摘要
基于“流空间”理论,利用中国智能物流骨干网大数据构建电子商务快递物流网络,采用复杂网络分析、机器学习算法等,探究电商快递物流网络空间结构特征,分析快递物流要素空间流动规律性,揭示网络形成机理。研究发现:1)城市电商快递物流重要性的空间非均衡特征显著,重要性最高的城市分布于胡线以东四大城市群内部。随机森林结果显示,快递物流外向输出型城市在东南沿海形成“电商快递物流输出带”,中国澳门和中国台湾分别以小规模接收京津冀、长三角城市群的快递物流输入为主,中国香港作为快递物流高平衡区在网络中承担着重要的物流集散功能。2)电商快递物流网络覆盖范围广泛,城市间快递物流线路较为完整,且网络具有小世界效应,要素流动效率较高。网络中的优势流以上海、广州、重庆和北京为核心形成“钻石结构”。3)长三角城市群电商快递物流发展较为均衡;京津冀和成渝城市群对内部核心城市依赖性较强;粤港澳大湾区城市群的网络凝聚力最低,中国香港、中国澳门与珠三角9市之间的快递物流联系较为薄弱。4)物流网络形成受城市群发展影响,在信息技术、传统与新型基础设施建设等推动下,网络对距离因素依赖性较弱,快递物流要素主要遵循等级扩散机制。
Based on the space of flows theory,this study adopts China Smart Logistics Network big data to build China's e-commerce express logistics network,and explores the spatial structure characteristics of the ecommerce express logistics network,summarizes the regularity of the express logistics flows,finally reveals the formation mechanism of the network through complex network analysis,machine learning algorithms and other methods.The results are as follows:From the node dimension,the spatial inequality characteristics of the importance of e-commerce express logistics in Chinese cities are significant.Taking Heihe-Tengchong Line(Hu Line)as the boundary,the most important cities in the network are distributed within the four major urban agglomerations east of the boundary.The results based on random forest method show that express logistics export-oriented cities form the"e-commerce express logistics export belt"in the southeast coast.Macao and Taiwan receive express logistics input from Beijing-Tianjin-Hebei Urban Agglomeration and Yangtze River Delta Urban Agglomeration respectively on a small scale while Hong Kong plays an important role in logistics distribution function in the network as a high-equilibrium express logistics area.Additionally,from the dimensions of edges and overall network,the network density value is 0.9270,and the average least connections value is 1.1375,indicating a wide network coverage and relatively complete express logistics routes between cities.Besides,China's e-commerce express logistics network has a small-world effect and high efficiency of the factor flows.A diamond-structured network is also formed with Shanghai,Guangzhou,Chongqing,and Beijing as the four core nodes.In comparison,the Yangtze River Delta Urban Agglomeration is more balanced in the development of e-commerce express logistics;Beijing-Tianjin-Hebei and Chengdu-Chongqing Urban Agglomerations are more dependent on the internal core cities;Guangdong-China Hong Kong-China Macao Greater Bay Area has the lowest network cohesion,and the express logistics links among China Hong Kong,China Macao and the other nine cities in the Pearl River Delta are weak.Overall,the network formation is influenced by the development of urban agglomerations.Driven by information technology,traditional and new infrastructure construction,etc.,the network is less dependent on the distance factor.Express logistics elements mainly follow the hierarchical diffusion mechanism.This research expands the application of logistics big data in the field of urban network research,reveals the structural characteristics and formation mechanism of China's e-commerce express logistics network,helps enrich the theory of"space of flows",and is also of great significance for understanding the city correlation under the digital economy and the shaping of urban space by modern logistics elements,and promoting the digital transformation and high-quality development of express logistics.
作者
李苑君
吴旗韬
李苑庭
梁木新
武俊强
金双泉
Li Yuanjun;Wu Qitao;Li Yuanting;Liang Muxin;Wu Junqiang;Jin Shuangquan(Guangzhou Institute of Geography,Guangdong Academy of Sciences,Guangzhou 510070,China;School of Geosciences,South China Normal University,Guangzhou 510631,China;Nationalchip(Guangzhou),Inc.,Guangzhou 510700,China;Guangdong Provincial Transportation Planning and Research Center,Guangzhou 510101,China)
出处
《热带地理》
CSCD
北大核心
2023年第4期657-668,共12页
Tropical Geography
基金
国家自然科学基金项目(42071165)
广东省科学院打造综合产业技术创新中心行动资金项目(2023GDASZH-2023010101)。
关键词
流空间
城市网络
电子商务
快递物流
物流大数据
中国
space of flows
city network
e-commerce
express logistics
logistics big data
China
