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港口建设如何产生影响?全球石油运输中的扩散模式。

How Does a Port Build Influence? Diffusion Patterns in Global Oil Transportation.

机构信息

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

Naval Academy Research Institute, Brest Naval, CEDEX 09, 29240 Brest, France.

出版信息

Sensors (Basel). 2022 Nov 8;22(22):8595. doi: 10.3390/s22228595.

DOI:10.3390/s22228595
PMID:36433192
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9695644/
Abstract

Ports play a critical role in the global oil trade market, and those with significant influence have an implicit advantage in global oil transportation. In order to offer a thorough understanding of port influences, the research presented in this paper analyzes the evolution of the dominance mechanisms underlying port influence diffusion. Our study introduces a port influence diffusion model to outline global oil transport patterns. It examines the direct and indirect influence of ports using worldwide vessel trajectory data from 2009 to 2016. Port influences are modelled via diffusion patterns and the resulting ports influenced. The results of the case study applied to specific ports show different patterns and influence evolutions. Four main port influence trends are identified. The first one is that ports that have a strong direct influence over their neighboring ports materialize a directly influenced area. Second, geographical distance still plays an important role in the whole port influence patterns. Third, it clearly appears that, the higher the number of directly influenced ports, the higher the probability of having an influence pattern, as revealed by the diffusion process. The peculiarity of this approach is that, in contrast to previous studies, global maritime trade is analyzed in terms of direct and indirect influences and according to oil trade flows.

摘要

港口在全球石油贸易市场中发挥着至关重要的作用,那些具有重要影响力的港口在全球石油运输中具有隐含的优势。为了全面了解港口的影响力,本文研究分析了港口影响力扩散的主导机制的演变。我们的研究引入了一个港口影响力扩散模型,以概述全球石油运输模式。它使用 2009 年至 2016 年的全球船舶轨迹数据,研究了港口的直接和间接影响力。港口影响力通过扩散模式和由此产生的受影响港口进行建模。应用于特定港口的案例研究结果显示了不同的模式和影响力演变。确定了四个主要的港口影响力趋势。首先,那些对其邻近港口具有强大直接影响力的港口会形成一个直接影响区域。其次,地理位置在整个港口影响力模式中仍然起着重要作用。第三,通过扩散过程清楚地表明,直接受影响的港口数量越多,影响力模式的可能性就越高。这种方法的特点是,与以往的研究相比,根据石油贸易流量,直接和间接影响来分析全球海上贸易。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cd/9695644/c12a3a48c757/sensors-22-08595-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cd/9695644/d5fd7caec0f8/sensors-22-08595-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cd/9695644/7258105213df/sensors-22-08595-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cd/9695644/e6c9ccd87b2e/sensors-22-08595-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cd/9695644/8b4910f83b2d/sensors-22-08595-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cd/9695644/854b382d1806/sensors-22-08595-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30cd/9695644/2ff5ecc53bf2/sensors-22-08595-g012.jpg
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本文引用的文献

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Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data.基于 AIS 数据在线清洗、压缩、分区和聚类的动态海上交通模式识别。
Sensors (Basel). 2022 Aug 22;22(16):6307. doi: 10.3390/s22166307.
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