Yingtao Qi

Traditional village resilience is shaped by interactions among ecological foundations, cultural continuity, and social functioning, yet cultural resilience and the coordination and constraints among internal subsystems remain insufficiently understood. This study evaluated 16 sampled traditional villages in the northern foothills of the Qinling Mountains, Xi’an, China. Based on the theory of cultural ecology and the pressure–state–response framework, an ecological–cultural–social resilience evaluation system was developed. The coupling coordination degree model, obstacle degree model, and two-step cluster analysis were applied to examine overall resilience, subsystem coordination, and obstacle patterns. Results reveal differences in overall resilience among villages and clear asynchronous characteristics among ecological, cultural, and social subsystems, with cultural resilience showing the greatest inter-village variation. Ten villages fall into the barely coordinated level, five reach the primary coordination level, and one achieves intermediate coordination. Under the combined weighting scheme, the cultural subsystem is identified as the dominant obstacle dimension in 13 villages, while three exploratory obstacle patterns are observed based on subsystem constraint combinations. Sensitivity analysis indicates that overall resilience rankings are generally stable, whereas dominant obstacle identification shows some sensitivity to weighting schemes. These findings support differentiated conservation and resilience enhancement by integrating overall resilience, subsystem coordination, and constraint structures.

To address the coordination between social and spatial relationships within villages in the loess gully region, this study developed a joint Geospatial–Social Network analytical approach. Taking Caojian Village in Zizhou County, Shaanxi Province as the study case, geospatial and social network models were constructed, respectively, with time cost and social interaction frequency as measurement indicators. The two network models were analyzed using cohesive subgroups and node centrality, respectively. Correlation analysis revealed the dominant role of spatial relationships in shaping social relationships, as well as the reinforcing effect of kinship on cross-geographical connections. Furthermore, the joint analysis of the two networks was used to identify village spatial hierarchies and core areas. On this basis, targeted strategies were proposed for public space allocation, public service facility layout, and site selection for relocated dwellings. Ultimately, this research aims to provide quantitative analysis tools and decision-making support for optimizing the spatial structure of villages in the loess gully region.

Clarifying the mechanism through which community-built environment types influence the supply–demand adaptability of service facilities can promote the effectiveness of community service facility provision in the context of urban renewal. Existing studies have focused on facility quantity, service radius, or accessibility, while few have integrated community-built environment classification with supply–demand adaptability assessment. As a result, the differentiated mechanisms of facility mismatch under different spatial structures remain insufficiently revealed. Taking Xi’an as an example, this research takes 15-minute life circles as basic units and integrates macro- and micro-scale built environment indicators to identify community types. Then, it assesses the supply–demand adaptability of service facilities from four dimensions, including completeness, accessibility, diversity, and equity. The results show that supply–demand adaptability of community facilities is jointly influenced by factors such as road networks, residential complex scale, and walking environment quality. Facility provision adaptability in Xi’an generally shows a declining spatial pattern from the urban core to peripheral areas, while different indicators reveal different types of mismatches. Facility layout patterns act as an intermediate spatial mechanism linking community morphology with supply–demand adaptability outcomes. Their service performance is conditioned by road network permeability, block size, and residential enclosure. Based on these findings, this study proposes targeted optimization strategies for different community types, providing a reference for diagnosing supply–demand conflicts within 15-minute life circles.

Construction period is a core item of information for the heritage conservation of traditional dwellings, supporting building archive compilation, historical character zoning, conservation grading, and restoration strategy formulation in traditional villages. However, accurately determining the construction period of traditional dwellings remains difficult. Existing methods rely heavily on expert judgment and field surveys and are limited by low efficiency, subjectivity, and weak scalability. This study focuses on surface-built traditional dwellings in the Guanzhong region. UAV image acquisition and field photography were used to collect dwelling images, while resident interviews were used to verify construction-period information. Architectural components with chronological relevance were then annotated. On this basis, a construction-period classification model was developed using a support vector machine (SVM). Model performance was evaluated through classifier comparison, RBF parameter optimization, and repeated dwelling-level test-set evaluation. The optimized SVM model achieved an average classification accuracy of 81.9%. Qing-dynasty and post-1980 dwellings showed the highest and nearly identical classification performance, with F1-scores of 87.2% and 87.0%, respectively. Republican-period dwellings also achieved relatively good performance, with an F1-score of 82.9%, whereas dwellings built between 1949 and 1979 showed the lowest performance, with an F1-score of 75.4%. These findings indicate that construction-period classification based on architectural component features and SVM can support preliminary screening of dwelling construction periods, providing auxiliary technical support for digital archiving, period-based classification, character control, conservation planning, and targeted restoration in traditional villages.

