Street space plays a crucial role in human activity and social life, forming an essential component of a livable and sustainable built environment. Consequently, its quality has garnered significant attention from researchers, designers, and policymakers who aim to achieve precise assessments of street infrastructure and conditions. This study presents a multi-dimensional framework for evaluating street space, considering factors such as access frequency, environmental quality, and amenity richness. By utilizing city-level path planning data, street view imagery, point of interest data, and social media check-in data, this framework assesses each street and assigns scores across these dimensions. These scores facilitate a human-centered analysis of the disparities in street usage and quality. The aggregation of results by administrative regions supports effective policy formulation and implementation. Application of this framework in Xi’an, China, reveals that only 6.95% of frequently visited streets exhibit high environmental quality and functional richness. This study underscores the potential of leveraging public data for detailed street space assessments to inform urban renewal policies.

Rural settlements are the fundamental socio-economic units of China’s countryside. In line with national strategies that emphasize place-based and category-specific pathways for rural revitalization, accurate classification of rural settlements is essential for differentiated planning and policy delivery. However, given the sheer number of settlements, manual classification is time-consuming and resource-intensive, limiting scalability. This study proposes an AI-driven, multi-model framework to automate rural settlement classification with high stability and accuracy. First, informed by a rigorous literature review, we construct a multidimensional indicator system that integrates natural conditions, socio-economic attributes, and land-use factors to capture spatial and functional characteristics at the settlement scale. Using Gaoqing County (Shandong Province) as the study area, we collect and curate survey data and apply outlier detection for preprocessing. We then benchmark multiple machine learning models and find that algorithms with native handling of missing values perform markedly better—a critical advantage given the prevalence of missingness in survey-based datasets. Finally, we assemble the three best-performing models—LightGBM, CatBoost, and XGBoost—into a weighted-voting ensemble, achieving an overall classification accuracy of approximately 88%. The results demonstrate that the refined indicator system, coupled with a multi-model ensemble, substantially improves both accuracy and robustness. This work provides a methodological foundation and empirical evidence to support differentiated planning and targeted rural revitalization at the settlement level, offering a scalable blueprint for broader regional and national implementation.

Against the backdrop of global population aging, the impact of urban built environments on the physical and mental health of older adults is receiving increasing attention. As the world’s largest developing nation, China, is simultaneously undergoing rapid urbanization and significant demographic aging. This dual trend makes it particularly imperative to investigate the relationship between the built environment and senior health. This study employs a meta-analysis methodology to quantitatively evaluate the relationship between urban built environment characteristics and physical and mental rehabilitation among older adults. Empirical studies were systematically screened from the CNKI and Web of Science databases using weighted Z-scores. Methodological quality, sample size, and heterogeneity were assessed to ensure the robustness of the analysis. Thirteen environmental indicators were categorized into objective built environment attributes and perceived environmental attributes. Results indicate that land use diversity and green coverage significantly correlate positively with better physical health outcomes, while safety, road quality, and environmental aesthetics significantly correlate positively with mental health. In contrast, some indicators, such as intersection density and NDVI, did not show significant correlations. This study explores the synergistic and complex effects of objective and perceived environmental characteristics in Chinese cities on the physical and mental rehabilitation of older adults within the context of dual-trend superposition. The findings not only provide scientific evidence for future urban planning and renewal in China but also offer valuable references for addressing the tension between urbanization and the health of older adults in Asia and other developing regions.

In the context of people-oriented and high-quality development, improving the quality of rural public service facilities is an important way to promote rural revitalization. However, at present, there are problems such as imbalanced resource allocation, single service functions, and low service quality in regard to rural public service facilities in general, and the contradiction between supply and demand is becoming increasingly prominent. Only by effectively grasping the correlation between residents’ needs and their satisfaction with such facilities can the precise allocation of service facilities be carried out. In this study, Gaoqing County, Zibo City, Shandong Province, is selected as the empirical case. By applying the Kano model and Importance–Performance Analysis (IPA) through the use of questionnaire surveys and expert interviews, we develop an integrated “demand–satisfaction” analytical framework to propose targeted optimization strategies for rural public service facilities. Based on the Kano model, the essential, desired, and attractive needs of rural residents for various types of facilities were identified. Combining the IPA model, the coupling and coordination relationship between demand and satisfaction was further analyzed, and rural public service facilities were analyzed and categorized accordingly. The research results show that educational institution facilities, healthcare facilities, and market facilities have the highest demand, while cultural, sports, and technological facilities have relatively low demand. Based on the analysis results, this study proposes an optimization strategy for the classification and grading of the “demand–satisfaction” coupling relationship of public service facilities. It provides a scientific basis for the layout optimization of rural public service facilities, and also provides an effective reference for the sustainable development and service quality improvement and upgrading of rural areas.