## 4 Results and Analysis
### Analysis of Spatial Patterns of Perceived Spatial Quality in Urban Streets
The local R2 can be interpreted as the proportion of the variance of the dependent variable covered by the regression model globally. As can be seen in Table 4, the local R2 for all six dimensions is above and below 0.4; due to the complexity of the influencing elements of urban spatial perception, a fit of 0.4 can be judged as an acceptable range. Since urban street space is  
Figure 3: Cluster analysis of the high and low values of spatial perception of six streets. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Boring** & **Beautiful** & **Depressing** & **Lively** & **Safe** & **Wealthy** \\ \hline Moran’s \(I\) & 0.125 & 0.122 & 0.131 & 0.257 & 0.126 & 0.336 \\ z-score & 157.58 & 153.67 & 165.64 & 323.54 & 159.10 & 424.31 \\ \(p\)-value & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the global Moran’s \(I\) for the six perceptions of urban streets. closely related to geographic location and its perception scores are spatially heterogeneous, it is also necessary to pay attention to the variability in the local R2 and the fluctuation of the correlation coefficient. Figure 3 shows the values of localized R2, which we segmented. Regression model instability occurred in some places as a result of the absence of street attractions and the scarcity of dependent variable data. However, because they are uncommon, these locations were left out of the study. When the fit is too small, the regression coefficients cannot sufficiently explain the relationship between the independent and dependent variables. As shown in Figure 4, the fit is less than 0.2 (blue area). As such, subsequent studies should carefully refer to the model interpretation. Still, the proportion of this part is small so that the regression coefficients can be used in assessing most dimensions of the relationship between the spatial quality of the city street and the urban amenity points. Different influences have different interpretations in other locations. As such, we organized and analyzed each influential element for the six different perception types. The correlation coefficient represented the effect of different facility point elements. There were nine types of facility point elements used as independent variables in this study: restaurants, office buildings, residential locations, cultural facilities, transportation sites, parking lots, recreational facilities, medical facilities, and government facilities. A negative influence coefficient means the dependent variable contributes negatively to the perceived score. In contrast, a positive influence coefficient implies that the dependent variable contributes positively to the perceived score. The absolute value of the influence coefficient means that a dependent variable in this position contributes more to the perception score. The nine urban amenity elements contain two types of influence. The first category includes the global, entirely positive influence elements, i.e., they are positively influenced globally. The second category addresses local differential influence factors, which refer to factors that are positively influenced in local areas and negatively influenced in regional areas. Let us take the boring perception as an illustration. Figure 5 shows the localization of the influence coefficients of urban facility elements on the boring perception. According to their coefficients' positive and negative values, we classify the results as follows: parking facilities and medical facilities are entirely positive elements, and food and beverage facilities, office buildings, entertainment and recreational facilities, and government facilities are positive elements with local differences. The results of the other five perceptual scores are categorized along the same lines, and the results are shown in Table 5. These will serve as a reference for the later portion of the local impact study. The values of the coefficients can also reflect the local impact on the score situation.