Foreland determination for containership and general cargo ports in Europe (2007–2011)

Seoane, María Jesús Freire; Laxe, Fernando González; Montes, Carlos Pais

doi:10.1016/j.jtrangeo.2013.03.003

Cited by 33 publications

(8 citation statements)

References 28 publications

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“…Some of them discussed the effect of node aggregation at different scales (Tsiotas and Polyzos, 2018;Wang et al, 2019a), while others studied the global maritime network with the world region as the unit of analysis (Tran and Haasis, 2014;Li et al, 2015;Xu et al, 2015). A large body of literature focused on ranking ports by centrality indicators, introducing more or less novelty (Wang and Cullinane, 2008;Lam and Yap, 2011;Montes et al, 2012;Cullinane and Wang, 2012;Doshi et al, 2012;Freire Seoane et al, 2013;Wang and Cullinane, 2014;Kang et al, 2014;Bartholdi et al, 2016). Certain works, albeit not always referring to "complex networks" explicitly, pushed the analysis of port centrality one step further, by examining the interrelation between centrality and actual port throughput (Kim and Lu, 2015;Wang and Cullinane, 2016;Kang and Woo, 2017), combining network analysis with multiple regression and data envelopment analysis.…”

Section: From Graph Theory To Complex Networkmentioning

confidence: 99%

The geography of maritime networks: A critical review

Ducruet

2020

Journal of Transport Geography

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Despite early cartographical and graph-theoretical analyses of maritime flows in the 1940s and 1960s, it is only from the 2000s onwards that maritime network analysis had grown apace, backed by newly available shipping data, increased computational power, and renewed conceptual frameworks to study networks in general. The evolution of maritime network analysis, in geography and other sciences, is marked by a wide diversity of methods and themes, which we classify into three main parts. We first present studies looking at maritime flows in an abstract space, focusing on operational, statistical, or managerial aspects where navigation, graph structure, and firms' strategies are the key concerns. Second, we review researches where maritime flows and networks are markers and vectors of wider geo-economic structures and dynamics, such as regional inequalities and areas of dominance. Lastly, maritime networks have also been considered as integral parts of territories and wider chained systems, such as urban networks, regional networks, and coupled networks. We conclude that network analysis and maritime transport still share many uncovered areas and discuss potential research pathways for future works.

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Section: From Graph Theory To Complex Networkmentioning

confidence: 99%

The geography of maritime networks: A critical review

Ducruet

2020

Journal of Transport Geography

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“…Maritime traffic has been mapped using AIS data with respect to ports in just a few papers. Seoane et al [40] determined the foreland of container and general cargo ports in Europe; Jia et al [41] investigated Norwegian port connectivity in terms of vessel visits, vessel sizes, and cargo sizes; Yu et al [42] showed the different layers that Chinese ports are linked in and; based on the use of AIS data, Jia et al [43] proposed an algorithm for aggregating, mapping and distributing real-time trade flows between the major ports of the world. There has been even less port-based research using AIS data that has focussed on areas that are more directly relevant to the objectives of this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Impact of Disruptive Events on Port Performance and Choice: The Case of Gothenburg

Svanberg

Holm

Cullinane

2021

JMSE

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This paper assesses the impact of a major disruptive event at the port of Gothenburg, Scandinavia’s largest container port. Automatic Identification System (AIS) data is analyzed, in combination with official port statistics on container handling in the four main container ports in Sweden, from 2014–2018. Particular attention is paid to the relationship between container volumes handled and calculated performance metrics at the specific times of the intense labour dispute at the port of Gothenburg during the periods Q2 (2016) and Q4 (2016)–Q2 (2017). The paper concludes that the decline in container volumes handled at Gothenburg over the period is specifically due to fewer ships calling at the port following each of the intense periods of the labour dispute. It is also concluded that the effect on competitor ports in the region were significant in terms of both increased volumes of gateway container traffic and the resulting short-term and medium term impacts on both port user profiles and port efficiency levels.

