Urban public green space (UPGS) plays an important role in sustainable development. In China, the planning, classification, and management of green spaces are based on the Standard for Classification of Urban Green Space (SCUGS). However, limitations to the UPGS exist due to the over-emphasis on quantitative standards and insufficient consideration of the actual access mode of residents. Though the taxi trajectory data are widely selected to study public service facilities, its adoption in UPGSs research remains limited. Based on the case of UPGSs in the three districts of Shenzhen, we used the taxi (including cruise taxis and Didi cars, which are like Uber) trajectory data to investigate the spatial layout and the allocation of management resource of the UPGSs from the spatial interaction perspective. By rasterizing and visualizing the percentage of pick-up and drop-off points in the UPGSs’ buffer, the service scope of UPGSs was defined, which reflected the spatial distribution and activity intensity of the visitors. Then, an unsupervised classification method was introduced to reclassify the twenty two municipal parks in the three districts. Compared to the traditional planning method, the results show that the service scope of the same type of UPGS in the traditional classification is not the same as the one obtained by the study. Visitors to all UPGSs are distributed as a quadratic function and decay as the distance increases. In addition, the attenuation rates of the same type of UPGSs are similar. The findings of this study are expected to assist planners in improving the spatial layout of UPGSs and optimizing the allocation of UPGS management resources based on new classifications.

Rapid urbanization has significantly exacerbated the Urban Heat Island (UHI) effect in high-density megacities, driven by the intensifying competition between built-up morphology and natural cooling infrastructure. Current research, however, often fails to accurately predict land surface temperatures (LST) because traditional models frequently overlook the complex spatial dependencies and neighborhood spillover effects inherent in urban environments. Existing studies often ignore the spatial dependence of heat transfer. This study proposes an explainable machine learning framework incorporating spatial lag variables to capture the thermal spillover from adjacent neighborhood context—such as green space cooling diffusion or built-up heat accumulation—which is frequently treated as noise in traditional models. Taking Shenzhen as a case study, we integrated multi-source data (Landsat 8, building vectors, DEM) and developed an XGBoost regression model (R² = 0.806) augmented with SHAP (Shapley Additive exPlanations) to quantify the contributions of local and contextual features. The results revealed that: (1) Non-linear Thresholds: Vegetation cooling exhibits a saturation effect, with the highest marginal benefit observed in the NDVI range of 0.2–0.4, while building warming effects converge at extremely high densities due to mutual shading; (2) Neighborhood Spillovers: Spatial interaction analysis confirms significant cool island synergy (where clustered green spaces provide amplified cooling) and heat island agglomeration effects—e.g., green spaces surrounded by high ecological backgrounds provide amplified cooling benefits; (3) Spatial Antagonism: A novel Interaction Balance Index (IBI) based on game-theoretic SHAP contributions was constructed to map the source-sink competition patterns, identifying distinct heat-dominated (West) and cool-dominated (East) zones. Unlike traditional area-weighted source-sink landscape metrics, IBI enables a pixel-level additive decomposition of warming and cooling factors, quantifying the net thermal outcome of local morphology and neighborhood spillover. By explicitly encoding spatial context into non-linear modeling, this study provides a more mechanistically robust understanding of urban thermal environments. The identified thresholds and dominant driver maps offer precise, spatially differentiated guidance for urban climate-adaptive planning and ecological restoration.

As an important public facility in cities, hospitals provide services that are vital to people’s lives. Big data is representative of geographic data from numerous sources. It has the advantages of large amounts of data, high timeliness, and abundant information, which may compensate for the inadequacy of traditional planning and design, which is overly subjective. Big data can be used to obtain large-scale human movement data to study hospital characteristics from a supply–demand balance perspective. Here, 182 comprehensive hospitals in three districts of Shenzhen were picked as an example, and multi-source data were introduced as a quantitative assessment tool to analyze them. First, the mobile public participation geographic information system (PPGIS) was researched and developed independently and used to collect volunteered geographic information (VGI) data, which was adopted to quantify hospital service supply. Second, the population’s demand for hospitals was characterized by the quantified point of interest (POI) data. Finally, the location entropy method was utilized to quantify the levels of supply and demand for comprehensive hospital services from a supply–demand balance perspective, which was used to guide the classification of the comprehensive hospitals. The result shows that: (1) the service scopes and the service pressure are different for the same type of comprehensive hospitals classified by the traditional method; (2) the spatial distribution of patients is related to land function; (3) the overlap area between the part with a high service pressure level in the comprehensive hospital and the part with high medical demand of the patient is prone to service blindness. This study paves a new way to optimize the spatial allocation of medical institutions or other types of public facilities, rationalize resource allocation, and alleviate urban transportation congestion.

