The benefits of urban green spaces (UGSs) to public health arise not only from the green spaces themselves but also from the surrounding environment (SE). However, leveraging multimodal social media data to comprehensively assess the associations between environmental features and sentiment toward UGSs remains challenging. This study integrates SHapley Additive exPlanations and Geographically Weighted Regression, using 59,880 social media text entries and 49,501 images from 280 UGSs in Beijing to reveal nonlinear associations, synergies, and spatial heterogeneities across different scales of UGSs. The results show that: (1) SE features play a more important role than UGS attributes in determining UGS sentiment, with building coverage ratio, gross domestic product, and population density contributing the most. (2) All environmental variables exhibit nonlinear, interactive effects, and geographic heterogeneity on UGS sentiment. (3) Within SE features, accessibility is positively associated with sentiment in community green spaces but negative in non-community green spaces, whereas floor area ratio is positive in both, revealing scale-dependent heterogeneity across UGSs and localized effects within their effective ranges. Our methodology combines photo- and text-based sentiment analysis, offering a more accurate and efficient approach to capturing public insights and thereby enabling more precise UGS planning decisions.

Art therapy serves as a crucial intervention modality for children with autism spectrum disorder (ASD), demonstrating unique value in emotional expression, sensory integration, and social communication. However, current practice presents critical challenges, including the disconnect between design expertise and clinical needs, unclear mechanisms of environmental factors’ impact on therapeutic outcomes, and insufficient evidence-based support for technology integration. Purpose: This study aimed to construct an evidence-based theoretical framework for art therapy environment design for children with autism, clarifying the relationship between environmental design elements and therapeutic effectiveness. Methodology: Based on the Web of Science database, this study employed a dual-track approach comprising bibliometric analysis and micro-qualitative content analysis to systematically examine the knowledge structure and developmental trends. Research hotspots were identified through keyword co-occurrence network analysis using CiteSpace, while 24 representative design cases were analyzed to gain insights into design concepts, emerging technologies, and implementation principles. Key Findings: Through keyword network visualization analysis, this study identified ten primary research clusters that were systematically categorized into four core design elements: sensory feedback design, behavioral guidance design, emotional resonance design, and therapeutic support design. A responsive therapeutic environment conceptual framework was proposed, encompassing four interconnected components based on the ABC model from positive psychology: emotional, sensory, environmental, and behavioral dimensions. Evidence-based design principles were established emphasizing child-centeredness, the promotion of multisensory expression, the achievement of dynamic feedback, and appropriate technology integration. Research Contribution: This research establishes theoretical connections between environmental design elements and art therapy effectiveness, providing a systematic design guidance framework for interdisciplinary teams, including environmental designers, clinical practitioners, technology developers, and healthcare administrators. The framework positions technology as a therapeutic mediator rather than a driver, ensuring technological integration supports rather than interferes with children’s natural creative impulses. This contributes to creating more effective environmental spaces for art therapy activities for children with autism while aligning with SDG3 goals for promoting mental health and reducing inequalities in therapeutic access.

The spatial distribution mechanism of industrial agglomeration has long been a central topic in urban economic geography. With the increasing availability of street view imagery and built environment data, effectively integrating multi-source spatial information to identify key drivers of firm clustering has become a pressing research challenge. Taking Shanghai as a case study, this paper constructs a street-level Built Environment (BE) database and proposes an interpretable spatial analysis framework that integrates SHapley Additive exPlanations with Multi-Scale Geographically Weighted Regression. The findings reveal that: (1) building morphology, streetscape characteristics, and perceived greenness significantly influence firm agglomeration, exhibiting nonlinear threshold effects; (2) spatial heterogeneity is evident in the underlying mechanisms, with localized trade-offs between morphological and perceptual factors; and (3) BE features are as important as macroeconomic factors in shaping agglomeration patterns, with notable interaction effects across space, while streetscape perception variables play a relatively secondary role. This study advances the understanding of how micro-scale built environments shape industrial spatial structures and offers both theoretical and empirical support for optimizing urban industrial layouts and promoting high-quality regional economic development.

As urbanization intensifies, the challenge of preserving industrial heritage while fostering authentic intergenerational connections has become increasingly salient. This study investigates how artificial intelligence (AI) and augmented reality (AR) technologies can be applied to enhance authenticity and promote both hedonic and eudaimonic well-being within the context of heritage tourism. Using a facility in Shanghai as a case study, we propose a cultural co-creation mechanism that transforms implicit intergenerational memories into shared cultural resources through digital interaction. The study first evaluates public awareness and participation needs in the context of industrial heritage revitalization. In response, we design an immersive platform that enables visitors of different generations to co-create meaning through historical scene reconstruction, multisensory engagement, and collaborative storytelling. A novel five-sense encoding strategy is introduced to reinterpret the enclosed spatial characteristics of industrial architecture as an experiential form of storytelling. This process fosters a deeper connection to place, contributing to authenticity and well-being. Prototype testing results suggest that this AI-AR-enabled co-creation system supports meaningful cultural attachment, improves authenticity, and facilitates the sustainable transmission of heritage. This research provides a replicable model for integrating digital technology, community participation, and authenticity in the well-being-oriented revitalization of industrial heritage sites.