## 1 Introduction  
With the increasing rates of urbanization, urban construction, and population density growth, the global development of high-quality cities is imminent. The street constitutes a direct display of urban imagery, serving as the principal site of citizens' activities, and has a significant impact on the quality of life of urban residents. At present, big data technology has been deeply integrated into the urban digital transformation [1, 2, 3, 4]. The deep integration of urban digital transformation and big data technology provides a foundation for the scale, refinement, and intelligence of urban spatial research. For planning and architectural research, the combination of streetscape imagery and geographic information breaks through the limitations of traditional field collection methods. It also has a positive effect on large-scale urban measurement research. At present, street-view imagery has high coverage in large cities around the world and provides high-density and massive images based on urban road networks [5]. Street-view images have been employed in the study of the spatiotemporal traffic mode [6], the spatial perception score [7], the evaluation of urban greening [8], and the earthquake risk assessment of cities [9]. Street-view photographs have also opened up new possibilities for the study of urban space; however, there remains a need to investigate the different ways of combining street-view images to conduct research on the effects of urban street-space quality. Different cities have different urban characteristics, and there is variation within different blocks and different spaces of the same city [10]. These factors lead to local differences in street-space perception in an urban area. Therefore, the study of spatial perception not only has global characteristics but can also capture the distinctive characteristics of blocks within a city. Many Western countries emphasize the protection of urban characteristics and plan regional development via urban planning documents (e.g., the Vancouver City Plan, the Sheffield Development Framework, the London Plan 2021, etc.). Similarly, some studies in China have focused on providing different guidance in different zoning control zones. An example can be found in the work of scholar [PERSON] Taking Harbin as an example, the author selected 10 places with different period characteristics in order to discuss the protection and regeneration of urban characteristics under the Chinese planning system [11]. The case in this study, similar to that of Harbin, describes a large provincial capital city in central China. Possessing the comprehensive characteristics of a modern city, historical city, comprehensive city, and representative city, the Wuchang District is one of the central urban areas of Wuhan city. Its urban street characteristics cover many periods, and some streets retain the historical characteristics of different periods. This is one of the core reasons for performing this study using the Wuchang District of Wuhan. The principal objective of this study is to answer the following questions:  
What are the characteristics of urban street spatial perception scoring in spatial patterns in relation to the scoring situation based on street-view images? Is the quality score, which leads to such local differences, related to the spatial measures of urban public facilities and the human visual scale? If they are related, how do different facilities and different spatial visual measures affect the perception of street spatial quality? The main contribution of this study lies in constructing a research framework for the assessment of the multi-dimensional impact of urban street spatial perception based on multi-source data. These dimensions include the autocorrelation of the overall spatial pattern, the influence of the public facility distribution, and the influence of the human visual scale quantification index.