The building sector is a major source of global carbon emissions, with embodied carbon playing an increasingly critical role. This study quantitatively compares the life-cycle embodied carbon of three residential building design patterns (cast-in situ, CIS; design for disassembly, DfD; and skeleton–infill, SI) under a unified scenario of a 90-year service life with functional renovations every 30 years. A total of 36 cases, derived from twelve prototypical residential designs implemented in each pattern, were evaluated via life-cycle assessment following the EN 15978 standard. The results show that the SI pattern reduces embodied carbon by 45–55% compared to CIS, while the DfD pattern achieves a 35–45% reduction (with SI pattern consistently performing best). Structural system selection also influences outcomes, with shear wall structures reducing emissions by 7–14% compared to frame systems. Plan layout effects were marginal. The analysis indicates that the design pattern is the dominant factor influencing embodied carbon outcomes. SI pattern yields the largest carbon reduction by pairing a long-lived structural frame with flexible infill that extends service life and adaptability, while DfD lowers material demand via component reuse. These findings highlight the substantial benefits of circular design strategies and support a shift toward more adaptable, long-lifespan, and low-carbon residential design.

Rapid urbanization has exacerbated urban-stormwater challenges, highlighting the critical need for coordinated surface-water and groundwater management through rainfall recharge. However, current sponge city construction methods often overlook the crucial role of underground aquifers in regulating the water cycle and mostly rely on simplified engineering approaches. To address these limitations, this study proposes a spatial optimization framework for urban-scale sponge systems that integrates a hydrodynamic model (FVCOM), geographic information systems (GIS), and Monte Carlo simulations. This framework establishes a comprehensive evaluation system that synergistically integrates surface water inundation depth, geological lithology, and groundwater depth to quantitatively assess sponge city suitability. The FVCOM was employed to simulate surface water inundation processes under extreme rainfall scenarios, while GIS facilitated spatial analysis and data integration. The Monte Carlo simulation was utilized to optimize the spatial layout by objectively determining factor weights and evaluate result uncertainty. Using Shenzhen City in China as a case study, this research combined the “matrix-corridor-patch” theory from landscape ecology to optimize the spatial structure of the sponge system. Furthermore, differentiated planning and management strategies were proposed based on regional characteristics and uncertainty analysis. The research findings provide a replicable and verifiable methodology for developing sponge city systems in high-density urban areas. The core value of this methodology lies in its creation of a scientific decision-making tool for direct application in urban planning. This tool can significantly enhance a city’s climate resilience and facilitate the coordinated, optimal management of water resources amid environmental changes.

The spatial heterogeneity of social vulnerability in China’s floodplains remains underexplored, particularly regarding how rapid urbanization and unique demographic shifts-specifically the “floating population” and an aging society-reshape human-water relationships. This study quantifies flood exposure across 361 Chinese cities by integrating the 2020 Seventh National Population Census with high-resolution (250 m) floodplain maps via a pixel-based spatial aggregation approach. This methodology captures both absolute population scales and relative structural dependencies within hazard zones, effectively eliminating estimation bias from administrative-level averages. We propose a coupled Principal Component Analysis and Multiscale Geographically Weighted Regression (PCA-MGWR) framework to mitigate multicollinearity among socioeconomic variables while capturing spatially varying scale effects. The analysis identifies four distinct vulnerability dimensions: floating population (PC-FP), livelihood instability (PC-LI), low education (PC-EL), and lack of action capabilities (PC-AC). The PCA-MGWR model (R² = 0.713) significantly outperforms traditional OLS and GWR models in explanatory power. Crucially, results reveal a “Paradox of Wealth Exposure” along the developed Yangtze River Economic Belt, where affluent regions exhibit higher flood exposure, challenging the conventional poverty-driven vulnerability narrative. Furthermore, the driving mechanisms exhibit significant spatial non-stationarity: (1) In eastern megacities, the floating population shows a statistical “risk aversion” effect driven by housing market sorting, yet remains vulnerable due to “institutional segregation” in emergency management; (2) In old industrial bases and eastern plains, an “in-situ aging” phenomenon has created a “spatial lock-in,” trapping elderly populations in high-risk zones. These findings suggest that “mobility” and “aging” have emerged as critical drivers of vulnerability in China alongside economic factors. Consequently, we advocate for spatially differentiated governance strategies, shifting from “one-size-fits-all” defenses to adaptive policies that address specific regional socio-hydrological traps.