Abstract
Express delivery volume is an important indicator of intercity e-commerce trading activities, offering a new vision for glimpsing the intercity linkages. However, it has been a challenge to reveal the spatial distribution of intercity express flows due to the limitations on the use of express parcel data by most companies. The study maps the spatial pattern of intercity express connection intensity and identifies the urban centrality, supported by waybill data from China. Intercity express connections form a triangular corridor centred on the ‘Pearl River Delta–Yangtze River Delta–Beijing-Tianjin-Hebei’ urban agglomerations. Shenzhen, Guangzhou, Jinhua, Shanghai, Beijing, Quanzhou, Xingtai, etc., exhibit higher centrality for their strong consumption power or commodity production capacity. These cities also develop relatively strong connections with national central cities (Chengdu, Chongqing, Wuhan, Tianjin, etc.) and provincial capitals (Changsha, Urumqi, Hulun Buir, etc.). The cities on the western side of the Hu-line present low centrality and weak connections with the eastern cities and form sub-network with the provincial capital cities as the core.
Express delivery industry is booming in some developed countries along with developing countries such as China and India, driven by e-commerce. Globally, Chinese express delivery markets are the largest, with 108.3 billion express deliveries in 2021. Allocating and delivering resources cross-regionally strengthens intercity connections. However, real-world and high-quality datasets are not readily available, and even simple quantitative information (Rai and Dablanc, 2022). As such, revealing the geographic distribution characteristics of intercity express flows is a challenge that requires further recognition to assist decision-making regarding express network and e-commerce base.
The waybill data is derived from Express 100, which provides parcel trajectory query service for all express enterprises in China with 300 million daily queries. A total of 35,504,607 data for November 2021 were collected with authorization from enterprises, geocoded and counted for each city’s express delivery volume (EDV). We declare that the private information of customers was not involved. The connection strength between cities is visualized with EDV as the weights, and the centrality of each city is represented by the eigenvector centrality (EC). Note that connection strength less than 1000 have been eliminated, and 29,211,686 data have been retained, considering Gephi’s data limitations.
Figure 1 illustrates how intercity express flows are distributed geographically. First, intercity connections are strong on the east side of the Hu-line, forming the triangle corridor centred on the ‘Pearl River Delta–Yangtze River Delta–Beijing-Tianjin-Hebei’ urban agglomerations. The prominent EC of Shenzhen, Guangzhou, Hangzhou, Shanghai, Beijing, etc., can be attributed to their huge consumer markets and their production capacity for goods (Dong et al., 2021; Song, 2022). It has also to be emphasized that the exuberant development of e-commerce has also highlighted some cities, such as Jinhua, Xingtai, Quanzhou, etc. (Wang et al., 2022). Meanwhile, these cities are used to radiate outward and form relatively strong connections with national central cities (Chengdu, Chongqing, Wuhan, Tianjin, etc.) and provincial capitals (Changsha, Urumqi, Hulun Buir, etc.). Compared with this, express connections between the west and east sides of the Hu-Line are weaker constrained by the transportation infrastructure. Also, sub-networks with provincial capitals as the core are noticeable. One glaring example is Urumqi, which connects prefecture-level cities within the province. Spatial patterns and urban centrality of express flows in China.
In general, the findings are consistent with reality. The triangular connection and sub-network characteristics are also well supported by studies on cargo flows (Ren et al., 2020). However, our findings highlight the role of supply factors more. Some cities in western China have high-quality agricultural products. Improving transport infrastructure and cold chain warehouses in these regions will help strengthen their links with backbone nodes and stimulate regional economic growth.
Footnotes
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China [42271195, 72173101].
