地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (8): 1240-1253.doi: 10.12082/dqxxkx.2019.190114

• 地理空间分析综合应用 • 上一篇    下一篇

深圳市快递自提点的空间分布特征与影响因素

刘玲1,2,李钢1,2,*(),杨兰1,2,薛淑艳1,2   

  1. 1. 西北大学城市与环境学院,西安 710127
    2. 陕西省地表系统与环境承载力重点实验室,西安 710127
  • 收稿日期:2019-03-12 修回日期:2019-04-24 出版日期:2019-08-25 发布日期:2019-08-25
  • 通讯作者: 李钢 E-mail:lig@nwu.edu.cn
  • 作者简介:刘 玲(1996-),女,四川眉山人,硕士生,主要从事城市地理与犯罪地理研究。E-mail: 1101210821@qq.com
  • 基金资助:
    西北大学仲英青年学者支持计划(2016)项目资助

Spatial Distribution Characteristics and Influencing Factors of the Delivery Sites in Shenzhen

LIU Ling1,2,LI Gang1,2,*(),YANG Lan1,2,XUE Shuyan1,2   

  1. 1. College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
    2. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China
  • Received:2019-03-12 Revised:2019-04-24 Online:2019-08-25 Published:2019-08-25
  • Contact: LI Gang E-mail:lig@nwu.edu.cn
  • Supported by:
    Tang Scholar Program of Northwest University (2016)

摘要:

随着信息技术的发展,电子商务的盛行推动着快递行业的迅猛发展,快递自提点成为人们日常生活的重要场所,从而成为城市地理与物流地理的重要研究对象。本文基于2018年4月深圳市菜鸟驿站和中国邮政速递物流站点的POI数据,综合运用文本分析、数理统计、空间分析方法,解析深圳市快递自提点的空间分布特征和影响因素。研究发现:① 快递自提点依托类型多样:由市场主导的菜鸟驿站主要依托专业的快递公司、便利店等;由政府主导的邮政站点一般设于中国邮政的分支服务网点;② 快递自提点服务对象种类繁多,二者都主要以服务社区为主,企业、工业园、酒店等为辅;③ 快递自提点的区位选择一般靠近服务对象的出入口,80%的快递自提点分布在距其最近出入口200 m范围内,邮政站点更接近服务对象;④ 快递自提点的空间分布不均衡,呈现“中西部多,东部少”的特点,沿“东-西”走向集聚分布,为多核集聚模式;⑤ 快递自提点的空间格局是区域经济发展水平、人口分布、交通便捷程度、土地利用类型等多因素综合作用的结果,最后探索了快递自提点选址与分布的综合影响机制。

关键词: 快递自提点, 空间分布, 影响因素, POI, 深圳市

Abstract:

With the advancement of information technology, the prevalence of e-commerce is driving the rapid development of the express delivery industry. To solve the distribution problem of the "last kilometer logistics", delivery sites appeared in most cities of China. The delivery sites, including Cainiao Station and China Post, have become the important places that residents visit frequently in everyday life and become an important research topic of urban geography and logistics geography. Based on the point of interest (POI) data of the Cainiao Station and China Post in 10 municipal districts in Shenzhen, this study used text analysis, mathematical statistics, and spatial analysis to examine the organization form, location choice, spatial distribution, agglomeration mode, and influencing factors of the delivery sites in Shenzhen. Results showed that: (1) The delivery sites depend on different types. The Cainiao station is dominated by sole business and joint venture, which relies on professional express companies and convenience stores, etc. China Post is a state-owned enterprise led by the government, generally located in branch service outlets. (2) The delivery sites have a variety of service objects, and both of Cainiao Station and China Post mainly serve communities, supplemented by companies, industrial parks, and hotels. (3) The location of the delivery sites is as close as possible to the entrances and exits of the service objects. Most of the delivery sites are distributed within 200m from the entrances and exits of facilities, and China Post sites are much closer to the service objects. (4) The spatial distribution of the delivery sites is not balanced, more in the central and western regions yet less in the east. It is distributed in the "east-west" direction, with a multi-core agglomeration mode. (5) The spatial pattern of the delivery sites is a result of the combined effects of multiple factors including the level of regional economic development, population distribution, transportation convenience, land use type, and so on. The delivery sites are distributed on the urban residential land but their number is still small in the marginal residential areas. Their spatial relation presents a geographic-adjacent-based coordinated competition development trend. Our findings help explore the comprehensive impact mechanism of the location choice and distribution pattern of the two types of delivery sites, and indicate future prospects of research, including mining their detection function of urban growth and population distribution.

Key words: delivery sites, spatial distribution, influencing factors, POI, Shenzhen