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Journal of Geo-information Science >

2018 , Vol. 20 >Issue 5: 582 - 592

DOI: https://doi.org/10.12082/dqxxkx.2018.180085

Spatial-temporal Evolution Patterns of Maritime Networks between Important Economic Developing Zones Revealed

  • YU Hongchu , 1 ,
  • FANG Zhixiang , 1, 3, * ,
  • LU Feng 2 ,
  • PENG Peng 2 ,
  • ZHAO Zhiyuan 1 ,
  • FENG Mingxiang 1
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  • 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing （LIESMARS）, Wuhan 430079, China
  • 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 3.Engineering Research Center for Spatiotemporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan 430079, China
*Corresponding author: FANG Zhixiang, E-mail:

Received date: 2018-01-27

  Request revised date: 2018-04-27

  Online published: 2018-05-20

Supported by

Key Project of the Chinese Academy of Sciences, No.ZDRW-ZS-2016-6-3

National Natural Science Foundation of China, No. 41771473

National Key Research and Development Program of China, No.2017YFC1405302

LIESMARS Special Research Funding.

Copyright

《地球信息科学学报》编辑部 所有
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Abstract

MSR (countries along the Maritime Silk Road), BRICS (Brazil, Russia, India, China, and South Africa) and AJK (the United States, Japan and South Korea) are important economic developing zones to promote international trade. Study on the spatio-temporal evolution pattern of maritime network is helpful to analyze the competition and balance of international trade between different countries, and to make scientific strategic deployment and intelligent decision in global maritime trade. Automatic Identification System （AIS） data makes the real-time analysis of maritime network possible for its advantages of real-time and the near-complete coverage for the offshore area of the ports. Based on the AIS data of three main types of business ships in international trade, namely bulk ship, container ship and tanker ship, this paper reveals spatio-temporal evolution patterns of maritime networks between important economic developing zones, using the timeline method to describe the change of the networks in and outside different zones. The result shows that the structural maritime network evolutions of bulk-layer, container-layer, and tanker-layer inside the MSR are much larger than the evolutions inside BRICS and AJK, which indicates that the Belt and Road Initiative has promoted the trade between MSR countries. The dynamics outside three typical important economic developing zones （MSR, BRICS, and AJK） are large from 2013 to 2016, which indicates that the networks between them have changed greatly with the implementation of the Belt and Road Initiative. The structural maritime network dynamics weighted by cargo handling capacity in 2015 and 2016 are smaller than 2013 and 2014, which indicates that under the implementation of the Belt and Road Initiative, the changes of cargo handling capacity in and outside the three economic developing zones have decreased in 2015 and 2016. Obviously, the Belt and Road Initiative has different influences on MSR, BRICS, and AJK. The maritime networks inside and outside MSR are affected by this initiative. The bulk, container, tanker maritime networks inside BRICS have diverse dynamics derived from this initiative, which also affected the maritime networks outside BRICS to some extent. This initiative has no influences on the network inside AJK, but partly affects the network outside AJK. Improving the throughput and position of MSR in the maritime network is still very important to global trade balance.

Key words： AIS trajectories; economic developing zones; maritime network; spatial-temporal evolution pattern; the Belt and Road Initiative

Cite this article

YU Hongchu , FANG Zhixiang , LU Feng , PENG Peng , ZHAO Zhiyuan , FENG Mingxiang . Spatial-temporal Evolution Patterns of Maritime Networks between Important Economic Developing Zones Revealed[J]. Journal of Geo-information Science, 2018 , 20(5) : 582 -592 . DOI: 10.12082/dqxxkx.2018.180085

1 引言

“海上丝绸之路”沿线国家（MSR）、金砖国家（BRICS）、美日韩（AJK）是重要经济发展区域,对促进国际运输和经济发展有重要意义。如何及时掌握区域层面海洋运输网络时空动态和演变规律,对分析和掌握海洋运输的制约因素和竞争均衡关系,并进行科学部署和智慧应对十分有帮助。
目前,海运网络的相关研究主要包括结构动态、网络流、海运交通和海运安全等方面。针对海运网络结构动态建模方面,相关学者通过OD流矩阵[1,2]和加权邻接矩阵[3],结合运输时间、运输能力、运营策略、服务模式和腹地可达性等因子,提出了基于组合优化和优先级规则的启发式模型[4],最小化运输时间和最大化运输能力模型[5]等,研究海运网络的空间结构[6]、区域动态[7,8,9]、时间动态[10]、空间异质性[11,12]、空间可达性[13]、局部优劣势[14]等。海运网络流从节点-边交通流（节点度、连接强度、连接紧密性[15,16]）出发,研究海运网络的多样化特征[17,18],如中心性和脆弱性[19,20,21,22]、连接性和复杂性[23,24,25]、不均衡性[26]和健壮性[27]。海运交通研究包括运输服务模式（点对点直航和支线-枢纽转运[10,28]）设计和优 化[29,30],航运公司的竞争和垄断行为分析[31],物流运输系统信息化建设和运营管理[32,33]等,有助于提升海洋运输的经济效益和运营效率[34]等。海运安全包括风险评估[35]、安全航行[36]、海上搜救[37]等。现有研究通常基于国际集装箱、劳氏海事情报组、劳氏船级社以及其他专门网站的统计数据,以记录性数据的实证分析为主,在严谨的方法论上存在不足[38,39]。同时,记录数据主要关注大型国际港口,对区域层级内部港口间的地理空间结构考虑不周,限制了相应研究成果的应用范围。
海洋运输网络演化作为网络动态分析的重要组成部分,目前主要从3个角度出发,包括传统空间港口扩张模式的模拟分析[40],基于腹地可达性和港口区域特性的港口系统演变规律分析[41,42,43,44],和基于图论和复杂网络的运输流的动态演化模型（如随机游走模型和拓扑结构演变分析）[26,45]。目前基于图论和复杂网络的研究主要围绕统计指标（如节点度、聚类系数、最短路径、平均行程数、接近中心性、介数中心性、直达中心性等）开展,对由单个节点层次的变化拓展到整个网络结构的变化考虑不足。海运网络由不同的子网组成,不同的子网作为国际海运格局的重要组成部分,子网内部和子网间的海运网络演变反映了国际政策实施、地理区位、运营策略等对海运网络的影响,如“一带一路”倡议,对所覆盖的沿线国家会产生诸多经济与社会影响,同时也对MSR同AJK、BRICS等的贸易往来产生一定影响。通过MSR、BRICS和AJK 3 个子网间和子网内部的结构动态对比,能够一定程度上反应“一带一路”倡议对海运网络格局产生的影响,同时区域间海运网络时空演变差异化特性也为分析重大事件对海运网络的影响提供了技术支撑。
本文将借助船舶Automatic Identification System （AIS）的位置监控数据,充分利用该数据的实时性及其在全球港口近海区域的覆盖能力,挖掘AJK、BRICS和MSR散货、集装箱、油轮型的内部和外部网络结构演变规律和吞吐量加权的网络结构演变规律,从而满足多层级区域性海运网络动态的及时分析需求,为评估重大政策的可能影响提供一定的依据。

