Accurate evaluation of the accessibility of medical facilities is a prerequisite for the reasonable allocation of medical resources in a city. The accessibility of medical facilities depends not only on the distance to the supply and demand points, but also on the time spent in the process, and the supply capacity of the supply points. Taking Xi’an City of Shaanxi Province as an example, this paper comprehensively considers the facility supply capacity and introduces the selection probability function based on the two-step floating catchment area (2SFCA) method. In addition, in order to approximate the residents’ acceptance of different types of hospitals for long-distance medical treatment in real situations, different levels of search radius were set for the different levels of hospitals, and ArcGIS was used to measure the accessibility and evaluate the spatial layout of medical facilities in the main urban area of Xi’an. The results show that there is a significant difference in the accessibility of medical facilities in the main urban area of Xi’an, and the accessibility tends to decrease gradually from the central city to the periphery. The inequity in the allocation of medical facilities in the main urban area of Xi’an is more obvious, with about 81.64% of people having access to 54.88% of medical resources. The accessibility evaluation model established by the improved 2SFCA method can obtain more accurate and objective evaluation results. This study can provide a reference basis for urban medical facilities’ planning and rational spatial layout.

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.

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.