact on flight transportation capacity, with importance values of 0.7 and 0.65 respectively. Therefore, when conducting flight scheduling and risk assessment, special attention should be paid to key weather factors such as temperature and wind speed, while also considering the weight of each route to ensure flight safety, efficiency, and economic benefits.

In an urban road network, the ability of the traffic flow itself to alleviate congestion caused by external disruptions is overlooked. This study applied the two‐fluid model to simulate mesoscopic traffic flow, focusing on the resilience of urban road networks under normal disturbances. Three resilience indices—plasticity, transition of elasticity, and elasticity—were introduced based on the failure deformation process of rigid materials. These indices were used to modify the two‐fluid model’s parameters, considering the effects of bus operations and temporary roadblocks on traffic flow and service quality. A hidden Markov model (HMM) was employed to predict service quality transitions (distinction, merit, and pass), with validation using dynamic bayonet traffic data from Xuancheng and video recordings from Xi’an, China. The results confirmed that resilience varies significantly across different times and locations, with peak hours and dense urban areas exhibiting lower resilience and higher susceptibility to disruptions. Bus queuing was found to degrade service quality, and rainstorms had a more severe impact than construction zones. The study can aid in the development of management efficiency of urban road networks.

In the context of global digital transformation, scientifically examining the spatio-temporal evolution patterns and transition mechanisms of the digital economy at the municipal level is crucial for promoting coordinated regional development. This study takes 281 prefecture-level cities in China from 2011 to 2023 as its research units. Exploratory Spatio-Temporal Data Analysis (ESTDA) is employed to analyze its spatio-temporal dynamics, while a panel quantile regression model nested with spatio-temporal transition types is used to reveal the driving mechanisms. The findings indicate that (1) the overall development level of China’s municipal digital economy has steadily increased, yet significant regional heterogeneity persists, characterized by a pattern of “eastern leading, central fastest-growing, and western lagging,” with considerable room for overall improvement. (2) The digital economy exhibits a significant positive spatial correlation. High–high agglomeration areas remain stable in the southeastern coast, whereas low–low agglomeration areas are concentrated in the central-western and northeastern regions. The spatial pattern demonstrates strong stability and path dependence. (3) LISA time paths reveal drastic changes in local spatial structures in provinces such as Heilongjiang, Inner Mongolia, Hubei, Guangdong, and Guangxi, while East and Central China remain relatively stable. Tortuosity analysis indicates that spatial linkages in the western region are becoming active yet unstable. (4) The quantile regression nested with transition types identifies four mechanisms: “Economic development-Technological innovation” serves as the fundamental driving mechanism across all regions. Low-quantile areas face a complex situation with dual suppression from “opening-up and urbanization” coexisting with drivers from “human capital, government intervention, and industrial structure.” High-quantile areas are synergistically driven by “urbanization, human capital, government intervention, and advanced industrial structure.” This study provides a decision-making reference for overcoming the dilemma of “low-level club convergence” in digital economy development and formulating differentiated regional policies.

This study establishes a municipal-level evaluation system for rural revitalization in China, grounded in the five-sphere integrated framework encompassing “prosperous industries, livable ecology, civilized rural customs, effective governance, and affluent life.” Employing methodologies including the entropy weight-coupling coordination model, LISA spatiotemporal analysis, and multi-scale geographically weighted regression (MGWR), it empirically investigates the evolution and driving mechanisms of rural revitalization development across 282 prefecture-level cities from 2011 to 2023. The findings reveal: (1) Nationwide and regional rural revitalization levels demonstrate a consistent upward trajectory, progressing from a state of “Mild Disorder” to being “On the Verge of Disorder,” with a distinct gradient pattern of “Eastern Region > National Average > Central Region > Western Region.” (2) Significant global spatial correlation is observed, manifesting as polarization typified by “high–high” and “low–low” agglomeration, alongside notable volatility in Northeast and Southwest China. (3) Influencing factors display marked spatiotemporal heterogeneity. Agricultural production efficiency (North China) and technological innovation (nationwide, except the Yangtze River Delta) significantly foster rural revitalization. Conversely, economic development level (Northeast, Central, and Western China), government intervention (Northeast China), and industrial structure upgrading (Northwest China) exhibit constraining effects. The localized positive impacts of urbanization (border areas of Yunnan, Heilongjiang, Sichuan, Jilin, and Tibet) and opening up (border ports) are increasingly evident. Building on these insights, the study proposes recommendations—such as implementing differentiated regional policies, innovating spatial governance models, and activating multidimensional drivers—to overcome the “low-level lock-in” predicament and advance comprehensive rural revitalization. Furthermore, this paper reveals the patterns of multidimensional system coupling and the spatial heterogeneity of driving mechanisms. These findings provide a reference for deepening the understanding of geographical complexity within global sustainable development theory.