Using data from 2008 to 2017 regarding the service sector in 11 cities of the Hebei Province, this study measures the degree of agglomeration and efficiency of service subsectors, analyzes their spatiotemporal evolution, and discusses the interactive relationship between them using panel vector autoregressive (PVAR) models and GMM dynamic panel model. The findings are summarized as follows: (1) from 2008 to 2017, service subsectors in Hebei’s cities have a high degree of agglomeration and benefit significantly from specialization; (2) the service in Hebei’s cities is severely inefficient (i.e., efficiency loss is grave). The empirical results of PVAR models reveal that service sector agglomeration is primarily reliant on its own development momentum and while that can improve technical efficiency and pure technical efficiency, it can also inhibit technical change efficiency. The variance decomposition results reveal that service sector efficiency is influenced more significantly by itself than by service sector agglomeration. With the passage of time, the self-influence of efficiency decreases and the influence of service sector agglomeration on efficiency increases.

By taking Beijing as the case site, using open-source Point of Interest data, and employing spatial visualization techniques, this study explores the spatial structural characteristics of the Beijing tourism and leisure industry and its sub-sectors. It has been found that (1) the nearest neighbor indexes of the tourism and leisure industry and its sub-sectors are all less than 1, indicating that the tourism and leisure industry and its sub-sectors in Beijing exhibit a spatial clustering distribution. Scenic spots have the largest R-value of 0.52 and, thus, the lowest degree of clustering. The minimum R-value of 0.15 is found in catering, marking the highest degree of clustering in the industry; (2) the main directional trend of the tourism and leisure industry and its sub-sectors in Beijing is the “northeast-southwest” direction, the south-north directional dispersion is dominant, and scenic spots demonstrate a more noticeable trend of spatial dispersion; (3) within the area from Sanlitun Street in the north to Panjiayuan Street in the south, and from Chaoyangmen Street in the west to Liulitun Street in the east, is situated the largest portion of cluster centers with the highest degree of clustering in Beijing’s tourism and leisure industry. The contiguous high-density cluster center of catering starts from Sanlitun Street in the north to Jinsong Street in the south, and from Chaoyangmen Street in the west to Liulitun Street in the east. The cluster of shopping and entertainment shows a checkerboard pattern in the CZCF and NUDZ. The high-value cluster of accommodation occurs primarily around Sanlitun, Panjiayuan, and Qianmen; (4) the distribution of three grades of hot spot areas and non-significant areas of tourism and leisure, catering, accommodation, and shopping and entertainment in Beijing demonstrates a circular pattern that centers around the CZCF and expands outward in sequence. High-value hot spot streets for this area are dominated by Beixinqiao Street, Hepingli Street, Sanlitun Street, Heping Street, and Tuanjiehu Street; and the high-value cold spot streets of the area are chiefly in Fuzizhuang Township, Wangping Town, Miaofeng Mountain Town, and Tanzhesi Town.