## 3 Method
### Evaluation of 15-min Walkability to Elderly Care Facilities
#### 3.3.1 Machine Learning-Based Analysis of Facility Importance Weights
In Changsha's central urban districts, the top three primary indicators were catering services (0.3273), medical facilities (0.1790), and government institutions (0.1663), with secondary indicators prioritizing Chinese restaurants (0.16046), government agencies (0.14259), and residential zones (0.09495). These findings demonstrate that lifestyle-supporting infrastructure substantially impacts senior care facility sitting in urban cores, suggesting planners should emphasize such amenities during site selection processes. The strong correlation between elderly care facilities and residential clusters likely reflects aggregated demand stemming from population concentration effects. In contrast, township areas exhibited a distinct prioritization pattern, with catering services, government institutions, and retail establishments comprising the top three primary indicators. Secondary indicators revealed the highest weights for government agencies (0.21532), Chinese restaurants (0.17109), and convenience stores (0.12842). This spatial preference pattern implies rural elderly care facilities tend to cluster near administrative centers (e.g., village committees), potentially due to enhanced access to integrated public services, including administrative processing and community activities. Such colocation advantages may better satisfy seniors' social support needs and daily living requirements through improved service accessibility. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Urban area & 0.792 & 0.814 & 0.792 & 0.785 \\ Rural area & 0.793 & 0.797 & 0.793 & 0.791 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical analysis of the RF model’s performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**First Level Indicators** & **Second Level Indicators** & **First-Level Weight** & **Local Weight** & **Global Weight** \\ \hline \multirow{4}{*}{Catering} & Chinese Restaurant & & 0.49021 & 0.16046 \\  & Dessert and beverage & & 0.27263 & 0.08924 \\  & Fast restaurant & & 0.13724 & 0.04492 \\  & Bakery & & 0.09992 & 0.03271 \\ \hline \multirow{4}{*}{Shopping} & Supermarket & & 0.25023 & 0.03852 \\  & Shopping mall & & 0.07064 & 0.01087 \\  & Convenience store & & 0.37732 & 0.05808 \\  & Integrated market & & 0.18385 & 0.02830 \\  & Bird and flower market & & 0.11796 & 0.01816 \\ \hline \multirow{4}{*}{Septtlement} & Resident & 0.0950 & & 0.09495 \\ \cline{2-5}  & Bus station & & 0.87539 & 0.02049 \\  & Subway station & & 0.12461 & 0.00292 \\ \hline \multirow{4}{*}{Medical care} & General hospital & & 0.21525 & 0.03854 \\  & Specialized hospital & & 0.09776 & 0.01750 \\  & Clinic & & 0.23671 & 0.04238 \\  & Pharmacy & & 0.45028 & 0.08062 \\ \hline \multirow{2}{*}{Leisure} & Park and square & & 0.23619 & 0.00351 \\  & Landscape & & 0.76381 & 0.01133 \\ \hline \multirow{4}{*}{Finance} & Bank & & 0.37171 & 0.01495 \\  & ATMs & 0.0402 & 0.40827 & 0.01642 \\  & Insurance & & 0.22002 & 0.00885 \\ \hline \multirow{2}{*}{Government} & Government & 0.1663 & 0.85744 & 0.14259 \\  & Social organization & & 0.14256 & 0.02371 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The weight of amenities is calculated by random forest in the central urban area.