Spatial Quality Evaluation of Historical Blocks Based on Street View Image Data: A Case Study of the Fangcheng District

Abstract

Urbanization in China has reached a mature stage, and research on spatial quality has become an important topic in urban research. This paper employs a machine learning method using a large set of street view image data to explore the spatial quality for historical districts, specifically in terms of vitality, safety, and landscape. The Fangcheng district is taken as the research object to evaluate the spatial quality of historical blocks. The results suggest that the following: (1) The spatial quality of the Fangcheng district presents a pyramidal structure. (2) High-quality streets are mainly distributed in the core areas of historical protection and commercial areas, medium-quality streets are mainly distributed in residential areas around the core areas of the historical district, and low-quality streets are mostly streets with poor accessibility. Based on the findings, we proposed several spatial quality improvement recommendations for the Fangcheng district in Shenyang.