With the global spread of various human-to-human epidemics, public health issues have become a focus of attention. Therefore, it is of great importance to improve the quantitative risk assessment of the construction of resilient cities in terms of epidemic disasters. Starting with the dimensions of social activities and material space, this paper took Qingdao, China, with a population of 5 million, as an example, and took its seven municipal districts as the research scope. In this paper, five risk factors, including the Population density index, Night light index, Closeness index of roads, Betweenness index of roads and Functional mixed nuclear density index were selected for weighted superposition analysis. We conducted a quantitative assessment of the spatial risk of epidemic disaster so as to obtain the classification and spatial structure of the epidemic disaster risk intensity. The results show that: ① The roads with a large traffic flow are most likely to lead to the risk of urban spatial agglomeration, and the areas with a large population density and large mixture of infrastructure functions are also important factors causing the risk of epidemic agglomeration. ② The analysis results regarding the population, commerce, public services, transportation, residence, industry, green space and other functional places can reflect the high-risk areas for epidemic diseases with different natures of transmission. ③ The risk intensity of epidemic disasters is divided into five risk grade areas. Among them, the spatial structure of epidemic disasters, composed of the first-level risk areas, is characterized by “one main area, four secondary areas, one belt and multiple points” and has the characteristics of spatial diffusion. ④ Catering, shopping, life services, hospitals, schools and transportation functional places are more likely to cause crowd gathering. The management of these places should be focused on prevention and control. At the same time, medical facilities should be established at fixed points in all high-risk areas to ensure the full coverage of services. In general, the quantitative assessment of the spatial risk of major epidemic disasters improves the disaster risk assessment system in the construction of resilient cities. It also focuses on risk assessment for public health events. It is helpful to accurately locate the agglomeration risk areas and epidemic transmission paths that are prone to outbreak or cause epidemic transmission in cities so as to assist the relevant practitioners in containing the epidemic from the initial stage of transmission in a timely manner and prevent the further spread of the epidemic.

Public charging infrastructure plays a critical role in supporting the sustainable development of electric vehicles (EVs), yet its effectiveness is often constrained by environmental conditions and spatial mismatches between supply and demand. This study develops a demand-oriented analytical framework to evaluate public charging services and support compensatory layout planning under cold-climate conditions, using Harbin, China, as a case study. The framework integrates demand hotspot identification, climate-adjusted service coverage reconstruction, service mismatch diagnosis, and compensatory layout recommendations. The results show that public charging demand in Harbin exhibits a clear centre-oriented clustering pattern. As cold-climate constraints intensify, the effective service coverage of charging facilities continuously contracts, and service mismatch areas become concentrated in high-demand clusters, forming an overall pattern of prominent central areas and scattered peripheral zones. Under the general winter scenario, a total of 197 service mismatch grids were identified, accounting for 58.98% of all hotspot grids. After the proposed compensatory layout, the number of mismatch grids decreased to 115, representing a reduction of 82 grids or 41.62%. These findings demonstrate that climate-sensitive service evaluation is essential for accurately identifying critical service deficiencies in cold-climate cities. The proposed framework provides a transferable approach for climate-sensitive service evaluation and compensatory layout planning of public charging infrastructure in high-latitude urban areas.

Under China’s “Dual Carbon” goals, the electric vehicle (EV) industry has expanded rapidly, while the imbalance between supply and demand in public charging infrastructure (PCI) has emerged as a critical bottleneck. Accordingly, a structural assessment of PCI demand potential is essential for improving planning effectiveness. Focusing on the seven municipal districts of Qingdao, this study developed a dual-dimensional framework integrating physical space and population activity. Five core factors were incorporated: road network accessibility, road network betweenness, POI functional mixing density, population distribution density, and nighttime light intensity. By integrating Spatial Design Network Analysis (sDNA), Kernel Density Estimation (KDE), and the entropy weighting method, we conducted a structural assessment of PCI demand potential and derived spatial demand tiers and hierarchy. The results indicate that: (1) road network betweenness had the highest weight (0.396), acting as the dominant driver of structural demand potential, followed by POI functional mixing density (0.271), whereas nighttime light intensity (0.151) and population distribution density (0.143) functioned as baseline supportive indicators; (2) spatial demand was classified into five levels (Levels 1–5), with Level 1 hotspots exhibiting a radial spatial structure characterized by “one primary core, four secondary cores, three corridors, and multiple nodes”; and (3) while the existing PCI distribution exhibited overall gradient consistency with the structurally derived demand tiers, quantitative deviation results indicated localized mismatches, including under-allocation in high-demand areas and over-allocation in selected lower-demand pockets. The proposed dual-dimensional framework facilitates the identification of structural demand gradients for PCI by explicitly incorporating traffic-flow potential, functional aggregation, and population concentration. These findings provide planning-oriented diagnostic support for PCI configuration and contribute to the sustainable transformation of urban transportation systems in megacities.

To enhance winter tourism competitiveness and address seasonal tourist flow pressures, this study adopts Harbin as a case study and introduces a metamodernist theoretical framework. This framework redefines the “population pulse” phenomenon as a structural oscillation involving periodic switching between the two poles of global tourist consumption and local resident daily needs. By integrating multi-source spatiotemporal data, the study employs X-means clustering to identify population aggregation–dispersion patterns and combines the Geographical Detector and GWR model to construct a complete technical pathway ranging from global factor detection to local heterogeneity analysis. The findings reveal that (1) population activity in Harbin exhibits a “monocentric polarization” pattern during the peak season, which shifts to a “polycentric weak agglomeration” mode in the off-season, reflecting the seasonal oscillation of the city’s functional roles; (2) X-means clustering identifies three types of functional zones: transit-oriented areas on the urban periphery, commercial supporting service zones, and core commercial districts; (3) the Geographical Detector quantifies the independent explanatory power and interactive effects of various influencing factors, identifying the interaction between POI density and road network accessibility as having the strongest explanatory power regarding population aggregation; (4) GWR analysis reveals significant spatiotemporal heterogeneity in the effects of various built environment and socioeconomic driving factors. This study provides specific evidence and technical support for urban planning practices in Harbin and other similar cities, deepens the theoretical understanding of the “constitutive conditions” of urban vitality, and explores a post-paradigmatic research path in geographical methodology that can embrace complexity and analyze oscillatory behavior.

The impact of electric vehicle (EV) charging stations on sustainable urban transitions has become a crucial research topic due to the rapid growth of the new energy industry. However, the uneven distribution of EV charging infrastructure currently presents a major challenge to urban spatial equity and sustainable development. This study focuses on the central metropolitan area of Wuhan, positioning EV charging stations as a key mediating variable to explore how green infrastructure interacts with the built environment to drive sustainable economic vitality. Utilizing multi-source big data, the XGBoost-SHAP model, and Bootstrap mediation tests, this research identifies the non-linear thresholds and mediation pathways involved. The findings reveal that: (1) distinct built environment factors exhibit clear non-linear relationships with commercial vitality; (2) EV charging stations act as critical mediators that spatially reinforce or balance built environment variables, exhibiting pronounced spatial heterogeneity; and (3) EV charging stations show a significant partial mediation effect, with building density wielding the strongest direct influence. This study provides vital scientific support for low-carbon urban planning and sustainable infrastructure deployment by uncovering the threshold characteristics and mediating mechanisms necessary to harmonize commercial vitality with environmental and urban sustainability.