2 研究区和数据源

本文主要采用2013年1月到2016年12月的AIS轨迹数据,包括船舶ID、船舶类型、经纬度坐标、船舶国籍、船舶吨位等信息。研究区域（图1）包括“海上丝绸之路”典型沿线国家（中国CN、菲律宾PH、马来西亚MY、印尼ID、新加坡SG、柬埔寨KH、越南VN、老挝LA、泰国TH、缅甸MM、孟加拉国BD、印度IN、斯里兰卡LK、巴基斯坦PK、沙特SA、也门YE、索马里SO、肯尼亚KE、埃及EG、土耳其 TR、意大利IT（ http://www.hssczl.net/mode_55839.htm ）,BRICS国家（中国CN、俄罗斯RU、印度IN、巴西BR、南非ZA）、AJK国家（美国US、日本JP和韩国KR）。
Fig. 1 Schematic diagram of study area

图1 研究区域示意图

3 研究方法

本文利用4年的全球AIS数据（2013年1月到2016年12月）,挖掘海量船舶轨迹,并结合全球港口航行指南等信息资料,进行空间位置匹配;通过对匹配结果进行OD（Origin and Destination）识别和船次统计,得到不同时段内不同国家间的航次频数,利用船舶ID关联船名库（包括船型、吨位、尺寸等）,估算不同时段内不同国家间的吞吐量。其研究方法流程如下（图2）：
通过BRICS、AJK和MSR内部网络结构特征,分析其内部网络结构演变规律和吞吐量加权的内部网络结构演变规律。通过BRICS、AJK和 MSR同其他国家间的贸易货运流,分析它们的外部网络结构演变规律和吞吐量加权的外部网络演变规律。
本文选择散货、集装箱、油轮3种船型所构成的网络进行分析。这3类船舶被称为“三大商船”,在全球海运贸易中具有举足轻重的作用。散货船服务于全球铁矿石、煤炭、粮食等大宗商品的海运贸易,以铁矿石为例,铁矿石贸易量在全球铁矿石总产量中的比重接近60%,铁矿石贸易量中海洋运输方式占比接近90%,其他运输方式占比不足10%。集装箱运输一直都是港口贸易物流的重要形式,支撑着全球贸易供应链,以2015年为例,集装箱吞吐量占据了70%以上的国际贸易海运量。石油及成品油的运输主要以海洋运输为主,如目前有超过80%的进口原油是需要海洋运输的,因此,油轮贸易也十分重要。通过这3种类型的船舶在不同国家间吞吐量变化,能够有效反映国家之间、区域之间的贸易变化特征,有助于评估重大事件对重要经济发展区域海运贸易的影响。为此,本文选择这3类船舶对海运网络进行分析。
Fig. 2 The method for spatial-temporal evolution pattern analysis of maritime network

图2 本文研究方法流程图

3.1 网络结构演变分析方法

Timeline作为社会网络演化分析的重要手段,能够有效而快速的发现网络结构的变化、生动描述网络结构演化趋势。该方法以复杂网络理论为基础,节点代表网络中的个体,边表示网络中的个体之间的联系。通过分析任意2个连续时间段间网络结构的差异,得到多个时间片段下的网络演化特征[47]。
传统的Timeline是基于滑动窗口来确定网络的结构变化,考虑海洋运输的长周期特性,本文以季度作为基本时间单元,分析不同季度间的网络演变规律。从季度 S i 到 S i + 1 的网络结构变化由 σ ( S i , S i + 1 ) 来衡量。
对于单个节点,其在 S i 到 S i + 1 的变化定义为：
d ˜ ( s i , s i + 1 ) (v) = log d ( s i ) ( v ) + 1 1 , v ∈ V ( Out     Node ) log 1 d ( s i + 1 ) ( v ) + 1 , v ∈ V ( In     Node ) log d ( s i ) ( v ) d ( s i + 1 ) ( v ) + log ad j s i ( v ) ⋂ ad j s i + 1 ( v ) ad j s i ( v ) ⋃ ad j s i + 1 ( v ) , v ∈ V ( Stable     Node ) （1）
式中： d ˜ ( s i , s i + 1 ) ( v ) 表示基于网络连接结构的节点变化计算值; d ( s i ) ( v ) 表示节点 v 在 S i 时刻的节点度; d ( s i + 1 ) ( v ) 表示节点 v 在 S i + 1 时刻的节点度; ad j s i ( v ) 表示节点 v 在 S i 时刻与之有连接的节点个数（Neighbors）; ad j s i + 1 ( v ) 表示节点 v 在 S i + 1 时刻与之有连接的节点个数（Neighbors）。
网络从 S i 到 S i + 1 变化则可定义为：
σ ˜ ( s i , s i + 1 ) = ∑ ∀ v ∈ V ( OUT ) d ˜ ( s i , s i + 1 ) ( v ) + ∑ ∀ v ∈ V ( IN ) d ˜ ( s i , s i + 1 ) ( v ) + ∑ ∀ v ∈ V ( STABLE ) d ˜ ( s i , s i + 1 ) ( v ) V ( g s i ) ⋃ V ( g s i + 1 ) （2）
式中： σ ˜ ( s i , s i + 1 ) 为基于传统的Timeline的网络变化计算值; V ( g s i ) ⋃ V ( g s i + 1 ) 为 S i 和 S i + 1 时刻网络节点的并集; ∑ ∀ v ∈ V ( STABLE ) d ˜ ( s i , s i + 1 ) ( v ) 表示 S i 到 S i + 1 时刻网络稳定节点变化的累积值; ∑ ∀ v ∈ V ( IN ) d ˜ ( s i , s i + 1 ) ( v ) 为 S i 到 S i + 1 时刻网络新增节点变化的累积值; ∑ ∀ v ∈ V ( OUT ) d ˜ ( s i , s i + 1 ) ( v ) 为Si到Si+1时刻网络消亡节点变化的累积值。

