Maritime network dynamics before and after international events

doi:10.1007/s11442-018-1514-9

Abstract

Abstract:

Investigating the influence of international events on global maritime networks is a challenging task that must comprehensively incorporate geographical, political, and maritime sciences. Understanding global maritime network dynamics is an initial and critical step in this investigation. This study proposes an automatic identification system (AIS)-based approach to understanding maritime network dynamics before and after international events. In this approach, a spatiotemporal modeling method is introduced to measure the similarity in shipping trends before and after international events. Then, a spatiotemporal analytic framework is proposed to understand the maritime network dynamics by grouping similar situation, and assessing possible indirect effects within a network. Finally, three case studies of international events, military conflict, lifted economic sanctions, and government elections, were used to investigate the observed network dynamics possibly affected by international events. The results indicate that container, tanker, and bulk shipping between India and its connected countries all declined more than 69% after military conflicts between India and Pakistan in August 2015. Tanker shipping between Iran and the United Arab Emirates increased 51% after economic sanctions on Iran were lifted. Container shipping between Sri Lanka and Singapore, Malaysia, and India increased more than 74% after the general election in Sri Lanka. These investigations demonstrate the feasibility of the proposed approach in assessing the possible effects of international events on maritime network dynamics.

Key words: global maritime network, fluctuation dynamics, international events, spatiotemporal similarity

Zhixiang FANG, Hongchu YU, Feng LU, Mingxiang FENG, Meng HUANG. Maritime network dynamics before and after international events[J].Journal of Geographical Sciences, 2018, 28(7): 937-956.

Figures/Tables 15

Table 1

Summary of research on maritime networks"

Research category	Research contents
Structure and dynamics	Spatial structure (Xu et al., 2015); regional dynamics (Ducruet and Notteboom 2012; ; Yu et al., 2017); time dynamics (Ducruet, 2016); spatial heterogeneity (Liu et al., 2017; Li et al., 2016); reachability (Li et al., 2014b), and local strength and global weakness (Ducruet et al., 2009).
Network and flows	Network diversity and maritime flows (Ducruet, 2013; Dinwoodie et al., 2013); statistical properties, including distribution extent, correlations, weight distribution, strength distribution, average shortest path length, line length distribution, and centrality measures (Hu and Zhu, 2009; Fugazza, 2017); centrality and vulnerability (Laxe et al., 2012; Viljoen and Joubert, 2016; Wu et al., 2017; Wang et al., 2016); connectivity and complexity (Jiang et al., 2015; Tian et al., 2007; Liu and Hu et al., 2017); inequality (Xu et al., 2015a); and evaluations of robustness (Peng et al., 2017).
Maritime transport and efficiency	Direct port-to-port service, hub, and spoke networks (Fremont, 2007; Wang and Wang, 2011); maritime economics (Stopford, 2009); logistics (Rodrigue and Browne, 2002; Song and Lee, 2009; Davarzani et al., 2016); transportation (Guerrero and Rodrigue, 2014; Gagatsi et al., 2017); transport network design (Angeloudis et al., 2015; Karsten et al., 2017) and their network efficiency (Song et al., 2005; Tai and Hwang, 2005; Zeng and Yang, 2002; Fahmiasari and Parikesit, 2017); intermediacy (Rodrigue, 2017); maritime transport chain choice (Talley and Ng, 2013; Lam and Yam, 2011) and interactions (Knappett et al., 2008); and oligopolistic and competitive carrier behavior (Lee et al., 2012).
Maritime safety	Risk (Akhtar and Utne, 2014; Lam et al., 2014a); safety (H?nninen et al., 2014); and maritime search and rescue operations (Bezgodov and Esin, 2014a)

Table 1

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Item	Meaning
MMSI	Unique ID for the vessel
Start time (Ship entering the port)/End time (Ship leaving the port)	Second-level timestamp (e.g., 2015-06-10 01:16:58)
Port’s location	Longitude and latitude of the port location
World_port_index_number	Index number for a port
Region_index	Index number for a region
Port_name	Name of the port
Wpi_country_code	Code for the port country
Vessel_type	Type of vessel (bulk / container / tanker)
Vessel_name	Name of the vessel

Link	Highest number of voyages and time period	Lowest number of voyages and time period	Average voyages before Event B	Average voyages after Event B	Minimum crossing area after standardi- zation	Time period for the same trend after standardi- zation	Similarity of time-varying changes (1/journeys)
Iran-United Arab Emirates	143 (2015-10)	64 (2015-01)	59.806	121.176	0.353	0.167	0.473
United Arab Emirates- Indonesia	17 (2015-10)	3 (2015-02)	5.871	12.294	0.353	0.167	0.473

Link	Highest number of voyages and time period	Lowest number of voyages and time period	Average voyages before Event C	Average voyages after Event C	Minimum crossing area after standardi- zation	Time period of the same trend after standardi- zation	Similarity of time- varying changes (1/journeys)
Sri Lanka-India	412 (2015-03)	0 (2014-07)	34.286	241.286
India-United Arab Emirates	136 (2015-06)	0 (2014-07)	15.571	85.857	0.444	0.361	0.811
India-Saudi Arabia	39 (2015-03)	0 (2014-06)	4.429	23.857	0.530	0.259	0.489
India-Pakistan	91 (2015-03)	3 (2014-06)	10.286	59.571	0.250	0.185	0.741
Sri Lanka-Singapore	114 (2015-06)	1 (2014-07)	18.571	70.429	0.413	0.315	0.762
Singapore-Bangladesh	50 (2015-03)	1 (2014-07)	8.833	23.714	0.413	0.315	0.762
Sri Lanka-Malaysia	148 (2015-06)	0 (2014-07)	17.571	83.143