Utilizing Multi-source Data Fusion to Identify the Layout Patterns of the Catering Industry and Urban Spatial Structure in Shanghai, China

Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities. This study used Shanghai’s catering industry as a case study, leveraging electronic reviews and consumer data sourced from third-party restaurant platforms collected in 2021. By performing weighted processing on two-dimensional point-of-interest (POI) data, clustering hotspots of high-dimensional restaurant data were identified. A hierarchical network of restaurant hotspots was constructed following the Central Place Theory (CPT) framework, while the Geo-Informatic Tupu method was employed to resolve the challenges posed by network deformation in multi-scale processes.These findings suggest the necessity of enhancing the spatial balance of Shanghai’s urban centers by moderately increasing the number and service capacity of suburban centers at the urban periphery. Such measures would contribute to a more optimized urban structure and facilitate the outward dispersion of comfort-oriented facilities such as the restaurant industry. At a finer spatial scale, the distribution of restaurant hotspots demonstrates a polycentric and symmetric spatial pattern, with a developmental trend radiating outward along the city’s ring roads. This trend can be attributed to the efforts of restaurants to establish connections with other urban functional spaces, leading to the reconfiguration of urban spaces, expansion of restaurant-dedicated land use, and the reorganization of associated commercial activities.The results validate the existence of a polycentric urban structure in Shanghai but also highlight the instability of the restaurant hotspot network during cross-scale transitions.