3.2 吞吐量加权的网络结构演变分析方法

吞吐量加权的网络结构演变分析方法不仅考虑网络节点间的连接结构,还引入网络节点间的交通流变化,即吞吐量的变化。从季度 S i 到 S i + 1 的网络变化由 σ ( S i , S i + 1 ) ' 来衡量。对于单个节点,其在 S i 到 S i + 1 的变化定义为：
d ˜ ( s i , s i + 1 ) ( v ) ' = log ∑ C P ( v , v i ) + 1 1 , v ∈ V ( Out     Node ) log 1 ∑ C P ( v , v i ) + 1 , v ∈ V ( In     Node ) log ∑ C P ( v , v i ) ∑ C P ( v , v i ) + log ∑ C P ( v , v i ) ⋂ ∑ C P ( v , v i ) ∑ C P ( v , v i ) ⋃ ∑ C P ( v , v i ) , v ∈ V ( Stable     Node ) （3）
式中： d ˜ ( s i , s i + 1 ) ( v ) ' 表示基于吞吐量加权的节点变化计算; ∑ C P ( v , v i ) 表示节点 v 在 S i 时刻的吞吐量; ∑ C P ( v , v i ) 表示节点 v 在 S i + 1 时刻的吞吐量; ∑ C P ( v , v i ) ⋂ ∑ C P ( v , v i ) 表示节点 v 在 S i 和 S i + 1 时刻吞吐量的交集; ∑ C P ( v , v i ) ⋃ ∑ C P ( v , v i ) 节点 v 在 S i 和 S i + 1 时刻吞吐量的并集。网络从 S i 到 S i + 1 变化则可定义为：
σ ˜ ( s i , s i + 1 ) ' = ∑ ∀ v ∈ V ( OUT ) d ˜ ( s i , s i + 1 ) ( v ) ' + ∑ ∀ v ∈ V ( IN ) d ˜ ( s i , s i + 1 ) ( v ) ' + ∑ ∀ v ∈ V ( STABLE ) d ˜ ( s i , s i + 1 ) ( v ) ' CP ( g s i ) ⋃ CP ( g s i + 1 ) （4）
式中： σ ˜ ( s i , s i + 1 ) ' 表示基于吞吐量加权的网络变化计算值; CP ( g s i ) ⋃ CP ( g s i + 1 ) 表示 S i 和 S i + 1 时刻网络吞吐量的并集; ∑ ∀ v ∈ V ( STABLE ) d ˜ ( s i , s i + 1 ) ( v ) ' 表示 S i 到 S i + 1 时刻网络稳定节点变化的累积值; ∑ ∀ v ∈ V ( IN ) d ˜ ( s i , s i + 1 ) ( v ) ' 表示 S i 到 S i + 1 时刻网络新增节点变化的累积值; ∑ ∀ v ∈ V ( OUT ) d ˜ ( s i , s i + 1 ) ( v ) ' 表示 S i 到 S i + 1 时刻网络消亡节点变化的累积值。

4 研究结果

4.1 散货海运网络时空演变规律

图3表示重要经济发展区域散货型内部网络结构变化（图3（a））、外部网络结构变化（图3（b））、吞吐量加权内部网络结构变化（图3（c））和吞吐量加权外部网络结构变化（图3（d））,纵坐标表示这一季度较上一季度的变化值除以最大变化值进行归一化之后的结果。表1为3个区域的散货海运网络在2013-2016年的动态变化统计分析结果。可以发现：散货船型方面,AJK、BRICS和MSR的外部网络结构变化较为相似,内部网络结构变化差异较大。具体分析如下：
（1）AJK的内部网络结构在2013-2016年没有变化,而MSR的变化明显大于AJK和BRICS。如：MSR在2013-2016年内部网络结构变化的年度均值依次为0.812、0.632、0.636、0.651,BRICS的内部网络结构变化年度均值依次为0.500、0.375、0和0.250,说明“一带一路”倡议的实施期间,MSR的散货型海运网络的内部网络结构发生了明显变化。AJK内部网络变化的均值和标准差都为0,说明AJK内部网络结构没有发生变化,非常稳定。
（2）AJK、BRICS和MSR的外部网络结构变化在每个季度相对上一季度变化都较大,且年度变化均值在0.74以上,说明“一带一路”倡议的实施期间,MSR的散货型海运网络的外部网络结构与BRICS、AJK没有明显差异。
（3）AJK和MSR的吞吐量加权的内部网络结构变化较为相似,都呈现2015-2016年较2013-2014年变化较小的趋势,如：AJK的2015、2016变化均值为0.361、0.25;MSR的2015、2016变化均值为0.241、0.241;AJK的2013、2014变化均值为0.570、0.616;MSR的2013、2014变化均值为0.667、0.628。而BRICS的吞吐量加权的内部网络结构变化与AJK、MSR差异较大,并在2016年第一季度出现峰值。说明“一带一路”倡议的实施期间,AJK和MSR的内部网络中的散货吞吐量发生了较大变化,但在2015-2016年这种变化逐渐减缓。
（4）2013-2016年AJK、BRICS和MSR的吞吐量加权的外部网络结构变化标准差在0.07-0.263之间,总体上每年的第一和第四季度较第二和第三季度变化较大,这3个区域的表现较为相似。说明“一带一路”倡议实施期间,AJK、BRICS和MSR的吞吐量加权的散货型外部网络结构变化没有明显差异。
Fig. 3 evolution pattern of bulk-layer maritime networks between important economic developing zones

