关键词: 快递服务网点, 空间分布特征, 集聚模式, 影响因素, DBSCAN算法, Ripley's K函数, 北京

Abstract:

The leap-forward development of e-commerce has injected fresh blood into the express delivery industry to promote the national economy. As a bridge between express delivery companies and users, service outlets have gradually become an important research object of urban geography and logistics geography.In this paper, by taking Shunfeng Express service outlets of Beijing as the research object,we introduced for the first time the DBSCAN clustering algorithm andunattended intelligent express cabinets into the urban logistics express industry. Spatial analysis methods such as the nuclear density analysis and Ripley's K function wereused to quantitatively compare and analyze the spatial pattern, agglomeration features, and influencing factors of the two types of Shunfeng Express service outlets, namely, manned cooperative outlets and unattended intelligent express cabinets.Results show that: (1) The density-based DBSCAN clustering algorithm can quickly and efficiently identify clusters of express service outlets of any arbitrary shape. The algorithm identified 24 intelligent express cabinet clusters and 14 cooperative network clusters. (2) The high-density area of Shunfeng Express service outlets was mainly concentrated in residential areas with large population density, economic prosperity, convenient transportation, and residential areas in popular business districts, such as Shuangjing, Financial Street, Sanlitun, and Xueyuan Road. (3) The spatial distribution of the two types of service outlets, namely, cooperative outlets and smart express cabinets, was in an agglomeration mode, but the scale of agglomeration was different. The scale of express cabinet agglomeration was significantly larger than that of cooperative outlets, while the intensity of agglomeration was smaller than that of cooperative outlets. (4) The intelligent express cabinet had a large cluster density and a smaller service radius, and was more inclined to serve residential areas within the walking distance; the cooperative network had a smaller cluster density and a larger service radius, and the service object extended to the surrounding major areas with the service radius, at the same time, the demand for traffic accessibility increased. (5) The layout of Shunfeng Express service outlets were the result of a combination of the regional economic level, population size, traffic conditions, land use types, and urban functional area positioning.

Key words: express service outlets, spatial distribution characteristics, influencing factor, agglomeration mode, DBSCAN algorithm, Ripley's K function, Beijing