Under the pressure of carbon pollution and resource scarcity, electric vehicles (EVs) have gradually replaced fuel vehicles as a new trend of low-carbon transformation. However, public charging stations (PCS) face with problems such as insufficient quantity and unreasonable distribution. By using multi-source big data, this paper analyzes the population distribution, traffic organization, infrastructure, land use and regional economy of Jinan urban area, China, and constructs a comprehensive evaluation index system to predict the spatial demand of PCS for EVs. We analyse: (1) Distribution of population activities on weekday and rest days, the closeness and betweenness of road network, high-density area, commerce, public service facilities, parks, transportation facilities, residential area, building coverage, floor area ratio, economic development area and housing price level. (2) Correlation and influence weights of 14 evaluation indexes and PCS layout. (3) Prediction of spatial demand distribution of PCS. (4) Comparison of current PCS distribution and spatial demand prediction results. This method makes up for the deficiencies of too single consideration factor, lack of intuitiveness of mathematical model and lack of urban geospatial research. This is of significance for predicting the demand distribution of PCS in the future and further promoting the whole popularity of EVs.

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.

The development of primary healthcare facilities facilitates a balanced distribution of medical resources, relieving pressure on large hospitals by enhancing these facilities, thus optimizing resource allocation. Such equitable distribution not only improves the accessibility and fairness of healthcare services, but also supports their sustainable growth. This study introduces a spatial resilience framework that evaluates transmission, diversity, and stability from a spatial resilience perspective. Focusing on Harbin's main urban area's primary healthcare facilities, methods such as Gaussian-Based Two-Step Floating Catchment Area (2SFCA), network analysis-based service radius, walking impedance, and overlap analysis are utilized for spatial distribution analysis. Findings indicate significant variance in facility transmission, with Daoli, Nangang, and Xiangfang Districts showing higher accessibility, Songbei and Daowai Districts exhibiting lower transmission. The road network capacity decreases from the urban center towards the periphery in a diminishing pattern. Local Indicators of Spatial Association (LISA) identifies areas with demand-supply mismatches, primarily in northern Xiangfang District and southern Songbei District, while overall spatial connection remains balanced. Based on these findings, we propose strategic enhancements for medical facilities of corresponding levels, offering insights for similar large cities seeking to refine their primary healthcare spatial layout.

Under the dual backdrop of the Healthy China strategy and the concept of sustainable development, optimizing the spatial layout of primary healthcare facilities is important for fairly distributing healthcare resources and strengthening the resilience of the public health system in a sustainable way. This study introduces an innovative 3D spatial resilience evaluation framework, covering transmission (service accessibility), diversity (facility type matching), and stability (supply demand balance). Unlike traditional accessibility studies, the concept of “resilience” here highlights a system’s ability to adapt to sudden public health events through spatial reorganization, contrasting sharply with vulnerable systems that lack resilience. Method-wise, the study uses an improved Gaussian two-step floating catchment area method (Ga2SFCA) to measure spatial accessibility, applies a geographically weighted regression model (GWR) to analyze spatial heterogeneity factors, combines network analysis tools to assess service coverage efficiency, and uses spatial overlay analysis to identify areas with supply demand imbalances. Harbin is located in northeastern China and is the capital of Heilongjiang Province. Since Harbin is a typical central city in the northeast region, with a large population and clear regional differences, it was chosen as the case study. The case study in Harbin’s main urban area shows clear spatial differences in medical accessibility. Daoli, Nangang, and Xiangfang form a highly accessible cluster, while Songbei and Daowai show clear service gaps. The GWR model reveals that population density and facility density are key factors driving differences in service accessibility. LISA cluster analysis identifies two typical hot spots with supply demand imbalances: northern Xiangfang and southern Songbei. Finally, based on these findings, recommendations are made to increase appropriate-level medical facilities, offering useful insights for fine-tuning the spatial layout of basic healthcare facilities in similar large cities.

The sustainable regeneration of industrial heritage in cold regions is constrained by severe winter climate, seasonal behavioral shifts, and declining spatial vitality. However, existing research has rarely explained how cold-climate conditions influence catalyst effects and regeneration performance in industrial heritage areas. This study proposes a digital twin-enabled framework for the sustainable regeneration of cold-region industrial heritage. Using industrial heritage sites in Harbin, China, as a case study, the research integrates multi-source data to construct a dynamic assessment system that links climate constraints, spatial structure, and human activity patterns. The results show that winter conditions significantly reduce the effectiveness of traditional catalyst mechanisms by weakening outdoor interaction, fragmenting movement continuity, and increasing reliance on indoor transitional spaces. Simulation results further demonstrate that climate-responsive interventions, such as indoor connectivity enhancement, mixed-use functional implantation, and seasonal activity optimization, can improve regeneration effectiveness and spatial resilience. By combining digital twin technology with sustainable urban regeneration theory, this study provides a replicable analytical framework and practical decision-support tool for industrial heritage revitalization in cold-region cities.