## 3 Data and Methodology
### Study on the Correlation of Multi-Dimensional Elements of Spatial Quality of Urban Streets  
This section studies the relationship between the spatial quality scores of urban streets and urban amenities. The primary method for quantifying and evaluating geospatial dimensions is geographically weighted analytical regression analysis (GWR), first proposed by [PERSON], [PERSON], and [PERSON] [61] in 1996. Geographically weighted regression differentiates the use of different operations at different scales, and each spatial location applies a unique algorithm for regression analysis. In this study, each of the six perceptual scores is used as a dependent variable, and facility points within the urban grid are operated as independent variables to explore the correlation between urban facility configurations and spatial quality scores and to identify the significant influencing factors.  
Firstly, the range of Wuchang District was gridded with 100 \(\times\) 100 m as the image element length and width, and the six perceptual scores were projected into each grid. For a single grid with multiple POI points, the average of the relevant attributes of all the points falling within the range was taken. After pre-processing the facility types, numerous types of facility points were obtained, such as those relating to government facilities, medical facilities, recreational facilities, parking facilities, transportation site facilities, tourist areas, cultural facilities, residential areas, public security and traffic police, large-scale shopping, and office buildings. After completing the above, the facility point POI data required processing. We projected it into the grid as a sum of quantities, i.e., we calculated the number of POI points in each image element. Regression analysis was performed using the perceived score as the dependent variable and the number of urban facilities as the independent variable. To better screen the independent variables, we used the Pearson correlation coefficient to determine whether the independent and dependent variableswere significantly correlated. The independent variables that did not have a significant correlation were finally excluded. Additionally, because the total amount of data for some types of POI points was too small, they were also excluded. A geographically weighted regression was calculated for the final screening results. The formula is as follows:  
\[y_{i}=\beta_{0}(u_{i},v_{i})+\sum_{k=1}^{p}\beta_{k}(u_{i},v_{i})x_{ik}+ \epsilon_{i}i=1,2,\ldots,n \tag{5}\]  
where \(y_{i}\) is the value of the dependent variable at position \(i\); \(x_{ik}(k=1,2,\ldots,m)\) is the value of the independent variable at location \(i\); \((u_{i},v_{i})\) is the coordinates of sampling point \(i\); \(\beta_{0}(u_{i},v_{i})\) is the intercept term; and \(\beta_{k}(u_{i},v_{i})\) is the regression coefficient of the kth independent variable \(x\) at sampling point \(i\).  
After the above work was completed, it was also necessary to correlate the urban microcosmic space measurement indexes. The quantitative calculation methods used in such exercises are Pearson coefficient correlation analysis and linear regression analysis, with the dependent variables being the six urban street perception scores and the independent variables being the five items of green visual index, sky visibility index, degree of enclosure, degree of motorization, and degree of non-motorization.