Imbalance and breakout in the post-epidemic era: Research into the spatial patterns of freight demand network in six provinces of central China

PLoS One. 2021 Apr 22;16(4):e0250375. doi: 10.1371/journal.pone.0250375. eCollection 2021.

Abstract

This study aims to explore the freight demand network spatial patterns in six provinces of central China from the perspective of the spread of the epidemic and the freight imbalance and breakout. To achieve this purpose, the big data of "cart search" demand information provided by small and medium freight enterprises on the freight information platform are analyzed. 343,690 pieces of freight demand big data on the freight information platform and Python, ArcGIS, UCINET, and Gephi software are used. The results show that: (1) The choke-point of unbalanced freight demand network is Wuhan, and the secondary choke-points are Hefei and Zhengzhou. (2) In southern China, a chain reaction circle of freight imbalance is formed with Wuhan, Hefei, and Nanchang as the centers. In northern China, a chain reaction circle of freight imbalance is formed with Zhengzhou and Taiyuan as the centers. (3) The freight demand of the six provinces in central China exhibits typical characteristics of long tail distribution with large span and unbalanced distribution. (4) The import and export of freight in different cities vary greatly, and the distribution is unbalanced. This study indicates the imbalance difference, chain reaction, keys and hidden troubles posed by the freight demand network. From the perspectives of freight transfer breakout, freight balance breakout, freight strength breakout, and breakout of freight periphery cities, we propose solutions to breakouts in the freight market in six provinces of central China in the post-epidemic era.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / economics*
  • COVID-19 / epidemiology*
  • COVID-19 / virology
  • China / epidemiology
  • Cities / economics
  • Cities / epidemiology
  • Economic Recession*
  • Epidemics / economics*
  • Humans
  • SARS-CoV-2*
  • Software
  • Spatial Analysis

Grants and funding

This work is supported by the National Natural Science Foundation of China (Grant no. 71804200); Natural Science Foundation of Hunan Province of China (Grant no.2019JJ50991); Hunan Key Laboratory of Intelligent Logistics Technology (Grant no.2019TP1015) and Foundation of Young Backbone Teachers Training Object of Common Universities in Hunan Province in 2018. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.