Spatial distribution pattern of the customer count and satisfaction of commercial facilities based on social network review data in Beijing, China

In cities, commercial facilities play a very important role in economic growth and urban development. Current studies often discuss the spatial distribution of commercial facilities. The spatial distribution and relevant influencing factors of the customer count and satisfaction of commercial facilities, however, has rarely been considered. In this paper, a Weighted Network-constrained Kernel Density Estimation is applied to social network review data to analyze the spatial distribution of customer count and satisfaction of commercial facilities. We found differences in the spatial distribution of customer count, satisfaction, and the location of commercial facilities. To analyze these spatial differences, we present a new method for quantitative analysis using the Network-constrained Local Getis-Ord's General G* as an indicator. Road segments with high-value spatial clustering or low-value spatial clustering were detected, reflecting the spatial distribution pattern of the customer count and satisfaction of commercial facilities. The Network-constrained K-Function was used to explore the spatial clustering pattern of commercial facilities as well as the correlation between the spatial distribution of commercial facilities and other POI data, such as subway stations or business centers. The results of these analyses provide a quantitative reference when deciding locations for commercial facilities, and can help us to identify problems in commercial facility services to improve the quality of life among urban residents.