图3 重要经济发展区域间散货型海运网络时空演变规律

4.2 集装箱海运网络时空演变规律

在集装箱船型方面,图4表示重要经济发展区域的内部网络结构变化（图4（a））、外部网络结构变化（图4（b））、吞吐量加权内部网络结构变化（图4（c））和吞吐量加权外部网络结构变化（图4（d））,纵坐标表示这一季度较上一季度的变化值除以最大变化值进行归一化之后的结果。表2表示重要经济发展区域集装箱型海运网络在2013-2016年的动态变化统计分析结果,发现：在集装箱船型方面,AJK、BRICS和MSR的外部网络结构的变化明显大于内部网络结构。
（1）AJK的内部网络结构在2013-2016年保持稳定,MSR的内部网络结构变化大于BRICS,如：MSR的年度变化均值依次为0.788、0.590、0.549、0.492,BRICS的年度变化均值依次为0.486、0.390、0.372和0.466。说明“一带一路”倡议实施期间,MSR的集装箱型海运网络的内部网络结构发生了明显变化。
（2）AJK、BRICS和MSR的外部网络结构变化
Tab. 1 Statistics of bulk-layer maritime network dynamics between important economic developing zones from 2013 to 2016

表1 重要经济发展区域间散货型2013-2016年网络结构变化统计分析表

类型 年份 AJK BRICS MSR
均值 标准差 均值 标准差 均值 标准差
散货型内部网络结构变化 2013 0.000 0.000 0.500 0.500 0.812 0.148
2014 0.000 0.000 0.375 0.479 0.632 0.112
2015 0.000 0.000 0.000 0.000 0.636 0.327
2016 0.000 0.000 0.250 0.500 0.651 0.220
散货型吞吐量加权内部网络结构变化 2013 0.570 0.358 0.080 0.005 0.667 0.108
2014 0.616 0.402 0.121 0.037 0.628 0.301
2015 0.361 0.184 0.118 0.105 0.241 0.115
2016 0.257 0.200 0.283 0.478 0.241 0.116
散货型外部网络结构变化 2013 0.926 0.110 0.826 0.038 0.903 0.087
2014 0.808 0.033 0.741 0.041 0.772 0.052
2015 0.905 0.023 0.807 0.178 0.840 0.149
2016 0.847 0.119 0.811 0.097 0.866 0.046
散货型吞吐量加权外部网络结构变化 2013 0.823 0.153 0.790 0.128 0.785 0.109
2014 0.602 0.128 0.680 0.134 0.707 0.263
2015 0.494 0.070 0.587 0.154 0.558 0.126
2016 0.581 0.119 0.674 0.221 0.520 0.210
Fig. 4 Evolution pattern of container-layer maritime networks between important economic developing zones

图4 重要经济发展区域间集装箱型海运网络时空演变规律

Tab. 2 Statistics of container-layer maritime network dynamics between important economic developing zones from 2013 to 2016

表2 重要经济发展区域间集装箱型2013-2016年网络结构变化统计分析表

类型 年份 AJK BRICS MSR
均值 标准差 均值 标准差 均值 标准差
集装箱型内部网络结构变化 2013 0.000 0.000 0.486 0.352 0.788 0.192
2014 0.000 0.000 0.390 0.091 0.590 0.149
2015 0.000 0.000 0.372 0.434 0.549 0.152
2016 0.000 0.000 0.466 0.096 0.452 0.136
集装箱型吞吐量加权内部网络结构变化 2013 0.268 0.130 0.772 0.198 0.475 0.160
2014 0.368 0.447 0.567 0.132 0.542 0.308
2015 0.171 0.120 0.170 0.090 0.265 0.112
2016 0.168 0.022 0.272 0.036 0.229 0.085
集装箱型外部网络结构变化 2013 0.670 0.127 0.867 0.117 0.906 0.083
2014 0.762 0.102 0.646 0.115 0.762 0.066
2015 0.749 0.126 0.610 0.142 0.700 0.095
2016 0.701 0.220 0.732 0.140 0.762 0.125
集装箱型吞吐量加权外部网络结构变化 2013 0.580 0.182 0.723 0.087 0.767 0.117
2014 0.623 0.258 0.683 0.213 0.687 0.220
2015 0.480 0.128 0.468 0.106 0.543 0.134
2016 0.471 0.120 0.501 0.075 0.479 0.040
在2013-2016年都较大,波动均值在0.61以上,但BRICS和MSR的外部结构变化较为相似,AJK与其他2个区域间存在一定差异。
（3）BRICS、AJK和MSR的吞吐量加权的内部网络结构2013-2014年变化明显高于2015-2016年,说明“一带一路”倡议实施期间,AJK和MSR的内部网络中的集装箱吞吐量在2013-2014年发生了较大变化,并且2015-2016年这种变化逐渐减缓。
（4）AJK、BRICS和MSR吞吐量加权的外部海运网络在2013-2016年的年度变化均值呈现下降趋势,但是2014年这些区域的变化标准差最大（AJK为0.258、BRICS为0.213、MSR为0.220）。

