Abstract

Waterfront space is an important spatial carrier that reflects the quality of the human settlement environment. In the stage of stock-based urban renewal, value evaluation is the primary task for achieving the “precise optimization” of waterfront spaces. Based on Maslow’s hierarchy of needs, this study constructs a waterfront space value evaluation system covering basic support value, livability value, and well-being value. Taking the Suzhou Creek waterfront space in Shanghai as a case study, this research integrates data such as street-view images and Weibo check-in records, and applies deep learning, natural language processing, and other techniques to evaluate waterfront space value. Correlation analysis, principal component analysis, and spatial autocorrelation analysis are used to explore the factors associated with waterfront space value. The results show that waterfront space value presents significant spatial differences, with the eastern area near the Huangpu River showing a markedly higher value than the western area. Spatial value is associated with street environment, facility services, spatial morphology, functional experience, and spatial attractiveness. In addition, the results of spatial autocorrelation analysis indicate that optimizing functional experience and facility services in the eastern core area is of great importance, while improving online popularity and attractiveness in the western area is also important for promoting coordinated development between the eastern and western areas. The findings provide a reference for future waterfront space evaluation and offer scientific support for urban renewal and policy-making.

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