You Zou

The rapid growth of electric vehicles has intensified the spatial mismatch between the layout of charging infrastructure and user demand, resulting in a structural contradiction in which “local oversupply” and “local shortages” coexist. To systematically diagnose and optimize this issue, this study develops an innovative analytical framework for a “residential area–charging infrastructure” collaborative service network and conducts an empirical analysis using Hongshan District in Wuhan as a case study. The framework integrates actual facility utilization data, complex network analysis, and spatial clustering methods. The findings reveal that the collaborative service network in the study area is overall sparse, exhibiting a distinct “core–periphery” structure, with noticeable patterns of resource concentration and isolation. Residential areas can be categorized into three types based on their supply–demand characteristics: efficient-collaborative, transitional-mixed, and low-demand peripheral areas. The predominance of the transitional-mixed type indicates that most areas are currently in an unstable state of supply–demand adjustment. A key systemic mechanism identified in this study is the significant “collaborative reinforcement effect” between facility utilization rates and network centrality. Building on these insights, we propose a hierarchical optimization strategy consisting of “overall network optimization—local cluster coordination—individual facility enhancement.” This ultimately forms a comprehensive decision-support framework for “assessment—diagnosis—optimization,” providing scientific evidence and new solutions for the precise planning and efficient operation of urban charging infrastructure.

Ecological network resilience—the fundamental capacity of ecosystems to maintain functional stability under external disturbances—has emerged as a focal topic in regional ecological security research. This study focuses on the Wuhan Metropolitan Area, employing multi-temporal datasets spanning 2000–2020. First, we developed an ecological security pattern evaluation framework integrating water resources, soil and water conservation, and ecosystem quality to identify ecological source areas. Next, we established a three-dimensional evaluation index system encompassing natural environment, human activities, and physical barriers to generate resistance surfaces. The MCR model was applied to extract ecological corridors and construct the source–corridor pattern. The gravity model was employed to construct ecological networks and analyze their topological structures. Finally, ecological network resilience was assessed through simulated disturbance scenarios. The results indicate that: (1) From 2000 to 2020, the number of ecological source areas was 55, 65, and 54, respectively, exhibiting a “rise–then–decline” trend. Spatially, these areas shifted from scattered to concentrated and contiguous. The network’s core nodes evolved from a decentralized to a highly centralized control structure, increasingly influencing overall network resilience. (2) Over the same period, the number of ecological corridors was 1,485, 2,580, and 1,431, respectively, with primary corridors numbering 41, 89, and 139. These corridors exhibited a spatial pattern of “dense in the south and sparse in the north, dense in the periphery and sparse in the center.” Despite the overall decrease, interaction strength increased, species circulation efficiency improved, and a stable ecological corridor ring gradually formed. (3) The ecological network evolved from incremental expansion to quality- and efficiency-oriented enhancement, ultimately forming an efficient and stable structure. Based on these findings, we identified 8 core nodes, 36 secondary nodes, 29 general nodes, and 86 key ecological corridors in the Wuhan Metropolitan Area, leading to the construction of a composite ecological security pattern characterized as “one screen, three cores, three axes, and multiple networks.” Targeted optimization strategies were proposed to inform the sustainable development of composite ecosystem regions and guide the construction of ecological security patterns.

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities.

Ecological network resilience, the core capacity of ecosystems to maintain functional stability under external disturbances, is of great significance for ensuring regional ecological security and promoting sustainable development. This study takes the Wuhan Metropolitan Area as the research object. Based on multi-temporal data from 2000 to 2020, we innovatively constructed a comprehensive evaluation system of "water resources–soil conservation–ecosystem quality" to identify ecological source areas. Combined with a three-dimensional resistance surface model of "natural environment–human activities–physical barriers," the Minimum Cumulative Resistance (MCR) model was applied to extract ecological corridors, forming a "source–corridor" spatial frame-work. The gravity model was used to construct the ecological network and analyze its topological structure. Finally, a robustness model was employed to assess the dynamic changes in network resilience. The key findings include: (1) The number of ecological source areas exhibited a "rise-then-decline" trend, with spatial distribution shifting from fragmented to clustered and the global control of core nodes transitioning from decentralized to highly centralized. (2) Ecological corridors displayed a "dense-south-sparse-north, dense-periphery-sparse-center" spatial pattern. Although the number of corridors decreased, the interaction strength and species migration efficiency significantly improved. (3) The ecological network evolved from "expansion" to "quality enhancement," with significant improvements in network density and clustering coefficients, ultimately forming an efficient and stable structure. Based on these findings, strategic recommendations are proposed to optimize the ecological network and enhance environmental quality in the Wuhan Metropolitan Area's composite ecosystem.

Metropolitan areas are important regions with a high concentration of population and public service facilities. The coupling coordination between public service facilities and population plays an important role in the sustainable development of economy and society. However, previous studies have focused on a few core cities, effectively identifying areas with weak coordination of public services and currently lacking corresponding information support and paradigms at the regional scale. Taking the Wuhan Metropolitan Area as an example, this paper strengthens the concern about the spatiality and locality of public service facilities in the region;, applies the improved CRITIC method and coupling coordination model to study the spatiotemporal distribution characteristics of public service facilities at the county and grid levels by using multisource data, and evaluates the intercity differences and dynamic changes of coupling coordination relationships between public service facilities and population. The findings are as follows: (1) Wuhan was the core of public service facilities in the metropolitan area, and a continuous high-level coordinated distribution was gradually developed on the east-west axis from 2016 to 2022; (2) there were differences in the coupling coordination degree of different types of facilities, among which the imbalance between the supply and demand of elderly care facilities was obvious; and (3) the coupling coordination degree of facilities in the Wu-E-Huang-Huang core development area was relatively high. Finally, we summarize the development stages of the coupling coordination between the facilities and population in the Wuhan Metropolitan Area and obtain the typical development characteristics. The research results could provide scientific support for planning decisions.