## 3 Results
### Impact of Obstructions and Barriers  
While the above analysis focused on the binary presence or absence of obstructions and barriers, this section explores how the quantitative extent of these objects affects pedestrians' perceived risk. To do so, two pixel-based ratios were used: the obstruction-sidewalk ratio and the barrier-sidewalk ratio. These indices were derived from SAM-based image segmentation, as described in Section 2.4.  
Table 4 shows [PERSON]'s rank correlation between the mean perceived risk levels and both the obstruction-sidewalk ratio and the barrier-sidewalk ratio, respectively. The results indicate that the obstruction-sidewalk ratio exhibited a significant, positive correlation with the mean perceived risk ([PERSON]'s coefficient = 0.856, \(p\) < 0.001). In contrast, the barrier-sidewalk ratio showed no significant relationship with the mean perceived risk ([PERSON]'s coefficient = \(-\)0.088, \(p\) = 0.502), suggesting that the relative size of barriers alone does not meaningfully alter pedestrians' ratings.  
In addition to the correlation analysis, an ordinary least squares regression was performed to determine whether the mean perceived risk could be estimated from the obstruction-sidewalk or barrier-sidewalk ratios. As shown in Table 5, when the obstruction-sidewalk ratio was used as the independent variable, the model achieved an R-squared value of 0.66 and was highly significant (F = 112.4, \(p\) < 0.001). The positive slope coefficient (3.24) confirmed that as obstructions occupy a greater portion of the sidewalk area, participants tend to perceive the environment as more dangerous. By contrast, when using the barrier-sidewalk ratio, the slope was negative (\(-\)0.4772), implying a potential trend whereby an increased barrier area might reduce the perceived risk. However, the model's explanatory power was limited (R\({}^{2}\) = 0.05) and statistically non-significant (\(p\) = 0.092).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Spearman’s Coefficient** & \(p\)**-Value** \\ \hline Obstruction–sidewalk ratio & 0.86 & \(p\) < 0.001 \\ Barrier–sidewalk ratio & \(-\)0.09 & 0.502 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spearman rank correlation between pixel-based ratios and mean perceived risk.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison** & **Mean Difference** & \(p\)**-Value** & **Sig.** \\ \hline O0\_B0 vs. O0\_B1 & \(-\)0.53 & \(p\) < 0.001 & *** \\ O0\_B0 vs. O1\_B0 & \(-\)2.12 & \(p\) < 0.001 & *** \\ O0\_B0 vs. O1\_B1 & \(-\)1.91 & \(p\) < 0.001 & *** \\ O0\_B1 vs. O1\_B0 & \(-\)1.60 & \(p\) < 0.001 & *** \\ O0\_B1 vs. O1\_B1 & \(-\)1.38 & \(p\) < 0.001 & *** \\ O1\_B0 vs. O1\_B1 & 0.21 & \(p\) < 0.05 & * \\ \hline \hline \end{tabular} Significance levels: \(*\)\(p\) < 0.05; ***\(p\) < 0.001.  
\end{table}
Table 3: [PERSON]’s post hoc comparisons.  
Figure 6 visualizes these regression results as scatter plots. Each gray dot represents one street image, and the red line indicates the fitted regression line with a 95% confidence interval. In Figure 5(a), a clear positive linear relationship was observed between the obstruction-sidewalk ratio and the mean perceived risk. Conversely, a near-flat regression line in Figure 5(b) indicated no meaningful association between the barrier-sidewalk ratio and the mean perceived risk.  
A few outlier cases can also be observed from the plots. For example, one image showed a relatively high perceived risk level (-4.4) despite a low obstruction-sidewalk ratio (-20%), and another exhibited a similar pattern with a perceived risk of -4.0 and an obstruction-sidewalk ratio of -15%. To investigate the cause of these unexpected results, the corresponding images were reviewed. As a result, heavy construction equipment operating directly on the sidewalk was identified in both cases. This suggested that beyond the quantitative extent of obstructions, the type or visual characteristics of obstructions may also play a critical role in shaping the perceived risk.