## 6 Conclusions  
Through geographic spatial analysis, machine learning, and urban planning across disciplines, this investigation elucidates the distribution patterns of urban-rural elderly care facilities and the relationship between facility accessibility and street-level environmental factors in central urban areas. Key conclusions are as follows:  
1. Spatial Distribution Characteristics  
Substantial urban-rural heterogeneity was observed in facility distribution. Township areas demonstrated substantially lower public service coverage rates than central urban areas within 5-, 10-, and 15-min living circles (16.63% administrative resource-dependent site selection weighting). Market-driven allocation mechanisms proved insufficient, revealing spatial justice deficiencies through urban-rural resource disparities and inadequate spatial rights protection systems, particularly the \"spatial deprivation\" phenomenon among rural left-behind elderly populations.  
2. Accessibility Disparities  
A distinct urban-rural gradient emerged in walking accessibility. Under the 15-min walking threshold, central urban areas exhibited substantially higher facility accessibility diversity and walkability scores than township regions, displaying a center-periphery decreasing spatial differentiation pattern.  
3. Built Environment Mechanisms  
Street-level built environment factors showed significant correlations with walkability scores. Within 15-min walking isochrones, street building interface density (\(\beta\) = 38.015, \(p\) < 0.01) and transportation facility proportion (\(\beta\) = 117.292, \(p\) < 0.01) positively influenced walkability, while sky view factor demonstrated negative associations (\(\beta\) = \(-\)77.853, \(p\) < 0.01), reflecting complex relationships between high-density development and pedestrian friendliness. At the same time, this research also reveals that new town development needs to focus on the degree of function mixing.  
4. Facility Layout Optimization  
The random forest algorithm-based siting prediction framework identified multiple elderly care service blind zones, effectively quantifying regional service pressures and facility deficiencies, and highlighted the decision-making value of AI technology in the precise allocation of resources. This provides data-driven decision support for addressing spatial equity challenges under urban-rural dual structures.  
The developed \"multi-scale evaluation-machine learning prediction-spatial optimization\" technical framework offers methodological references for aging societies. The spatial prediction model identifies critical construction areas through facility concentration hotspots, proposing science-based solutions for central urban area siting and suburban equity improvement. Using Changsha as a case study, this research establishes an adaptable quantitative model for urban-rural differential governance under population aging, integrating supply-demand perspectives for site optimization. This framework holds particular significance for developing countries confronting dual urbanization-aging challenges, with technical pathways transferable to other regions through parameter localization.