Based on the landscape perception theory and human factors methods, this study adopted eye-tracking and multimodal physiological monitoring to explore the effects of age-friendly rehabilitative landscapes on stress recovery in older adults. A total of 44 healthy older adults were recruited. Fourteen landscape scenes from five types of parks in Xi’an were selected to collect physiological data under visual-only and audio-visual combined conditions. The results showed three key findings. First, the main effect of visual environment was significant ( p < 0.05). Landscapes with moderate plant diversity and static water features produced the optimal stress recovery effects. Second, the audio-visual interaction effect was significant ( p < 0.05). Auditory stimuli did not universally promote stress recovery, and their effects were regulated by visual landscape types. Third, eye movement data indicated excessive audio-visual stimuli might reduce recovery efficiency. Open spaces and static water features showed strong anti-interference capabilities. Sound cues effectively drew older adults’ visual attention to water landscapes. This study suggests prioritizing landscapes with moderate plant complexity and static water features. The sound volume of dynamic water scenes should be controlled, and open spaces can be designed to reduce cognitive load. The findings deepen the understanding of how audio-visual landscapes affect stress recovery in older adults, and provide empirical evidence for the design and optimization of age-friendly landscapes in urban parks and community green spaces.

Urban green spaces (UGSs) are considered crucial for enhancing older adults’ subjective well-being. However, limited studies have explored the synergistic effects of UGS quality and quantity on satisfaction across green spaces, residential environments, and life domains, making it challenging to uncover the multifaceted sustainable benefits of UGSs on older adults’ subjective well-being. This study drew on multi-source data and place attachment theory to depict neighborhood-accessible UGS quantity (provision, accessibility, and visibility) and quality (cognition, behavior, and affect). Through the geographical visualization of bivariate SHapley Additive exPlanations (SHAP) interaction values extracted from the trained eXtreme Gradient Boosting (XGBoost) model, and the comparison of bivariate SHAP maps with univariate SHAP maps, the study revealed the nonlinear geographic associations between UGS quantity and quality and three types of satisfaction. The results showed that when UGS quantity and quality coexisted, variations in the impact of quantity on older adults’ satisfaction were associated with quality differences. The gain effect of quality on quantity was more significant in areas with limited green space within a 500 m buffer zone. UGSs made a direct contribution to green space satisfaction, while their indirect association with life satisfaction surpassed that of residential satisfaction due to their provision of emotional qualities. This study calls for neighborhood green planning aimed at improving older adults’ subjective well-being, which should shift focus from quantity to quality and balance the relationship between quantity and quality based on regional characteristics.

As a result of China’s rapid urbanization, new urban districts are characterized by a superblock development paradigm that contrasts sharply with core urban areas, where service facilities remain largely congruent with the population distribution. This planning approach has resulted in a pronounced spatial mismatch, with an intensive concentration of public service facilities within commercial cores and a critical lack of facilities proximate to high-density residential clusters. Within the framework of the 15 min community life circle policy, evaluating and optimizing these configurations is imperative for mitigating such structural imbalances. Using Xi’an’s Qujiang New District as a representative empirical case, this study integrates Point of Interest (POI) geospatial data with 330 resident behavioral questionnaires to assess facility distribution and utilization patterns. The findings reveal a distinct spatial pattern of core–periphery polarization, which is significantly influenced by cultural landscapes and commercial land values. Furthermore, the utilization patterns differ markedly across age groups. The reliance of young and middle-aged groups on digital life circles should be viewed not only as a lifestyle preference but also as an adaptation to mitigate physical facility deficits. While digital services compensate for physical facility shortages, they mask the actual lack of community spaces. This further disadvantages older adults, who still rely heavily on walking to access daily services. Addressing the unique characteristics of new urban districts, this study proposes a synergistic physical–digital dual-tier system in which physical infrastructure safeguards the equity baseline, while digital platforms enhance operational efficiency, providing a scientific basis for constructing age-friendly communities.

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based...

Over the past two decades, Building-Integrated Photovoltaics (BIPV) has become a core technology in the green building sector, driven by global carbon-neutrality goals and the growing demand for sustainable design. This review adopts a scalability-oriented perspective and systematically examines 82 peer-reviewed articles published between 2001 and 2025. The results indicate that existing research is dominated by studies on electrical and thermal performance, with East Asia and Europe—particularly China, Japan, and Germany—emerging as the most active regions. This dominance matters for scalability because real projects must satisfy comfort, compliance, buildability, and operation/maintenance constraints alongside energy yield; limited evidence in these dimensions increases delivery risk when transferring solutions across regions and building types. Accordingly, we interpret the observed distribution as an evidence-maturity pattern: performance gains are increasingly well characterized, whereas deployment-relevant uncertainties (e.g., boundary-condition sensitivity and validation depth) remain less consistently reported. Multidimensional integration of thermal, optical, and electrical functions is gaining momentum; however, user-centered performance dimensions remain underexplored. Simulation-based approaches still prevail, whereas large-scale empirical studies are limited. The review also reveals extensive interdisciplinary collaboration but also identifies a notable lack of architectural perspectives. Using Biblioshiny, this study maps co-authorship networks and research structures. Based on the evidence, we propose future research directions to enhance the practical scalability of BIPV, including strengthening interdisciplinary integration, expanding empirical validation, and developing product-level design strategies.