Spatial heterogeneity of ports in the global maritime network detected by weighted ego network analysis

Liu, Chengliang; Wang, Jiaqi; Zhang, Hong

doi:10.1080/03088839.2017.1345019

Cited by 63 publications

(31 citation statements)

References 67 publications

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“…[ Figure 5] Secondly, Table 1 provides an overview of the topological structure of the three ego networks (see Appendix 1 for the definition of network indices), in order to better focus on the subtle mix between local and global scales underlying nodes' situation (Liu et al, 2018). A striking result is the loss of numerous nodes in the immediate aftermath of the shock: between the previous week and the next one, Kobe lost 54% of its neighbors, New York 25% and New Orleans 41%.…”

Section: Network Evolutionmentioning

confidence: 99%

Disruptions in Spatial Networks: a Comparative Study of Major Shocks Affecting Ports and Shipping Patterns

Rousset

Ducruet

2019

Netw Spat Econ

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In this research, the effect of local exogenous shocks on seaports and maritime networks is assessed throughout three case-studies. The Hanshin-Awaji earthquake, the 9/11 World Trade Center attack and hurricane Katrina triggered a shock on Kobe, New York and New Orleans respectively and led to temporary port failures. A global database on vessel movements is computed to gauge the intensity, duration and scope of these shocks with descriptive statistics. Port-centered analyses of the disrupted ports and their geographic as well as topological neighbors are conducted to assess the resilience of local port systems using traffic volumes, distances, and graph-theoretical methods at various time scales, from daily to yearly evolutions. Main results demonstrate certain similarities between the three cases given the importance of traffic specialization and of the spatial range of disruption.

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Section: Network Evolutionmentioning

confidence: 99%

Disruptions in Spatial Networks: a Comparative Study of Major Shocks Affecting Ports and Shipping Patterns

Rousset

Ducruet

2019

Netw Spat Econ

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“…Secondly, the problems here discussed are neither old nor limited to second tier journals. On the contrary, it is easy to find examples of papers published in this same year [29,31] or in very prestigious journals, both in the statistical physics and in transportation communities.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, [30], while analysing the evolution of global liner shipping networks between years 1996 and 2006, reports an exponent varying from −1.351 to −1.293 without describing how these values were obtained. Reference [31] concludes that the maritime network is scale-free without any calculation at all: "nearly 80% of nodes account for less than 20% of the respective accumulative values of the degree of the nodes, just like scale-free properties". In the analysis of urban street networks, [32] states that "the investigation of how well the fat-tailed distribution can fit power law in comparing with other distributions (e.g., log-normal and exponential) shows that no significant evidence is found for scale-free feature in the dual space"; nevertheless no statistical evidence of any kind is provided.…”

Section: Common Pitfalls and Misleadingmentioning

confidence: 99%

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Zanin

Sun

Wandelt

2018

Journal of Advanced Transportation

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The introduction of complex network concepts in the study of transportation systems has supposed a paradigm shift and has allowed understanding different transport phenomena as the emergent result of the interactions between the elements composing them. In spite of several notable achievements, lurking pitfalls are undermining our understanding of the topological characteristics of transportation systems. In this study, we analyse four of the most common ones, specifically related to the assessment of the scale-freeness of networks, the interpretation and comparison of topological metrics, the definition of a node ranking, and the analysis of the resilience against random failures and targeted attacks. For each topic we present the problem from both a theoretical and operational perspective, for then reviewing how it has been tackled in the literature and finally proposing a set of solutions. We further use six real-world transportation networks as case studies and discuss the implications of these four pitfalls in their analysis. We present some future lines of work that are stemming from these pitfalls and that will allow a deeper understanding of transportation systems from a complex network perspective.

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“…Social network analysis is a useful tool for investigating various kinds of networks [56,57]. It can offer an overall picture and describe the details of a technology transfer network from the perspective of patent transactions.…”

Section: Social Network Analysismentioning

confidence: 99%

Spatial Dynamics of Intercity Technology Transfer Networks in China’s Three Urban Agglomerations: A Patent Transaction Perspective

2019

Self Cite

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Technology transfer has become a vital pipeline for acquiring external knowledge. The purpose of this paper is to portray the spatial dynamics of intercity technology transfer networks in China’s three urban agglomerations based on patent right transaction data from 2008 to 2015. The integration of social networks and spatial visualization is used to explore spatial networks and influencing variables of the networks. The results demonstrate that Beijing, Shanghai, and Shenzhen are emerging as hubs in the three urban agglomerations. The spatial distributions of degree and weighted degree are significantly heterogeneous and hierarchical. The larger cities play the role of a knowledge and technology incubator, highly related to their economic scale, research and development (R&D) input, and innovation output. The evolution of intercity technology linkages is driven by the networking mechanisms of preferential attachment, hierarchical and contagious diffusion, path dependence, and path breaking. Moreover, we found that the geographical proximity and technology gaps are determinants of the strength of intercity technology linkages. As a result, it has been discovered that the network in the Beijing–Tianjin–Hebei agglomeration is organized in a tree network, while the Yangtze River Delta features a polycentric network and the Pearl River Delta has multi-star characteristics.

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Spatial heterogeneity of ports in the global maritime network detected by weighted ego network analysis

Cited by 63 publications

References 67 publications

Disruptions in Spatial Networks: a Comparative Study of Major Shocks Affecting Ports and Shipping Patterns

Disruptions in Spatial Networks: a Comparative Study of Major Shocks Affecting Ports and Shipping Patterns

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Spatial Dynamics of Intercity Technology Transfer Networks in China’s Three Urban Agglomerations: A Patent Transaction Perspective

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