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Parking lots are typical urban spaces with a large total area and scattered distribution. With the development of smart cars and shared driving, parking demand is likely to decline. Thus, the reuse of existing parking spaces presents important opportunities and challenges in the process of the digital transformation of future cities. One of the key issues in the sustainable renewal of parking spaces is to make scientific decisions regarding the replacement of functions. Based on relevant data from the urban area of Hangzhou, this study analyzes the spatial co-location relationships between parking spaces and other urban points of interest (POIs). By mining the function association patterns, this research aims to establish a decision-making support model for the function replacement of parking spaces. The following conclusions are drawn: (1) based on charge, size, and affiliation, parking lots can be divided into eight categories; (2) parking lots of different charges, sizes, and affiliations differ in their spatial co-location relationships with POIs; and (3) most parking lots are suitable for catering services, followed by companies and commercial residences. The innovations of this research lie in providing scientific references for the renewal of urban fragmented spaces by mining urban function association rules at the microscale.

Function Replacement Decision-Making for Parking Space Renewal Based on Association Rules Mining