Exploring the Correlation between Streetscape and Economic Vitality Using Machine Learning: A Case Study in the Old Urban District of Xuzhou, China

Abstract

The streetscapes of old urban districts record the changes in urban space and the vitality of socio-economic entities like storefronts. However, prior studies of urban vitality have preferred the demand end of crowd agglomeration to the supply end of commercial businesses, while the refined application of street-view images (SVIs) and the spatial heterogeneity resulting from sectional differences among elements deserve further research. Under this context, this paper took both the alive and the closed storefronts as the objects and developed an analytical framework based on machine learning and SVIs to analyze the characteristics of the streetscape and the economic vitality, followed by a regression analysis between them with a multiscale geographically weighted regression (MGWR) model. Our findings comprise three aspects: (1) despite the sum of the storefronts being more often used, combining the alive and the closed businesses is beneficial to reflect the real economic vitality; (2) as a reflection of the spatial heterogeneity and sectional differences of elements, the asymmetric streetscape has a significant influence on the economic vitality; and (3) although different factors from the streetscape can influence economic vitality differently, based on varied proxies of the vitality, three factors, namely, higher difference value of the signboards, higher sum of glass interfaces, and lower difference value of the glass interfaces, can benefit the economic vitality. This research can support urban physical examination and the regeneration of old urban districts for urban planners, designers, and decision-makers, and provide new perspectives and proxies as well as a more fine-grained analysis among the traditional studies on economic vitality.