Spatial Quality Optimization Analysis of Streets in Historical Urban Areas Based on Street View Perception and Multisource Data

Historic urban areas are unique spaces that carry collective memories and cultural identities, and their spatial quality significantly contributes to urban development and vitality. The sustainable development of these areas is a complex subject that continually garners attention in the field of urban planning. Optimizing their spatial quality necessitates an in-depth understanding of public needs, along with careful consideration of the connection between physical attributes and public perception. Advancements in big data and machine learning have paved the way for the multidimensional evaluation of spatial quality in historic urban areas. In this light, our research proposed a new evaluation system and optimization strategy rooted in the unique attributes and cultural values of these areas. Our empirical research focused on Suzhou Ancient Urban, a representative historic urban area in China. First, we identified six indicators, including appearance, order, atmosphere, and scale, to evaluate the public's perception of the streets in Suzhou Ancient Urban, a place rich with cultural history and local characteristics. This evaluation resulted in a perception map of this area. Second, we explored the unique planning structure, functional distribution, and visual elements of Suzhou Ancient Urban. Using spatial syntax, points of interest (POIs), social media post data processed via natural language processing (SnowNLP), and semantic segmentation methods, we connected the physical attributes of the area with public behavioral preferences and perceptions. This macro-to-micro approach combined subjective and objective evaluations to measure spatial quality. Finally, we established a database for organic urban renewal, which highlights the spatial characteristics of historic urban areas that the public preferred. Our findings indicate that the spaces with higher accessibility in Suzhou Ancient Urban scored better in terms of overall perception. Furthermore, highly distinctive and accessible spaces were the most attractive to the public. Elements such as buildings and walls negatively impacted perception, while infrastructure elements such as roads, pavements, and greenery had a positive effect. This research evaluated the spatial quality of streets in historic urban areas with a special focus on public perception. By combining objective factors such as street accessibility, attractiveness, and visual elements, we discuss the influence of urban structure, function, and components on spatial quality. Our approach, founded on the specific spatial–geographical context of historic urban areas, offered a new methodology for optimizing their quality by integrating subjective and objective factors. Ultimately, our research aimed to foster digital and sustainable development in historic urban areas.