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.

Urban renewal initiatives grapple with a tripartite crisis in the community: resource allocation disparities, youth exclusion in participatory planning, and socio-spatial service degradation, all depressing life satisfaction among young residents. Despite extensive research on urban renewal and socio-spatial integration, the non-linear dynamics between these factors and life satisfaction, particularly in youth-oriented community renewal, remain underexplored, hindering effective decision-making. Furthermore, limited attention has been given to the combination of subjective perceptions and objective spatial metrics in assessing socio-spatial factors. This necessitates a move beyond simplistic analyses of singular urban amenities like greenery to a more comprehensive understanding of integrated socio-spatial dynamics. This study bridges these gaps by employing Gradient Boosting Decision Trees (GBDT) and eXtreme Gradient Boosting (XGBoost) models for non-linear exploration and Ordinary Least Squares (OLS) regression for subjective perception and objective spatial comparison, to explore how social and spatial factors influence youth life satisfaction in Suzhou, China. The results indicate that both subjective and objective factors significantly influence youth life satisfaction. GBDT prioritized Social Interaction and Commuting Environment as paramount. Subjective-objective comparative results revealed consistent positive linear impacts from both perceived and objective environmental and facilities accessibility factors. Crucially, XGBoost results indicate that Educational facilities density’s contribution value sharply rises from ~-0.005 to ~0.02 as density increases from ~-0.5 to ~0.5, and Medical facilities density’s contribution value turns negative at higher densities. The findings not only advance socio-spatial integration theory but also contribute to the development of youth-oriented urban renewal strategies and governance mechanisms, aiming to enhance youth life satisfaction by holistically addressing the complex interplay of diverse socio-spatial elements, moving beyond a singular focus on green spaces to achieve balanced environmental quality and social equity.

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.

Community regeneration plays a pivotal role in creating human-centered spaces by transforming spatial configurations, enhancing multifunctional uses, and optimizing designs that promote sustainability and vibrancy. However, the influence of such regeneration on spatial vitality—particularly its spatial heterogeneity and nonlinear effects—remains insufficiently explored. This study presents a comprehensive framework that combines the Difference-in-Differences (DID) method with multiple socio-spatial correlated factors, including place agglomeration, individual agglomeration, and social perception, offering a systematic assessment of urban vitality and evaluating the impact of regeneration interventions. By leveraging street-level imagery to capture environmental changes pre- and post-regeneration, this research applies Gradient Boosting Decision Tree Regression (GBDT) to uncover nonlinear built environment dynamics affecting urban vitality. Empirical analysis from six districts in Suzhou reveals the following: (1) A pronounced increase in urban vitality is seen in core areas, while peripheral districts exhibit more moderate improvements, highlighting spatially uneven regeneration outcomes. (2) In historically significant areas such as Wuzhong, limited vitality gains underscore the complex interplay among historical preservation, spatial configurations, and urban development trajectories. (3) Furthermore, environmental transformations, including variations in sky visibility, nonprivate vehicles, architectural elements, and the introduction of glass-wall structures, exhibit nonlinear impacts with distinct threshold effects. This study advances the discourse on sustainable urban regeneration by proposing context-sensitive, data-driven assessment tools that reconcile heritage conservation with contemporary urban regeneration goals. It underscores the need for integrated, adaptive regeneration strategies that align with local conditions, historical contexts, and urban development trajectories, informing policies that promote green, inclusive, and digitally transformed cities.

Higher education faces persistent tension between fostering student satisfaction and enhancing measurable learning performance. While pedagogical research has long debated the role of teaching methods in shaping thesis writing outcomes, empirical studies in urban planning and design (UPD) education remain fragmented. Existing scholarship often treats satisfaction as a secondary byproduct of instruction rather than as a central determinant of performance. This gap obscures the potential of "UPD education" as both a pedagogical goal and a performance-enhancing mechanism. This study explores how AI integration and thesis supervision shape students' satisfaction within higher education. Satisfaction is decomposed into two dimensions: thesis supervision educational satisfaction (EY1) and overall educational satisfaction (EY2). Using survey data from undergraduates from the UPD department, we integrate ordinary least squares (OLS) regression, random forest (RF) models, mediation analysis, and interpretable eXtreme Gradient Boosting (XGBoost) with SHAP values. Results show: (1) EY1 is primarily driven by efficiency gains-each unit increase in AI application proficiency raises process satisfaction by 0.519 units-while EY2 rises by 0.355 units for every unit increase in schedule rationality. (2) EY1 acts as a full mediator, transmitting AI use and personal factors' positive effects onto EY2. (3) Teaching arrangement quality exerts a steep threshold effect: once it surpasses a critical point, overall satisfaction surges sharply. Theoretically, the study reframes UPD education as a measurable convergence of satisfaction and performance, rather than a diffuse cultural mission. Practically, it provides a framework for recalibrating curricula, striking a balance between affective engagement and academic rigor. By pinpointing which pedagogical factors shape both dimensions, the findings provide actionable strategies for universities seeking to optimize pedagogical workflows, elevate teaching quality, and advance the integrative aims of thesis writing.