4.3 油轮海运网络时空演变规律

在油轮船型方面,图5表示重要经济发展区域内部网络结构变化（图5（a））、外部网络结构变化（图5（b））、吞吐量加权内部网络结构变化（图5（c））和吞吐量加权外部网络结构变化（图5（d））,纵坐标表示这一季度较上一季度的变化值除以最大变化值进行归一化之后的结果。表3表示重要经济发展区域油轮型海运网络在2013-2016年的动态变化统计分析结果。结合图5和表3,可以发现：在油轮船型方面,AJK、BRICS和MSR的内部网络结构变化差异较大,BRICS和MSR的外部海运网络结构变化较为相似,吞吐量加权的内外部网络结构变化都呈现出2015-2016年明显小于2013-2014年的趋势。具体如下：
（1）AJK的内部网络结构在2013-2016年没有变化,BRICS的内部网络结构变化均值在0.347以上,标准差在0.409以上,MSR的内部网络结构变化均值在0.462以上,标准差在0.055以上,而且BRICS的内部网络结构变化各个季度差异较大,规律性不明显。说明：“一带一路”倡议实施期间,AJK、BRICS和MSR的内部网络结构变化差异较大,MSR油轮型内部网络结构一直处于变化较大的状态。
（2）AJK、BRICS和MSR的外部网络结构变化均值分别在0.686-0.840、0.766-0.839、0.695-0.891之间,标准差分别在0.056-0.188、0.049-0.164、0.024-0.116之间,并且各个季度的变化值都较大。“一带一路”倡议实施期间,AJK、BRICS和MSR的油轮型外部网络结构变化都很大。
（3）AJK、BRICS和MSR的吞吐量加权的内部网络结构变化较为相似,2013-2014年的变化的均值和标准差都大于2015-2016年,而且各个季度变化值的差异也较大。“一带一路”倡议实施期间,重要经济发展区域内部网络结构的油轮吞吐量发生了较大变化,并在2015-2016年呈现减缓趋势。
（4）AJK、BRICS和MSR的吞吐量加权外部网络结构变化在2013年各季度变化均值分别为0.823、0.828、0.638,2014年为0.736、0.791、0.534,2015年为0.572、0.538、0.296,2016年为0.556、0.507和0.238。说明AJK、BRICS吞吐量加权外部网络结构变化明显大于MSR,并且3个区域外部网络结构的油轮吞吐量变化在2013-2014年较大,2015-2016年变化呈现减缓趋势。
Fig. 5 Evolution pattern of tanker-layer maritime networks between important economic developing zones

图5 重要经济发展区域间油轮型海运网络时空演变规律

Tab. 3 Statistics of tanker-layer maritime network dynamics between important economic developing zones from 2013 to 2016

表3 重要经济发展区域间油轮型2013-2016年网络结构变化统计分析表

类型 年份 AJK BRICS MSR
均值 标准差 均值 标准差 均值 标准差
油轮型内部网络结构变化 2013 0.000 0.000 0.435 0.432 0.737 0.239
2014 0.000 0.000 0.434 0.453 0.482 0.127
2015 0.000 0.000 0.626 0.421 0.462 0.218
2016 0.000 0.000 0.347 0.409 0.475 0.055
油轮型吞吐量加权内部网络结构变化 2013 0.502 0.354 0.550 0.299 0.638 0.297
2014 0.548 0.439 0.538 0.368 0.534 0.318
2015 0.228 0.142 0.207 0.146 0.296 0.091
2016 0.169 0.069 0.235 0.259 0.238 0.092
油轮型外部网络结构变化 2013 0.840 0.188 0.839 0.139 0.891 0.113
2014 0.825 0.084 0.779 0.049 0.695 0.024
2015 0.804 0.056 0.808 0.164 0.695 0.116
2016 0.686 0.104 0.766 0.085 0.733 0.061
油轮型吞吐量加权外部网络结构变化 2013 0.823 0.131 0.828 0.143 0.638 0.297
2014 0.736 0.255 0.791 0.188 0.534 0.318
2015 0.572 0.106 0.538 0.119 0.296 0.091
2016 0.556 0.101 0.507 0.074 0.238 0.092

5 结论

本文利用AIS挖掘AJK、BRICS和MSR的海运网络时空演变规律,为及时分析和评估国际重大事件对海洋贸易的影响提供了方法支撑,对国家层面的科学决策和战略应对具有深远意义。本文研究结果表明：
（1）MSR散货、集装箱、油轮型的内外部网络结构2013-2016年发生了较大的变化,说明“一带一路”倡议一定程度上影响了MSR的贸易结构。但在2015-2016年MSR 3种类型的吞吐量加权海运网络变化较2013-2014年逐渐减缓,说明此倡议虽然对MSR的贸易结构影响较大,但贸易结构的改变对吞吐量的影响却在减缓。提高MSR的海运贸易吞吐量,提升MSR在海运网络中的贸易地位,仍是当前的发展重点。
（2）BRICS散货、集装箱、油轮型的内部网络结构变化差异明显,说明“一带一路”倡议对BRICS内部不同类型的海运网络结构变化产生的影响不同。BRICS 3种类型的外部海运网络变化与MSR较为相似,反映此倡议一定程度上影响了BRICS的外部贸易结构。
（3）AJK散货、集装箱、油轮型的内部网络结构2013-2016年保持稳定,说明“一带一路”倡议对AJK的内部贸易结构并没有产生显著影响。AJK3种类型的外部海运网络结构变化与MSR存在一定的差异,说明“一带一路”倡议影响了AJK与部分其它国家的贸易结构。
由于缺乏具体的船舶卸货-装货量,本文在计算吞吐量时假设船舶是满载的,后续分析需结合更加精细的数据进一步深入研究。

The authors have declared that no competing interests exist.

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[21]
吴迪,王诺,吴暖,等.主航道中断背景下集装箱海运网络的脆弱性及其对中国的影响[J].地理研究,2017,36(4):719-730.为研究马六甲海峡、苏伊士运河及巴拿马运河等主航道受到攻击时对全球集装箱海运网络的影响,在统计全球集装箱班轮航线及挂靠港的基础上,分别计算三大主航道中断背景下海运网络的网络平均度、孤立节点比例、聚类系数、网络平均距离和网络效率的变化;结合地理特征分析了上述情况对中国集装箱海运产生的影响.研究表明:全球集装箱海运网络对三大主航道的畅通性十分敏感,影响最大的是马六甲海峡,其次是苏伊士运河;当三大主航道遭受攻击时,中国港口与国际其他港口间的网络距离产生了不同程度的增加,网络效率明显下降.为维系中国港口与全球其他港口间运输的畅通,讨论了替代航线,并从保障海运安全的角度提出了相应的对策.

