## 4 Results
### Multiple Regression Models  
This section employs OLS and GWR models to explore the mechanisms linking walkability scores in the central urban area to the elderly population and street-level environmental factors. First, a multicollinearity diagnosis was conducted. By calculating the Variance Inflation Factor, we found that all independent variables had VIF values below 5 (Table 8), indicating no significant multicollinearity issues. Thus, all independent variables were retained for subsequent analysis. Notably, the Sky View Factor (SVF) shows a significant negative correlation with walkability scores (\(p\) < 0.05). This phenomenon may reflect the dual impact of urban spatial form on the walking environment: while a higher SVF typically indicates a more open visual experience and a more aesthetically pleasing walking environment, such spatial characteristics are often found in suburban areas with lower building space ratios, where land use intensity and the concentration of public service facilities are relatively insufficient, thereby reducing walking comfort and convenience. Furthermore, the study finds that road space ratio and pedestrian density show positive associations with walkability scores (\(p\) < 0.05), particularly in the 15-min living circle. This result confirms that good road infrastructure and higher pedestrian density are often spatially coupled with well-distributed service facilities, thereby enhancing regional \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **5 min Life Circle** & \multicolumn{2}{c}{**10 min Life Circle**} & \multicolumn{2}{c}{**15 min Life Circle**} & \multicolumn{2}{c}{**VIF**} \\ \hline  & **Coef** & \(p\)**-Value** & **Coef** & \(p\)**-Value** & **Coef** & \(p\)**-Value** & \\ \hline Intercept & 1.421 & 0.059 & 5.844 & 0.010 & 2.153 & 0.635 & \\ Street-level environmental variables & & & & & & & \\ Road & 8.520 & 0.057 & 28.054 & 0.013 & 80.459 & 0.000 & 4.08 \\ Sidewalk & \(-\)1.569 & 0.924 & \(-\)29.486 & 0.533 & \(-\)124.315 & 0.149 & 1.60 \\ Building & 7.013 & 0.010 & 16.919 & 0.030 & 38.015 & 0.006 & 2.88 \\ Wall & \(-\)5.543 & 0.782 & \(-\)65.146 & 0.246 & \(-\)88.008 & 0.294 & 1.23 \\ Fence & 8.431 & 0.566 & 34.152 & 0.447 & 29.576 & 0.735 & 1.46 \\ Traffic & 18.315 & 0.898 & \(-\)386.299 & 0.393 & \(-\)336.588 & 0.683 & 1.70 \\ Vegetation & \(-\)4.457 & 0.148 & \(-\)5.606 & 0.436 & \(-\)15.466 & 0.224 & 1.72 \\ Terrain & \(-\)3.649 & 0.780 & \(-\)57.577 & 0.059 & \(-\)14.362 & 0.757 & 1.68 \\ Sky & \(-\)9.905 & 0.030 & \(-\)29.630 & 0.006 & \(-\)77.853 & 0.000 & 3.89 \\ Person & 151.564 & 0.107 & 517.689 & 0.081 & 1458.779 & 0.018 & 1.98 \\ Vehicle & 17.359 & 0.010 & 60.398 & 0.001 & 117.292 & 0.000 & 1.90 \\ Street population variables & & & & & & \\ Elderly population & 0.000 & 0.063 & 0.000 & 1.000 & 0.000 & 0.890 & 1.18 \\ R\({}^{2}\) & 0.241 & & 0.320 & & 0.449 & & \\ Adjusted R\({}^{2}\) & 0.214 & & 0.296 & & 0.429 & & \\ AICc/AIC & 1972.266 & & 2505.905 & & 2750.737 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The results of global regression. walkability. These findings provide important empirical evidence for optimizing urban walking environments and improving the quality of life for the elderly population. Based on the OLS results and previous research findings on walkability [81], we employed GWR to further analyze how the relationships between the four significant variables--building space ratio, SVF, road space ratio, and pedestrian density--vary across geographic regions and their spatial impacts on walkability scores. The coefficients of these independent variables were spatially visualized to examine their effects in different regions. Figures 6-8 vividly depict the spatial variations of these local parameter estimates for walkability scores within the 5-min, 10-min, and 15-min living circles. The research findings reveal a substantial spatial heterogeneity in the relationship between transportation facility density and walkability scores.