Rapid urbanization has accelerated the transformation of community dynamics, highlighting the critical need to understand the interplay between subjective perceptions and objective built environments in shaping life satisfaction for sustainable urban development. Existing studies predominantly focus on linear relationships between isolated factors, neglecting spatial heterogeneity and nonlinear dynamics, which limits the ability to address localized urban challenges. This study addresses these gaps by utilizing multi-scale geographically weighted regression (MGWR) to assess the spatial nonstationarity of subject perceptions and built environment factors while employing gradient-boosting decision trees (GBDT) to capture their nonlinear relationships and incorporating eXtreme Gradient Boosting (XGBoost) to improve predictive accuracy. Using geospatial data (POIs, social media data) and survey responses in Suzhou, China, the findings reveal that (1) proximity to business facilities (β = 0.41) and educational resources (β = 0.32) strongly correlate with satisfaction, while landscape quality shows contradictory effects between central (β = 0.12) and peripheral zones (β = −0.09). (2) XGBoost further quantifies predictive disparities: subjective factors like property service satisfaction (R² = 0.64, MAPE = 3.72) outperform objective metrics (e.g., dining facilities, R² = 0.36), yet objective housing prices demonstrate greater stability (MAPE = 3.11 vs. subjective MAPE = 6.89). (3) Nonlinear thresholds are identified for household income and green space coverage (>15%, saturation effects). These findings expose critical mismatches—residents prioritize localized services over citywide economic metrics, while objective amenities like healthcare accessibility (threshold = 1 km) require spatial recalibration. By bridging spatial nonstationarity (MGWR) and nonlinearity (XGBoost), this study advances a dual-path framework for adaptive urban governance, the community-level prioritization of high-impact subjective factors (e.g., service quality), and data-driven spatial planning informed by nonlinear thresholds (e.g., facility density). The results offer actionable pathways to align smart urban development with socio-spatial equity, emphasizing the need for hyperlocal, perception-sensitive regeneration strategies.

Amid accelerating globalization and urbanization, cities worldwide grapple with an interconnected crisis marked by the erosion of cultural heritage, homogenization of spatial identities, and fragme...

In smart home projects, building data management at the design stage increasingly relies on digital-twin assets delivered via game engines. Without a clear governance workflow, however, these practices tend to generate non-standard building data on the consumption side, causing broken data chains and increasing construction and management risks. To address this problem, this study proposes a traceability-oriented governance workflow that strengthens IfcGUID compliance and automatically detects and converts inconsistent digital-twin assets into IFC-compliant, auditable data, thereby reducing data chain breakage and improving cross-system traceability in building data management. The workflow uses IfcGUID as a cross-system primary key and is evaluated in a virtual smart home project through a pre-test–repair–post-test experiment at the design stage. We examine four indicators of IfcGUID quality—completeness, validity, uniqueness, and stability—together with a bridge recognition rate that reflects game engine interoperability on the consumption side. The results show that all four IfcGUID indicators converge towards 1 after applying the workflow, and the bridge recognition rate approaches 100%, indicating that the risk of data chain breakage, measured on an IFC basis, is substantially reduced. Within existing toolchains, this workflow provides design teams, visualization teams, clients, and auditors with a low-cost and reproducible path for standardizing design-stage digital-twin assets and establishing a traceable, auditable baseline for cross-system interoperability and lifecycle building data management and data reuse.

Against the backdrop of China's rapid urbanization and modernization, rural public spaces are undergoing significant changes. This study investigates the cultural vitality of rural public spaces in Fujian Province, focusing on four representative types: activity squares, village parks, village entrances, and ancestral halls. Using a combination of questionnaire surveys and image semantic segmentation, the research develops a comprehensive evaluation framework to assess these spaces across four dimensions: field attribute elements, functional attribute elements,social attribute elements and cultural attribute elements.The findings reveal that activity squares and village parks have high potential for enhancing cultural vitality, while village entrances and ancestral halls require significant improvements. The study proposes specific strategies for optimizing spatial layouts, integrating cultural elements, and promoting community participation to revitalize rural public spaces. Future research should expand the scope of data collection and explore more diverse space types to further enrich the understanding of rural public space creation and cultural vitality enhancement.

Introduction: Extreme heat events intensify health risks among vulnerable populations, raising concerns regarding climate justice. However, most existing assessments remain at the citywide scale and seldom examine inequities across different types of outdoor cooling spaces. Methods: This study integrates the Exposure–Sensitivity–Adaptive Capacity (VSD) framework with the dimensions of distributive, recognition, and procedural justice to construct a climate justice assessment model. Outdoor cooling spaces were classified into linear and areal forms. Using Gulou District in Fuzhou as the case study, we developed a multi-source indicator system based on remote sensing imagery, street-view data, points of interest, and demographic statistics. The entropy weight method was used to determine indicator weights, and K-means clustering was applied to identify climate injustice space types. Results: The findings show that 37.18% of linear cooling spaces and 44.45% of areal cooling spaces face significant climate injustice risks. High-risk areas are concentrated in dense built-up zones, aging neighborhoods, and peripheral areas with limited public services. Cluster analysis identified three distinct deficit categories: distributional justice deficit, recognitional justice deficit, and systemic justice deficit, reflecting overlapping vulnerabilities and uneven adaptive capacities. Discussion: These results highlight the need for differentiated interventions to reduce spatial inequities. Key actions include enhancing shading along traffic corridors, improving service accessibility in aging neighborhoods, and strengthening adaptive resources in systemic deficit areas. The proposed framework offers an evidence base for equity-oriented urban governance and supports resilient urban planning and public health strategies under extreme heat.

The journal retracts the article titled “Unraveling Spatial Nonstationary and Nonlinear Dynamics in Life Satisfaction: Integrating Geospatial Analysis of Community Built Environment and Resident Perception via MGWR, GBDT, and XGBoost”