DOI

[ Wu D, Wang N, Wu N, et al.The impact of main channel interruption on vulnerability of container shipping network and China container shipping[J]. Geographical Research, 2017,36(4):719-730. ]

[22]
王诺,董玲玲,吴暖,等.蓄意攻击下全球集装箱海运网络脆弱性变化[J].地理学报,2016,71(2):293-303.为探究近年来全球集装箱海运网络脆弱性的变化趋势,提出了研究网络脆弱性变化度的分析思路和量化方法。基于2004和2014年两个年度的世界主要集装箱班轮公司航线分布数据,将相关港口按节点度大小排序后以1%~10%的比例逐步删除,选择删除前后的网络平均度、网络聚类系数、网络孤立节点比例、网络平均距离和网络效率等特征值的变化率作为量化指标;提出了网络压力测试方法,由此求出各特征值对网络脆弱性的影响权重及贡献值,进而得到了在设定攻击规模内集装箱海运网络脆弱性变化的量化值。研究结果表明：在蓄意攻击下,近10年的全球集装箱海运网络的脆弱性呈变差趋势;当攻击规模为整体网络的10%以内时,网络脆弱性的变差幅度约为6.1%。研究成果对于深化港口地理学研究具有重要意义,其分析思路和方法对其他领域的网络脆弱性变化趋势研究也可提供借鉴。

DOI

[ Wang N, Dong L L, Wu N, et al.The change of global container shipping network vulnerability under intentional attack[J]. Acta Geographica Sinica, 2016,71(2):293-303. ]

[23]
Jiang J, Lee L H, Chew E P, et al.Port connectivity study: An analysis framework from a global container liner shipping network perspective[J]. Transportation Research Part E, 2015,73:47-64.This paper introduces an analysis framework for port connectivity from a global container liner shipping network perspective: it is defined in terms of the impact on the transportation network when the transshipment service is not available at the evaluated port. Under this framework, two models for port connectivity are introduced from transportation time and capacity. Compared with existing measures, the strength of our framework and models is not only that it provides scientific methods to compute port connectivity, but it is able to capture a global effect on how port connectivity contributes to the overall network for given shipping services.

DOI

[24]
田炜,邓贵仕,武佩剑,等.世界航运网络复杂性分析[J].大连理工大学学报, 2007,47(4):605-609.航运系统可以抽象为由港口和航线构成的网络,这个网络的结构与几 何性质对港口与航线的规划和管理具有重要的影响.在对复杂网络的形成、特性和代表性研究成果简要总结的基础上,对马士基航运集团下属的航运网络进行了实证 分析,研究了国际航运网络表现出的小世界与无标度特性,并对其具有的一些不符合典型复杂网络统计特性的现象进行了分析.可为今后政府和企业的航运线路、港 口建设规划及管理提供科学的研究手段和理论支持.

[ Tian W, Deng S G, Wu P J, et al.Analysis of complexity in global shipping network[J]. Journal-Dalian University of Technology, 2007,47(4):605-609. ]

[25]
刘婵娟,胡志华.“海上丝绸之路”海运网络层次体系划分[J].经济地理,2017,37(7):27-32.

[ Liu C J, Hu Z H.Hierarchy system research about the maritime silk road shipping network[J]. Economic Geography, 2017,37(7):27-32. ]

[26]
Xu M Q, Li Z F, Shi Y L, et al.Evolution of regional inequality in the global shipping network[J]. Journal of Transport Geography, 2015,44:1-12.Global shipping is a backbone of the global economy, and as such, it evolves alongside the development of trade and the elaboration of commodity chains. This paper investigates the evolution of regional inequality in the global shipping network by analyzing the changing positions of world regions during the period from 2001 to 2012. This was a period of both prosperity and recession in maritime shipping. Using data on inter-regional flow connections, the positions of seventeen regions in the global shipping network are analyzed in terms of their traffic development, centrality, dominance and vulnerability. The East Asian, Northwest European and Europe Mediterranean regions have consistently held the highest positions, while East African and North African regions have held the lowest positions. By commanding the largest flows in the network, East Asia assumes a dominant position. The Australasian, North American West Coast, Northwest European and Southern African regions show an increasing dependency on East Asia. The analysis also identifies a few emerging regions that have had the highest growth rates in total traffic volume and connectivity for the studied period, namely South American North Coast, South American East Coast, West Africa, Southern Africa and West Asia. The empirical results of this paper supplement existing research on global shipping network evolution. One implication of the analysis is that the traffic growth of East Asia does not imply that, there is an equivalent improvement in its position in the global shipping network. The paper also shows that indicators from network analysis may be used to provide a more nuanced understanding of port-regional development than existing measures based solely on total traffic volume.

DOI

[27]
彭澎,程诗奋,刘希亮,等.全球海洋运输网络健壮性评估[J].地理学报,2017,72(12):2241-2251.

[ Peng P, Cheng S F, Liu X L, et al.The robustness evaluation of global maritime transport networks[J]. Acta Geographica Sinica, 2017,72(12):2241-2251. ]

[28]
Wang C J, Wang J E.Spatial pattern of the global shipping network and its hub-and-spoke system[J]. Research in Transportation Economics, 2011,32(1):54-63.Port system is a research focus of transport geography, and most studies believe carriers are important factors in the development and concentration of the port system. Since the 1990s, carriers have played an important role in organizing the global shipping network and reorganizing the port system. But there isn-檛 a perfect method to evaluate carriers- influence and the roles of each port in the maritime shipping networks. In this paper, we use the monthly schedule table of international carriers to describe and model the spatial pattern of the global shipping network and identify its hub-and-spoke system. The result shows that a hierarchical structure exists in the global shipping network. The North Hemisphere, especially the East Asia and the Southeast Asia, is a dominant region of the worldwide shipping network. East Asia, Southeast Asia, Northeast Europe, and East coast of the USA are the concentration regions of worldwide shipping lines. The ports of Hong Kong, Singapore, Shenzhen, Shanghai, and Kaohsiung etc have advanced capacity for maritime shipping and high potentials for being hub ports in the global shipping network. Today, the worldwide shipping network is transforming from the multi-port calling system to 44 regional hub-and-spoke systems. Meanwhile, the sub-networks with hub ports of Antwerp, Singapore, and Hong Kong have become the most important ones and dominate the whole global shipping network.

