## 3 Data and Methodology  
### Research Framework  
The main framework of the research methodology of this study is as follows:  
In the first stage, it was necessary to obtain street image data using geographic information. We first collected the road network data in Wuchang District, Wuhan, through Open Street Map (OSM), the tool used in this study. Such data include urban expressways, main roads, secondary roads, and other side roads. For this study, 26,000 sampling points were generated. The Baidu Street View API was parsed and used to obtain the street-view images. Then, it was necessary to obtain Baidu maps of Wuchang District public facility interest points to provide essential data support for the correlation study.  
In the second stage, based on the urban six perception pre-training deep learning model of the China University of Geosciences (Wuhan) [49], we scored all street-view images using six categories: boring, beautiful, depressing, lively, safe, and wealthy. They were visualized in Arc GIS according to the coordinates of street-view acquisition points. The spatial distribution pattern of spatial autocorrelation and the scoring of different streetscape images were also investigated via the study of the spatial quality of urban streets at the macro level, i.e., from a global perspective. This part mainly quantified the spatial characteristics of different dimensions of the global [PERSON]'s \(I\) index and hot-spot analysis. The aggregate distribution of categories yielding high and low perceptual scores was then discovered. This can help researchers analyze the spatial quality pattern of urban streets more clearly, realize a comprehensive study at the global level, and provide measurement support for the future urban planning field.  
In the third stage, we mainly assessed the impact of urban facility elements on the perceived quality of urban streets, such as office facilities, dining facilities, medical facilities, etc. Firstly, the urban facilities data were cleaned and organized in order to screen the elements used as independent variables in this study. Then, the [PERSON]'s correlation coefficient was used to analyze the correlation between the six perceptual dimensions and the various types of facility elements, exclude non-significantly correlated factors, and research the influence of the remaining ingredients through geographically weighted regression. Due to the apparent spatial heterogeneity of streets, we conducted a meso-scale study on the spatial quality of urban streets in typical regions. This explored the reasons for the formation of different street perception score differentiation by combining other regional characteristics and the influence coefficients of their influencing factors. We analyzed and summarized the degree of the various influencing factors and finally sorted out the influencing mechanisms behind them to provide a basis for the future improvement of street quality at the urban planning level.  
In the fourth stage, we measured the influencing factors of urban street spatial quality at the micro level. In this part, we used semantic segmentation PSPNet to segment the street image, extracted the visual elements in the image, and calculated the green visual rate, sky visibility, interface enclosure, and other measurement indicators. The positive and negative impacts of the above indicators on urban street-space perception were analyzed through correlation analysis in order to guide future urban street-space transformation.