Urban regeneration is pivotal to sustainable development, requiring innovative strategies that align social dynamics with spatial configurations. Traditional paradigms increasingly fail to tackle systemic challenges—neighborhood alienation, social fragmentation, and resource inequality—due to their inability to integrate human-centered spatial governance. This study addresses these shortcomings with a novel multidimensional framework that merges social perception (life satisfaction) analytics with spatial quality (GIS-based) assessment. At its core, we utilize geospatial and machine learning models, deploying an ensemble of Gradient Boosted Decision Trees (GBDT), Random Forest (RF), and multiscale geographically weighted regression (MGWR) to decode nonlinear socio-spatial interactions within Suzhou’s community environmental matrix. Our findings reveal critical intersections where residential density thresholds interact with commercial accessibility patterns and transport network configurations. Notably, we highlight the scale-dependent influence of educational proximity and healthcare distribution on community satisfaction, challenging conventional planning doctrines that rely on static buffer-zone models. Through rigorous spatial econometric modeling, this research uncovers three transformative insights: (1) Urban environment exerts a dominant influence on life satisfaction, accounting for 52.61% of the variance. Air quality emerges as a critical determinant, while factors such as proximity to educational institutions, healthcare facilities, and public landmarks exhibit nonlinear effects across spatial scales. (2) Housing price growth in Suzhou displays significant spatial clustering, with a Moran’s I of 0.130. Green space coverage positively correlates with price appreciation (β = 21.6919 ***), whereas floor area ratio exerts a negative impact (β = −4.1197 ***), highlighting the trade-offs between density and property value. (3) The MGWR model outperforms OLS in explaining housing price dynamics, achieving an R² of 0.5564 and an AICc of 11,601.1674. This suggests that MGWR captures 55.64% of pre- and post-pandemic price variations while better reflecting spatial heterogeneity. By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. These findings recalibrate urban regeneration paradigms, demonstrating that data-driven socio-spatial integration is not a theoretical aspiration but an achievable governance reality.

Rapid urban expansion has transformed rural land use, generating multiple sustainability challenges, including environmental pollution, habitat degradation, and crises of community identity. However, existing studies lack a quantitative framework that captures the multidimensional coupling among economic vitality, ecological integrity, and social wellbeing during urban–rural transitions, limiting guidance for integrating urban and rural landscapes. To respond to this issue, this study introduces an integrated Urban-Rural Sustainability Index (URSI) that encompasses economic, ecological, and social dimensions. Additionally, it develops a methodological framework that combines a Random Effects Model (REM), an Extreme Gradient Boosting (XGBoost) model, and Shapley Additive Explanations (SHAP) to identify both linear and non-linear relationships, interaction effects, and threshold conditions, while ensuring predictive accuracy and interpretability. Using geospatial data from Suzhou, China, this study systematically examines the spatial-temporal dynamics of urban-rural integration and associated sustainable outcomes between 2012 and 2024. Three key findings were revealed: (1) Urban-Rural Sustainability Index (URSI)-based conflict exhibits significant spatial clustering, evolving from a monocentric hotspot into a polycentric, multi-nodal pattern as urban expansion proceeds; (2) REM results reveal heterogeneous effects across land use types: built-up area is positively associated with conflict, whereas farmland, water, and forest are negatively associated, indicating a buffering role of ecological and agricultural stocks; (3) XGBoost–SHAP analysis enhances these findings by identifying segmented effects and indicative operating ranges, including an inflection around a built-up area about 60%, diminishing marginal mitigation from water coverage around 15%, and conflict alleviation when contiguous farmland is maintained near 20%. Building upon these empirical insights, this study proposes targeted strategies to enhance urban-rural sustainability, including spatially differentiated land-use management, prioritising blue-green infrastructure in urban planning and balancing sustainability pressures within urban areas. The proposed framework and analytical methodology provide a transferable foundation for future research and practical urban-rural sustainability planning across diverse geographical contexts.

The journal retracts the article titled “Deciphering Socio-Spatial Integration Governance of Community Regeneration: A Multi-Dimensional Evaluation Using GBDT and MGWR to Address Non-Linear Dynamics and Spatial Heterogeneity in Life Satisfaction and Spatial Quality”