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“…In these studies, the hierarchy is not based on the volume of traffic handled at the ports but on their position in the network. The most common network indicators used for the ranking of ports are degree centrality (number of connections of a port) in order to assess its connectivity and betweenness centrality (number of shortest paths between any pair of ports in the network that pass through a specific port) in order to evaluate how centrally is a port placed in the network (Cisic et al, 2007;Ducruet et al, 2009b;Ducruet et al, 2010b;Ducruet and Notteboom, 2012a,b;Gonzalez et al, 2012;Pais-Montes et al, 2012;Freire et al, 2013;Tovar et al, 2015;Ducruet, 2013a,b;Kang and Woo, 2017).…”

Section: Formation Of Hierarchies In Empirical Applicationsmentioning

confidence: 99%

“…• To identify how ports are positioned in maritime networks, global or regional (Cisic et al, 2007;Ducruet et al, 2009b;Ducruet et al, 2010b;Ducruet and Notteboom, 2012a,b;Gonzalez et al, 2012;Pais-Montes et al, 2012;Freire et al, 2013;Tovar et al, 2015;Ducruet, 2013a,b;Kang and Woo, 2017). The most common indicators that are used are the degree (number of connections of a port) in order to assess its connectivity and the betweenness centrality (number of shortest paths between any pair of ports in the network that pass through a specific port) in order to evaluate how centrally is a port placed in the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of container throughput in the European port system-analysis of container liner shipping networks

