Spatial Structure of China’s E-commerce Express Logistics Network Based on Space of Flows

Li, Yuanjun; Wu, Qin; Zhang, Yuling; Huang, Guangqing; Zhang, Hongou

doi:10.1007/s11769-022-1322-0

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…Furthermore, transportation infrastructure significantly diminishes the impact of geographical distance. For example, the logistics network in the Yangtze River Delta region is less affected by geographical distance compared to the Pearl River Delta and the Beijing-Tianjin-Hebei urban cluster [56]. In more harmoniously developed urban clusters, with the improvement of transportation conditions, the influence of geographical factors tends to diminish.…”

Section: Discussionmentioning

confidence: 99%

The Spatial Structure and Driving Mechanisms of Multi-Source Networks in the Chengdu–Chongqing Economic Circle of China

Zhang,

Zuo,

et al. 2023

IJGI

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The phenomenon of polarized development among regional cities has sparked extensive contemplation and indicated a need for research on multi-source regional networks. However, such research faces two obstacles: the absence of quantitative measurement of differences in network structures and the lack of a thorough examination of the degree of city clustering and the dynamics of community composition in hierarchical networks. Thus, we identified 16 cities in the Chengdu–Chongqing Economic Circle (CCEC) as the spatial units to examine the spatial network structures of population, resources, and transportation and the integrated spatial network structure. Using social network analysis, this paper describes the structural characteristics of the three networks (population, resource, and transportation), followed by an analysis of their collective and hierarchical network clustering characteristics, and explores the driving mechanisms and factors that make up each network model. Our results show the following: (1) All three networks exhibit an “east dense, west sparse” characteristic, but there are differences in the layouts of the core cities in terms of the three networks. (2) The clustering characteristics of the hierarchical networks are more pronounced than those of the overall network. The results of the analysis combined with the network formation mechanisms can help effectively plan the future coordinated development of the CCEC.

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Section: Discussionmentioning

confidence: 99%

The Spatial Structure and Driving Mechanisms of Multi-Source Networks in the Chengdu–Chongqing Economic Circle of China

Zhang,

Zuo,

et al. 2023

IJGI

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“…Several scholars have utilized the social network analysis method, which is predominantly divided into two aspects-quantitative tools for industrial-related network structure characteristics and their impact on industrial development. Regarding the characteristics of industrial-related network structure and its research on the impact of industrial development, Li Yuanjun et al [19], based on single express data, studied the characteristics of e-commerce express logistics network structure and its formation mechanism in the three major urban agglomerations in China. They found that its network density is high, and a typical network structure is formed.…”

Section: Research Status Of Social Network Analysis Methodsmentioning

confidence: 99%

Analyzing Express Revenue Spatial Association Network’s Characteristics and Effects: A Case Study of 31 Provinces in China

Wang

Yang

2022

Sustainability

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In recent years, China’s express delivery industry has experienced significant regional differences in income, which may endanger the sustainable development of the economy and affect the sustainable development of society. To study the development setup and influencing factors of the express delivery industry in various regions of China, based on the income data of provincial express delivery, we constructed a spatial correlation network of provincial express revenue in China using the social network analysis method and analyzed its network characteristics and evolution rules. Additionally, the relationship between network structure parameters and express revenue was tested using the spatial Durbin model. The results reveal that the spatial correlation network of China’s provincial express revenue changes from decentralization to agglomeration, and the connection between the express industry in each province is constantly strengthened. The provinces in the eastern coastal areas exhibit a higher degree of centrality and effective scale in the network, and the limit system is smaller, which indicates that these provinces have higher importance and control ability in the network. Simultaneously, a moderating effect exists between network structure parameters and express income, indicating that network structure parameters affect express income through human capital input. This study provides theoretical research and empirical support to promote the coordinated and sustainable development of the express delivery industry.

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“…However, the findings are subject to certain limitations. Outside the city, urban networks are mainly constructed separately using highway [7,8], railway [9][10][11][12], aviation [13][14][15], and flight data. Due to differences in efficiency and experience among different modes of transportation, their spatial patterns and spatial organization models are diverse.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Liang,

Xie,

Bao

2024

Sustainability

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Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

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Spatial Structure of China’s E-commerce Express Logistics Network Based on Space of Flows

Cited by 10 publications

References 39 publications

The Spatial Structure and Driving Mechanisms of Multi-Source Networks in the Chengdu–Chongqing Economic Circle of China

The Spatial Structure and Driving Mechanisms of Multi-Source Networks in the Chengdu–Chongqing Economic Circle of China

Analyzing Express Revenue Spatial Association Network’s Characteristics and Effects: A Case Study of 31 Provinces in China

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

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