地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (7): 1481-1499.doi: 10.12082/dqxxkx.2023.220884

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COVID-19疫情前后北美五大湖航运网络多尺度时空变化及影响因素研究

潘佳乐(), 信睿()   

  1. 山东科技大学测绘与空间信息学院,青岛 266590
  • 收稿日期:2022-11-13 修回日期:2023-02-17 出版日期:2023-07-25 发布日期:2023-06-30
  • 通讯作者: *信 睿(1991— ),男,山东莱芜人,博士,主要从事时空数据挖掘、新型地图可视化研究。 E-mail: xinrui@sdust.edu.cn
  • 作者简介:潘佳乐(2001— ),男,陕西咸阳人,硕士生,主要从事时空数据挖掘研究。E-mail: panjl@sdust.edu.cn
  • 基金资助:
    国家自然科学基金项目(42101452);山东省自然科学基金项目(ZR2021QD027)

Multi-scale Spatio-temporal Changes and Influencing Factors of the Shipping Network in the Great Lakes of North America during the COVID-19 Pandemic Period

PAN Jiale(), XIN Rui()   

  1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
  • Received:2022-11-13 Revised:2023-02-17 Online:2023-07-25 Published:2023-06-30
  • Contact: *XIN Rui, E-mail: xinrui@sdust.edu.cn
  • Supported by:
    National Natural Science Foundation of China(42101452);Shandong Provincial Natural Science Foundation, China(ZR2021QD027)

摘要:

新冠疫情给全球海洋运输业造成了严重的影响,在后疫情时代航运产业的复苏过程中蕴含新的挑战与发展机遇。现有研究时间跨度不足,无法充分认识航运网络的变化过程,其次,仅对宏观尺度空间格局变化进行分析忽略了网络变化的内在驱动因素。因此本研究基于复杂网络理论、社区探测算法和TOPSIS分析方法,提出一种航运网络多尺度变化分析框架,结合2019—2021年北美五大湖船舶轨迹数据分析整体航运网络、港口群落和港口3个尺度的变化特征。此外,还对各类型航运网络的变化机理进行追踪。研究结果表明:① 网络流量方面,疫情前货轮和牵引船交通量占比达交通量总量的80%,疫情爆发后船舶交通量总量下降22.4%,各类型船舶交通量变化的时期、速度不同,其中,游船与货轮恢复能力强,油船次之,客船交通量降幅超50%为各类最高,受影响严重且恢复能力最弱,后疫情时期交通量总量较疫情前上涨4.4%,表明网络流量恢复能力整体较强;② 网络结构方面,疫情前航运网络随气温变暖呈扩张趋势,港口数量、航线数量递增式上升,疫情后网络结构受损连通性下降,后疫情时期指标值迅速恢复、航线密度增大,期间网络“骨架”未被破坏。整体而言,五大湖航运网络韧性较强; ③ 五大湖地区大型港口群位于各大湖泊内部且空间结构稳定,重要港口位于各大湖泊交界处,疫情前后港口评价值变化过程具有复杂性与不对称性。本研究通过多尺度视角分析网络变化,方法可应用于其他交通网络分析,为从业者认识疫情对航运业的影响提供参考。

关键词: COVID-19, 北美五大湖, AIS数据分析, 多尺度时空分析, 复杂网络分析, 多类型航运网络, 时空变化

Abstract:

The COVID-19 pandemic has had a serious impact on the global maritime transportation industry, and there are new challenges and opportunities for the recovery of the shipping industry in the post-pandemic era. However, the limited time span of existing studies is insufficient to fully recognize the change in shipping network. Additionally, analysis of only macro-scale changes in spatial pattern ignores the intrinsic drivers of network changes. Therefore, based on the complex network theory, community detection algorithm, and TOPSIS analysis method, this study proposes a multi-scale change analysis framework for shipping network. The change characteristics of the shipping network, port community, and ports of three scales are analyzed using the North American Great Lakes ship trajectory data from 2019 to 2021. In addition, the change mechanism of each type of shipping is tracked. The results show that: (1) In terms of network traffic, freighters and tugboats accounted for 80% of the total traffic volume before the pandemic outbreak. After the pandemic, the total volume of ship traffic decreased by 22.4%. The change period and rate of the traffic volume of various types of ships are different. Cruise and cargo ships have strong recovery capability, followed by oil tankers, while passenger ships have the highest decrease in traffic volume with a decrease of more than 50% and have the weakest recovery capability. During the post-pandemic period, the total traffic volume increased by 4.4% compared to that before the epidemic, indicating that the overall network traffic recovery ability is strong; (2) In terms of network structure, before the pandemic, the shipping network showed an expansion trend with warmer temperatures, and the number of ports and routes increased incrementally. After the pandemic, the network structure was damaged, leading to a decreased connectivity. The route density increased, and the network "skeleton" was not destroyed during the period. Overall, the Great Lakes shipping network demonstrated strong resilience; (3) Large port groups in the Great Lakes region are located inside the major lakes with a stable spatial structure, and the key ports are located at the junctions of major lakes. The change of port evaluation values before and after the pandemic is complex and asymmetric. This study analyzes network changes from a multi-scale perspective, providing reference for practitioners to understand the impact of the pandemic on the shipping industry, and the method can be applied to other transportation network analyses.

Key words: COVID-19, North American Great Lakes, AIS data analysis, multi-scale spatio-temporal analysis, complex network analysis, multi-type shipping network, space-time change