Stamatopoulos

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(English) This thesis records the evolution of the container throughput dynamics of the European port system from 2001-2020, analyses the dynamics of the European container port hierarchy with methodologies that have not been used in that context, applies network analysis on the liner shipping networks of individual shipping companies and proposes a model for short-term forecasts of container throughput and their accompanying prediction intervals. Chapter 2 explores the dynamics of container throughput in the European port system from 2001-2020 at the level both of groups of ports and individual ports (Notteboom, 2010). The evolution of the container throughput is examined by the application of concentration indicators. The market share of port groups or ports in the total throughput of the system, the normalized Herfindahl–Hirschman Index and a customized form of shift-share analysis are used for this purpose. The influence of the two crises that occurred during the examined period, the global financial crisis of 2009 and the health crisis of 2020, on the container throughput dynamics is explored. The dynamics of the European container port hierarchy are analyzed in chapter 3 by the application of 3 methodologies. First, two types of rank-size models are used in order to investigate the formation of the hierarchies and the dynamics of container throughput distribution. Second, a methodology based on the principals of Analytic Hierarchy Process (AHP) combined with an allometric growth model is introduced in order to explore the relative growth of large ports as a group upon groups of medium-sized and small ports. Third, a Markov chain modeling is applied in order to investigate the mobility of ports within the hierarchy of the European container port system. The dataset that is used in all the methodologies consists of the annual container port throughputs in TEUs of the top-100 European container ports for every year between 2001 and 2020. Chapter 4 analyzes the container liner shipping networks of the liner operators of Maersk and COSCO Shipping in 2001, 2007 and 2010. First, the whole network of the carriers is analyzed through global network indicators and visualization of the networks. Second, the shipping networks are investigated for the presence of complex network properties by fitting a power law distribution to the degree distribution of the networks. Third, the positions of the ports in the companies’ networks are explored by calculating their degree and betweenness centrality. Fourth, the weighted degree of the ports and the strongest inter-port links are presented using the frequency (weekly calls) as weight of the links. Finally, the chapter focuses on the regional networks of the companies and their evolution. A combined SARIMA-(G) ARCH model is proposed in chapter 5 in order to produce short-term forecasts of container throughput and prediction intervals of these forecasts. The uncertainty of the forecasts is expressed by the prediction intervals associated with the point forecasts. The point forecasts are generated form the SARIMA part of the model and the prediction intervals from the ARCH or GARCH part. ARCH/GARCH models are used in order to model and forecast the volatility of container throughput time series. The combined SARIMA-(G)ARCH model is applied to monthly container throughput data of the port of Barcelona in order to produce short-term point forecasts and its accompanying prediction intervals. (Español) Esta tesis a) registra la evolución de la dinámica del tráfico de contenedores del sistema portuario europeo desde 2001-2020 b) analiza la dinámica de la jerarquía portuaria de contenedores europea con metodologías que no se han utilizado en ese contexto c) aplica el análisis de redes en las redes de transporte marítimo de línea de compañías navieras individuales y d) propone un modelo de pronósticos del movimiento de contenedores a corto plazo y de los intervalos de predicción correspondientes. El capítulo 2 explora la dinámica del tráfico de contenedores en el sistema portuario europeo entre 2001 y 2020, tanto a nivel de conjuntos de puertos como de casos individuales (Notteboom, 2010). La evolución del movimiento de contenedores se examina mediante la aplicación de indicadores de concentración. Para este propósito, se utiliza la cuota del mercado de los grupos portuarios o puertos en el rendimiento total del sistema, el índice Herfindahl-Hirschman normalizado y una forma de “shift-share” análisis adaptada. Se explora también la influencia en la dinámica del tráfico de contenedores de los dos crisis que ocurrieron durante el periodo examinado, es decir la crisis financiera mundial de 2009 y la crisis de salud de 2020. En el capítulo 3 se analiza la dinámica de la jerarquía portuaria europea de contenedores mediante la aplicación de 3 metodologías. En primer lugar, se utilizan dos tipos de modelos de tamaño de rango para investigar la formación de las jerarquías y la dinámica de la distribución del rendimiento de los contenedores. En segundo lugar, se introduce una metodología basada en los principios del Proceso Jerárquico Analítico (AHP) combinado con un modelo de crecimiento alométrico para explorar el crecimiento relativo de los puertos grandes como grupo sobre grupos de puertos medianos y pequeños. En tercer lugar, se aplica un modelo de cadena de Markov para investigar la movilidad de los puertos dentro de la jerarquía del sistema portuario europeo de contenedores. El conjunto de datos que se utiliza en todas las metodologías consiste en el rendimiento anual de los puertos de contenedores en TEU de los 100 principales puertos de contenedores europeos cada año entre 2001 y 2020. El capítulo 4 analiza las redes de transporte marítimo de contenedores de línea de los operadores Maersk y COSCO Shipping en 2001, 2007 y 2010. Primero, toda la red de los operadores se analiza a través de indicadores de red global y visualización de las redes. En segundo lugar, la red de transporte marítimo se investiga en busca de propiedades de red complejas ajustando una distribución de ley de la potencia a la distribución de grados de las redes. En tercer lugar, se exploran las posiciones de los puertos en las redes de las empresas calculando su centralidad de grado y su centralidad de intermediación. En cuarto lugar, se presenta el grado ponderado de los puertos y los enlaces interportuarios más fuertes utilizando la frecuencia (llamadas semanales) como peso de los enlaces. Finalmente, el capítulo se centra en las redes regionales de las empresas y su evolución. En el capítulo 5 se propone un modelo combinado SARIMA-(G) ARCH para producir pronósticos a corto plazo del movimiento de contenedores e intervalos de predicción de estos pronósticos. La incertidumbre de los pronósticos se expresa mediante los intervalos de predicción asociados con los pronósticos puntuales. Los pronósticos puntuales se generan a partir de la parte SARIMA y los intervalos de predicción a partir de la parte ARCH o GARCH. Los modelos ARCH/GARCH se utilizan para que se modele y pronostique la volatilidad de las series temporales de rendimiento de contenedores. El modelo SARIMA-(G)ARCH se aplica a los datos mensuales de tráfico de contenedores del puerto de Barcelona para producir pronósticos puntuales a corto plazo y los intervalos de predicción que lo acompañan.

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Foreland determination for containership and general cargo ports in Europe (2007–2011)

Cited by 33 publications

References 28 publications

The geography of maritime networks: A critical review

The geography of maritime networks: A critical review

Assessing the Impact of Disruptive Events on Port Performance and Choice: The Case of Gothenburg

Modeling of container throughput in the European port system-analysis of container liner shipping networks

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