Public spaces are essential for the implementation of child-friendly principles and the development of child-friendly cities, with positive and healthy environments playing a crucial role in supporting children’s well-being and development. However, existing research on child-friendly public spaces predominantly targets economically developed urban areas with robust public service infrastructure, often neglecting rural areas with less advanced facilities. This study utilizes grounded theory and qualitative analysis to propose a child-friendly public space evaluation framework specifically for rural settings. The framework includes four primary indicators—safety, accessibility, comfort, and multifunctionality—and 19 secondary indicators, such as facility safety and plant safety. An empirical investigation was conducted in Baishoupu Town, a child-friendly pilot area within Changsha, China, which is designated as a United Nations Child-Friendly City, and the study encompassed an analysis of 11 rural villages within this area. The findings reveal that while Baishoupu Town demonstrates a relatively high level of child-friendly development, there is significant disparity among individual villages. Key determinants affecting the child-friendliness of rural public spaces include the type of rural industry, per capita income levels, and the degree of policy support. Specifically, the advancement of public service infrastructure and the tourism sector significantly influence the primary indicators. Moreover, while rural road infrastructure is positively correlated with accessibility, the presence of through traffic adversely affects safety indicators. Based on these insights, this study recommends enhancing child-friendliness in rural public spaces through strategic village planning, spatial design improvements, and ensuring child participation. This research provides valuable insights for government policy development and implementation and offers a replicable framework for child-friendly public space development in rural areas globally.

With the rapid development of cities and the rapid increase in automobile ownership, traffic has become one of the main sources of PM2.5 pollution, which can be reduced by road greening through sedimentation, blocking, adhesion, and absorption. Using the method of combining field monitoring and ENVI-met simulation, the influence of the tree height on the PM2.5 concentration on both sides of the city streets was discussed. The influence of tree height on PM2.5 under five conditions was analyzed, including 10 m tall trees (i), 15 m tall trees (ii), alternating distribution of 15 and 10 m tall trees (iii), 5 m tall trees (iv), no trees on either side of the road (v). The results show that: Roadside trees can increase the concentration of PM2.5 in the narrow space of street canyons. However, without roadside trees, PM2.5 from traffic sources is not reduced in time, it is more easily spread to the distance. When the height of the roadside trees is 5 m and their crown widths are smaller than those of other trees, there is a relatively wide space between them. Compared with the higher roadside tree models with larger crown widths, the concentration of PM2.5 on the roadway and the downwind sidewalk is relatively low. In the three models (i–iii) with tree height above or equal to 10 m, the PM2.5 concentration around the trees do not show regular change with the change in tree height. Due to the tree height of 10 and 15 m, the crown width is large enough, and the alternate distribution of tree height of 15 and 10 m fails to make the PM2.5 concentration in the models lower than the models with tree height of 15 m or 10 m. The reasonable height of roadside trees in street canyons helps improve the wind circulation to promote the diffusion of PM2.5 pollution. There is no optimal height of roadside trees for PM2.5 pollution in street canyons, thus it is necessary to select the height reasonably according to the specific situation.

Metropolitan hinterlands are zones of intense urban–rural factor flows and spatial restructuring, where understanding rural settlement evolution is crucial for revealing human–land relationship transformations. Taking Changsha County, the core hinterland of the Changsha–Zhuzhou–Xiangtan metropolitan area in central China, as a case study, we integrated landscape pattern indices, kernel density estimation, centroid migration, the Optimal Parameters-based Geographical Detector (OPGD), and Geographically Weighted Random Forest (GWRF) to analyze the spatiotemporal evolution of rural settlements from 1990 to 2020 and identify the factors associated with the spatial differentiation of rural settlement scale in 2020. The results showed that: (1) The scale of rural settlements continuously expanded, with the total area increasing by 69.7% while patch density declined by 26.7%, exhibiting a “dense south, sparse north” pattern. High-value kernel density zones progressively clustered toward the southwestern concentric zone, and the settlement centroid persistently migrated toward the urban core. (2) The output value of secondary and tertiary industries per unit area, NDVI, and living facility adequacy were identified as the core driving factors; GDP per capita, distance to cropland, and distance to major roads also exerted notable effects, and strong synergistic interactions were detected among these factors. (3) GWRF-SHAP analysis revealed pronounced spatial heterogeneity: NDVI exhibited a south-promotion, north-suppression bidirectional effect; distance to cropland showed the most stable positive influence; road proximity was significant only at transportation hubs; the output value of secondary and tertiary industries displayed a polarized “central driving, north–south suppression” pattern; and socioeconomic factors generally stimulated expansion in suburban areas while inhibiting it in remote hinterlands. This spatial divergence can be interpreted through the “south-industry, north-agriculture” structure: suburban industrial corridors are associated with externally oriented attraction, whereas remote agricultural hinterlands are more closely related to endogenous, resource-based upgrading. The study proposes a compound explanatory framework of “natural baseline constraints–locational guidance–socioeconomic dominance,” providing a scientific basis for differentiated spatial governance of rural settlements in metropolitan hinterlands.

Ecological changes in mining areas are characterized by long-term dynamics, temporal stages, and spatial heterogeneity. Existing monitoring methods often face challenges in balancing multidimensional ecological condition characterization with spatially consistent analysis and have limited capability to identify complex disturbance–recovery trajectories. To address these issues, an Object-based Ecological Index-driven Framework (OEI-GTCD) is proposed to monitor ecological disturbance and recovery trajectories in mining areas. The framework uses the Mining-area Ecological Index (MEI) to characterize multidimensional ecological conditions and conducts analysis based on spatially consistent object units. On this basis, a Globally Optimized Time-series Change Detection method (GTCD) is proposed to improve the identification of complex ecological disturbance–recovery trajectories by integrating trend-change detection, abrupt-change detection, and segmentation optimization. Landsat imagery from 2000 to 2024 was used to characterize the spatiotemporal patterns of ecological disturbance and recovery across the Daye Mining Area, China. The findings suggest that MEI captures multiple dimensions of ecological conditions, while object-scale analysis characterizes ecological changes through overall object-level trajectories, supporting their spatial interpretation. OEI-GTCD achieved disturbance and recovery detection accuracies of 88.4% and 84.8%, respectively, representing improvements of 6.0 and 5.2 percentage points over OEI-LandTrendr. Ablation analyses further highlighted the contributions of abrupt-change detection and the improved segmentation criterion to event identification, as well as the role of local breakpoint refinement in temporal localization. From 2000 to 2024, stable and continuously improving ecological states together accounted for 82.07% of the area represented by the non-water objects used in the ecological analysis, while disturbance and recovery processes exhibited pronounced spatiotemporal heterogeneity. Overall, OEI-GTCD provides an integrated approach for monitoring ecological disturbance and recovery and characterizing their spatiotemporal patterns in mining areas.