DOI

[29]
Vad Karsten C, Brouer B D, Pisinger D.Competitive liner shipping network design[J]. Computers & Operations Research, 2017,87:125-136.We present a solution method for the liner shipping network design problem which is a core strategic planning problem faced by container carriers. We propose the first practical algorithm which explicitly handles transshipment time limits for all demands. Individual sailing speeds at each service leg are used to balance sailing speed against operational costs, hence ensuring that the found network is competitive on both transit time and cost. We present a matheuristic for the problem where a MIP is used to select which ports should be inserted or removed on a route. Computational results are presented showing very promising results for realistic global liner shipping networks. Due to a number of algorithmic enhancements, the obtained solutions can be found within the same time frame as used by previous algorithms not handling time constraints. Furthermore, we present a sensitivity analysis on fluctuations in bunker price which confirms the applicability of the algorithm.

DOI

[30]
Fahmiasari H, Parikesit D.Container shipping network efficiency comparison in indonesia: Nusantara Pendulum and Sea Tollway[J]. The Asian Journal of Shipping and Logistics, 2017,33(2):79-80.The disparity of the trading activities in Indonesia has triggered Indonesia Port Corporation to come up with the plan of Nusantara Pendulum, the future network of container shipping in Indonesia. The Sea Tollway plan is the modification of Nusantara Pendulum that is also envisioned by Indonesia President for Indonesia future backbone container plan. Both networks were designed to increase Indonesia container shipping flow from west to east and vice versa like a pendulum, thus results the reduced transport cost. This paper depicts the comparison of network efficiency analysis between these networks: Nusantara Pendulum, Sea Tollway, and the existing network. Two main methods that are used are Nagurney-Qiang and Jenelius-Peterson-Mattson. Mainly, these two methods use the efficiency calculation by considering parameters of demand (goods flow) between each region and maritime transportation cost. It is concluded afterward that: 1) Sea Tollway is 8% more efficient than Nusantara Pendulum; 2) Sea Tollway and Nusantara Pendulum are ten times more efficient than current network; 3) Eastern ports in Indonesia (Bitung and Sorong) urgently requisite a development.

DOI

[31]
Lee H, Boile M, Theofanis S, et al.Modeling the oligopolistic and competitive behavior of carriers in maritime freight transportation networks[J]. Procedia - Social and Behavioral Sciences, 2012,54:1080-1094.The paper presents a novel multi-level hierarchical approach which models the oligopolistic and competitive behavior of carriers and their relationships in maritime freight transportation networks. With the merger of the carriers- industry and some dominant carriers in a shipping market, the carrier competition frequently exhibits an oligopolistic nature. Three types of carriers are considered herein; ocean carriers, land carriers and port terminal operators. The oligopolistic ocean carriers, land carriers and port terminal operators compete with each other in their pricing and routing decisions, respectively. The carriers determine service charges and delivery routes at different parts of the multimodal freight network, having hierarchical interactions. In a game theoretic approach, ocean carriers are regarded as the leaders in an oligopoly shipping market. Port terminal operators are the followers of ocean carriers as well as the leaders of land carriers. For the individual carrier problems, Nash equilibrium is used to find the optimal decisions for which each carrier obtains the greatest profit. A three-搇evel model is formulated to capture the interactions among different types of carriers. A numerical example is presented to demonstrate the validity and capability of the model.

DOI

[32]
Song D W, Lee P T W. Maritime logistics in the global supply chain[J]. International Journal of Logistics Research and Applications 2009,12(2):83-84.A comparative study on financial positions of shipping companies in Taiwan and Korea using entropy and grey relation analysis. Expert Systems with Applications 39:, pages 5649-5657.

DOI

[33]
Davarzani H, Fahimnia B, Bell M, et al.Greening ports and maritime logistics: A review[J]. Transportation Research Part D, 2016,48:473-487.This paper aims to examine the past and present research on ‘green ports and maritime logistics’ in order to identify established research streams and fertile research areas with potential for future investigations. Using rigorous bibliometric and network analysis tools, the paper completes a systemic mapping of the existing literature and identifies the key investigators, collaboration patterns, research clusters and interrelationships, and the “seminal research areas” that have provided the field with the foundational knowledge, concepts, theories, tools, and techniques. Major articles within each seminal research area are also identified. This will allow new researchers to quickly build understanding in a particular sub-field by reading these major articles. The findings obtained from the evolution of seminal research areas over time are important from both research and practice perspectives and can help the field grow in many dimensions.

DOI

[34]
Stopford M.Maritime economics[M]. London and New York: Routledge, 2009.

[35]
Akhtar M J, Utne I B.Human fatigue's effect on the risk of maritime groundings A Bayesian Network modeling approach[J]. Safety Science, 2014,62:427-440.The article introduces a general method for developing a Bayesian Network (BN) for modeling the risk of maritime ship accidents. A BN of human fatigue in the bridge management team and the risk of ship grounding is proposed. The qualitative part of the BN has been structured based on modifying the Human Factor Analysis and Classification System (HFACS). The quantitative part is based upon correlation analysis of fatigue-related factors identified from 93 accident investigation reports. The BN model shows that fatigue has a significant effect on the probability of grounding. A fatigued operator raises the probability of grounding of a large ship in long transit with 23%. Compared to the two watch system (6–6 and 12–12), the 8–4–4–8 watch system seems to generate the least fatigue. However, when manning level, which is influenced by the various watch schemes, is taken into account, the two watch system is preferable, leading to less fatigue and fewer groundings. The strongest fatigue-related factors related to top management are vessel certifications, manning resources, and quality control.

DOI

[36]
Hänninen M, Banda O A V, Kujala P. Bayesian network model of maritime safety management[J]. Expert Systems with Applications, 2014,41:7837-7846.This paper presents a model of maritime safety management and its subareas. Furthermore, the paper links the safety management to the maritime traffic safety indicated by accident involvement, incidents reported by Vessel Traffic Service and the results from Port State Control inspections. Bayesian belief networks are applied as the modeling technique and the model parameters are based on expert elicitation and learning from historical data. The results from this new application domain of a Bayesian network based expert system suggest that, although several its subareas are functioning properly, the current status of the safety management on vessels navigating in the Finnish waters has room for improvement; the probability of zero poor safety management subareas is only 0.13. Furthermore, according to the model a good IT system for the safety management is the strongest safety-management related signal of an adequate overall safety management level. If no deficiencies have been discovered during a Port State Control inspection, the adequacy of the safety management is almost twice as probable as without knowledge on the inspection history. The resulted model could be applied to performing several safety management related queries and it thus provides support for maritime safety related decision making.

