The Chinese urban regeneration movement underscores a “people-oriented” paradigm, aimed at addressing urban challenges stemming from rapid prior urbanization, while striving for high-quality and sustainable urban development. At the community level, fostering quality through a socially sustainable perspective (SSP) is a pivotal strategy for people-oriented urban regeneration. Nonetheless, explorations of community quality assessments grounded in an SSP have been notably scarce in recent scholarly discourse. This study pioneers a multidimensional quantitative model (MQM) for gauging community quality, leveraging diverse geospatial data sources from the SSP framework. The MQM introduces an evaluative framework with “Patency, Convenience, Comfort, and Safety” as primary indicators, integrating multi-sourced data encompassing the area of interest (AOI), Point of Interest (POI), Weibo check-ins, and Dianping data. The model’s efficacy is demonstrated through a case study in the Gusu district, Suzhou. Furthermore, semantic analysis of the Gusu district’s street view photos validates the MQM results. Our findings reveal the following: (1) AI-based semantic analysis accurately verifies the validity of MQM-generated community quality measurements, establishing its robust applicability with multi-sourced geospatial data; (2) the community quality distribution in Gusu district is notably correlated with the urban fabric, exhibiting lower quality within the ancient town area and higher quality outside it; and (3) communities of varying quality coexist spatially, with high- and low-quality communities overlapping in the same regions. This research pioneers a systematic, holistic methodology for quantitatively measuring community quality, laying the groundwork for informed urban regeneration policies, planning, and place making. The MQM, fortified by multi-sourced geospatial data and AI-based semantic analysis, offers a rigorous foundation for assessing community quality, thereby guiding socially sustainable regeneration initiatives and decision making at the community scale.

Accelerated urbanization has led to regional disruptions and exacerbated imbalances in spatial quality, social cohesion, and inequalities. Urban regeneration, as a mitigating strategy for these disruptions, faces significant social challenges, particularly at the community scale. This study addresses the existing research gap by comprehensively reviewing community regeneration (CR) from a socially sustainable perspective (SSP). Utilizing VOSviewer software, we synthesize and categorize relevant research trends and methods spanning from 2006 to 2023, retrieving 213 coded articles among 5002 relevant documents from Web of Science bibliometric datasets. The study explores the implementation trajectory of CR, considering novel scenario demands, emerging technologies, and new development paradigms and approaches. It delves into human-centric approaches to enhance the quality of life, precision, and diversification of community engagement and cultivate a sense of community equity and belonging. Moreover, the findings highlight densification as a synergistic and adaptive strategy for current regeneration actions. This scientometric review leverages new tools and innovative approaches for regeneration policy and planning decision-making, ultimately contributing to the improvement of livability. The study provides valuable insights into the challenges and opportunities associated with socially sustainable CR, offering a foundation for future research, and guiding practical urban planning and design interventions.

The 2023 SDGs report underscores the prolonged disruption of COVID-19 on community living spaces, infrastructure, education, and income equality, exacerbating social and spatial inequality. Against the backdrop of the dual impact of significant events and the emergence of digital technologies, a coherent research trajectory is essential for characterizing social–spatial equity and understanding its influential factors within the urban planning discipline. While prior research emphasized spatial dimensions and mitigated spatial differentiation to ensure urban equity, the complexity of these interconnections necessitates a more comprehensive approach. This study adopts a holistic perspective, focusing on the “social–spatial” dynamics, utilizing social perception (sentiment maps) and spatial differentiation (housing prices index) pre- and post-pandemic to elucidate the interconnected and interactive nature of uneven development at the urban scale. It employs a multi-dimensional methodological framework integrating morphology analysis of housing conditions, GIS analysis of urban amenities, sentiment semantic analysis of public opinion, and multiscale geographically weighted regression (MGWR) analysis of correlation influential factors. Using Suzhou, China, as a pilot study, this research demonstrates how these integrated methods complement each other, exploring how community conditions and resource distribution collectively bolster resilience, thereby maintaining social–spatial equity amidst pandemic disruptions. The findings reveal that uneven resource distribution exacerbates post-pandemic social stratification and spatial differentiation. The proximity of well-maintained ecological environments, such as parks or scenic landmarks, generally exhibits consistency and positive effects on “social–spatial” measurement. Simultaneously, various spatial elements influencing housing prices and social perception show geographic heterogeneity, particularly in areas farther from the central regions of Xiangcheng and Wujiang districts. This study uncovers a bilateral mechanism between social perception and spatial differentiation, aiming to delve into the interdependent relationship between social–spatial equity and built environmental factors. Furthermore, it aspires to provide meaningful references and recommendations for urban planning and regeneration policy formulation in the digital era to sustain social–spatial equity.

Sustainable urban forms (SUF) guide spatial creation, significantly revitalise the development of traditional settlements, and are an essential theoretical support for urban design. At the same time, the emergence of quantitative spatial analysis technology further promotes the visualised evaluation of the performance of spatial vitality in urban design. However, current research rarely studies the spatial vitality of traditional settlements with quantitative spatial analysis from the SUF perspective. Therefore, this research takes Anren Ancient Town in Chengdu, Western China, as an example to propose a design based on sustainable urban form theory to raise local spatial vitality. Then, it introduces the vitality evaluation system based on the urban form index (UFI) with three measurement methods: Space Syntax, Spacemate, and MXI, and conducts a comparative spatial vitality evaluation of Anren Ancient Town’s status quo to explain the process of how the design scheme came about. The results found that urban design proposals based on the principles of compactness, mixed land use and diversity in SUF design guidelines can effectively improve the vitality of traditional settlements. The high vitality of an urban settlement could be achieved by combining SUF-based design guidelines and UFI-based evaluation systems. The spatial vitality evaluation system based on the SUF could assist and optimise decision-making in design and act as a paradigm for urban design or urban regeneration in traditional towns.

The intersection of environmental conservation and urban development has garnered global attention. This study aims to contribute to the theoretical foundation and policy recommendations for the collaborative governance of territory and the natural environment in metropolitan agglomerations. Employing a comprehensive Morphological Spatial Pattern Analysis (MSPA) approach, this research analyses and overlays various indicators, constructing an integrated evaluation system based on “natural resource management and conservation, land use, and ecological network assessment”. Our findings reveal that: (1) The overall collaborative degree between the spatial configuration and natural environment in the Ecological Green Integration Development Demonstration Zone (EGIDDZ) is relatively high and still needs improvement at the micro level. Notably, regions characterised by elevated synergy levels exhibit considerable spatial overlap with ecological green cores outlined in territorial planning. (2) The selection and distribution of land use patterns are pivotal factors influencing collaborative levels, with multiple land use types favouring enhanced collaboration. By exploring the interplay between metropolitan territory and the natural environment using MSPA, this study seeks to provide holistic evaluation methodologies and governance insights for advancing sustainable urban planning and development.

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.