Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous demands across different elderly care facility types. This study addresses these gaps by proposing a data-driven framework that integrates machine learning with spatial analysis to optimize elderly care facility distribution in Xi’an City central area, Shaanxi Province, China. Leveraging multi-source datasets encompassing points of interest (POIs), road networks, and demographic statistics, we classify facilities into three categories (service-oriented, activity-oriented, and care-oriented) and employ an XGBoost model with SHAP interpretability to evaluate spatial distributions and influencing factors. The results demonstrate that the XGBoost model outperforms comparative algorithms (Random Forest, CatBoost, LightGBM) with superior performance metrics (accuracy rate of 97%, precision of 95%, and F1-score of 90%), effectively capturing nonlinear thresholds effects. Key findings reveal the following: (1) Accessibility and road density exert threshold effects on care-oriented facilities, with facility attractiveness saturating when these values exceed 6; (2) Land use intensity and medical resources positively correlate with activity-oriented facilities, while excessive retail density inhibits their distribution; (3) Service-oriented facilities thrive in areas with balanced accessibility and moderate commercial diversity. Spatial analysis identifies clustered distribution patterns in urban core areas contrasted with peripheral deficiencies, indicating need for targeted interventions. This research contributes a scalable methodology for equitable facility planning, emphasizing the integration of dynamic built environment variations with model interpretability. The framework provides significant implications for formulating age-friendly urban policies applicable to global cities undergoing rapid urbanization and population aging.

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.