DOI

[37]
Bezgodov A, Esin D.Complex network modeling for maritime search and rescue operations[J]. Procedia Computer Science, 2014,29:2325-2335.This paper introduces a complex network model for collective behavior of floating drifters at sea. Direct simulat ion method for floating objects on irregular waves is used to express the network dynamics. The features of co llect ive behavior (such as the network destruction) are considered. The model is applied to study of efficiency of maritime search and rescue operations at sea.

DOI

[38]
Fransoo J C, Lee C Y.The critical role of ocean container transport in global supply chain performance[J]. Production & Operations Management, 2013,22(2):253-268.With supply chains distributed across global markets, ocean container transport now is a critical element of any such supply chain. We identify key characteristics of ocean container transport from a supply chain perspective. We find that unlike continental (road) transport, service offerings tend to be consolidated in few service providers, and a strong focus exists on maximization of capital intensive resources. Based on the characteristics of ocean container transport as part of global supply chains, we list a number of relevant and challenging research areas and associated questions.

DOI

[39]
Wilmsmeier G, Notteboom T.Determinants of liner shipping network configuration: A two-region comparison[J]. Geojournal, 2011,76(3):213-228.The worldwide network of container transport services is becoming increasingly diffuse. The different strategies of shipping lines, the balance of power between shipping lines and shippers and constraints related to inland transportation all have a potential impact on the development of maritime shipping networks. Moreover, strategic alliances between the port and the shipping industry, which have both been driven by strong concentration processes and vertical integration, have a profound influence on the maritime network structure and also on the grade of integration of a region in the global maritime transport network. This paper seeks to understand the evolution of maritime networks in and between two differently developed regions. The focus is on the trade route and networks between the West Coast of South America and Northern Europe. The paper analyses the network structures and the behaviour of shipping lines in different economic contexts and port systems. Current and historical developments in the two regions under study have led to their relative position within the global maritime network and illustrate the potential implications of being peripheral or central in this network. The empirical results are compared with known strategies of shipping lines. The authors aim to answer the question of how far the configuration of hinterlands determines calling patterns and if strategic alliances and vertical integration reduce footloose behaviour of shipping lines. Further, we discuss how far, under the current configuration, shipping lines influence port development, and also the reverse situation of how far port accessibility and performance influence maritime network developments. The two region approach provides insights on the constraining factors of maritime network development between two differently developed regions and the associated implications for trade development.

DOI

[40]
Hägerstrand T.The Propagation of Innovation Waves[M]. Lund: Royal University of Lund, 1952.

[41]
Notteboom T E.The peripheral port challenge in container port systems[C]. International Maritime Transport: Perspectives, 2005:173-188.

[42]
Wilmsmeier G, Monios J, Pérez-Salas G.Port system evolution: The case of latin america and the caribbean[J]. Journal of Transport Geography, 2014,39:208-221.Results show that the manufacturing of strategic locations can be successful and may have driven the emergence of secondary ports in the LAC system. This finding demonstrates how path dependence can be challenged by new developments, the identification and success of which are nevertheless contingent on factors such as the first mover advantage, port planning regimes and diversification of port roles. The paper identifies some of the key factors influencing the transition of a port system from concentration at a few dominant ports to a deconcentrated system of primary and secondary ports, which can be applied to other port systems in future research.

DOI

[43]
Monios J, Wilmsmeier G.Giving a direction to port regionalization[J]. Transportation Research Part A: Policy and Practice, 2012,46(10):1551-1561.As theoretical approaches to port development have advanced over the years, the role of the inland terminal has attracted increasing focus, particularly under the framework of port regionalisation. This paper will explore port regionalisation in different contexts through a greater focus on the drivers and direction of a number of inland terminal development strategies. The paper will build on previous work by combining inland terminal taxonomies and the theory of directional development with traditional port development models. Regionalisation strategies will be compared and contrasted through examples derived from field work undertaken in Europe and the USA. The results contribute towards a disaggregation of the process of port regionalisation, revealing different levels of integration and cooperation observed in different location splitting models. In this way, different strategies will be elucidated, enabling further exploration and more nuanced understanding of the institutional aspects of spatial development.

DOI

[44]
Monios J; Wilmsmeier G.Port-centric logistics, dry ports and offshore logistics hubs: Strategies to overcome double peripherality[J]. Maritime Policy & Management, 2012,39(2):207-226.

[45]
Kosowska-Stamirowska Z, Ducruet C, Rai N.Evolving structure of the maritime trade network: Evidence from the Lloyd’s Shipping Index (1890-2000)[J]. Journal of Shipping and Trade, 2016,1:10.Over 90 % of the world trade volumes is being carried by sea nowadays. This figure shows the massive importance of the maritime trade routes for the world economy. However, the evolution of their structure over time is a white spot in the modern literature. In this paper we characterise and study topological changes of the maritime trade network and how they translate into navigability properties of this network. In order to do so we use tools from Graph Theory and Computer Science to describe the maritime trade network at different points in time between 1890 and 2000, based on the data on daily movements of ships. We also propose two new measures of network navigability based on a random walk procedure: random walk discovery and escape difficulty . By studying the maritime network evolution we find that it optimizes over time, increasing its navigability while doubling the number of active ports. Our findings suggest that unlike in other real world evolving networks studied in the literature up to date, the maritime network does not densify over time and its effective diameter remains constant.

DOI

[46]
吴斌,王柏,杨胜琦.基于事件的社会网络演化分析方法[J].软件学报,2011,22(7):1488-1502.

[ Wu B, Wang B, Yang S Q.Framework for tracking the event-based evolution in social networks[J]. Journal of Software, 2011,22(7):1488-1502. ]

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