## 3 Data and Methodology
### Convolutional Neural Network-Based Scoring and Spatial Pattern Study for Scene Perception  
This section quantifies the macro-level research into the perceived spatial quality of urban streets. The street spatial perception dataset is derived from MIT Lab's \"Place Pulse2\" project, an online data collection platform that collects people's perceptual evaluations of the appearance of cities. This dataset has massive data and wide coverage. However, there is still some specificity required for the accurate evaluation of the street perception score in China and its cities. Therefore, in this study, a pre-trained deep learning model with higher pertinence was selected to obtain the perception score of urban street-view images and carry out the analysis. The open-source model was developed and shared by China University of Geosciences (Wuhan). This is a deep-learning model with high precision and was pre-trained on a dataset of the Wuhan area [49]. After processing, the scores were finalized as shown in Table 2. The six street spatial perception scores are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average** & **Maximum** & **Minimum** & **Standard Deviation** \\ \hline boring & 57.81194 & 85.11137 & 23.5317 & 6.27788 \\ beautiful & 17.61704 & 54.10796 & \(-\)22.4117 & 7.96088 \\ depressing & 56.69106 & 91.56737 & 34.66571 & 5.30669 \\ lively & 37.11306 & 86.62331 & 2.01155 & 9.80697 \\ safe & 37.37488 & 61.92188 & 15.30053 & 5.68273 \\ wealthy & 41.92913 & 79.03056 & 10.92321 & 7.75064 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The six perception scores for urban streets.  
relationship between the values of attributes of neighboring spatially distributed objects. The [PERSON]'s \(I\) calculation formula is as follows:  
\[I=\frac{n\sum_{i=1}^{n}\sum_{j=1}^{n}W_{ij}(x_{i}-\overline{x})(x_{j}-\overline{ x})}{\sum_{i=1}^{n}\sum_{j=1}^{n}W_{ij}\sum_{i=1}^{n}(x_{i}-\overline{x})2} \tag{1}\]  
where \(n\) is the total number of space units; \(x_{i}\) is the observed value at space location \(I\); \(x_{j}\) is the observed value at space location \(j\); \(W_{ij}\) is the weighting matrix for space; and \(\overline{x}\) is the average value of all space units.  
The Moran \(I\) index has a value range of \([-1,1]\), with a negative spatial correlation indicated if \(I<0\) and a positive spatial correlation if \(I>0\).  
To further determine the type of numerical clustering in space, we used hotspot analysis to determine whether the space was positively or negatively correlated and to detect the location of clustered areas. Hotspot analysis is quantified by the Getis-Ord \(Gi^{*}\) coefficient, which is calculated by the following formula:  
\[G_{i}^{*}=\frac{\sum_{j=1}^{n}w_{i}x_{j}-\overline{X}\sum_{j=1}^{n}w_{i,j}}{S \sqrt{\frac{\left[n\sum_{j=1}^{n}w_{i,j}^{2}-\left(\sum_{j=1}^{n}w_{i,j}\right) ^{2}\right]}{n-1}}} \tag{2}\]  
where \(x_{j}\) is the attribute values of element \(i\) and element \(j\); \(w_{i,j}\) are the spatial weights of elements \(i\) and \(j\); and \(n\) is the total number of elements in the dataset.  
\[\overline{X}=\frac{\sum_{j=1}^{n}}{n} \tag{3}\]  
\[S=\sqrt{\frac{\sum_{j=1}^{n}x_{j}^{2}}{n}-(\overline{X})^{2}} \tag{4}\]  
The \(z\)-scores and \(p\)-values obtained indicate where spatial clustering occurs for high- or low-value elements.