<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This research focused on the identification and tracking of subgroups of vessels of interest, owners, operators, ports, cargoes, and specific activities associated with artificial reef enhancement and construction in the South China Sea. Historical automated information system (AIS) tracks and current maritime databases were used to develop sociogram depictions of the gray (licit but only partially transparent) maritime network that connects these nodes (ships, events, organizations, ports, activities). Social network matrices were dynamically updated by open source databases to provide insights into real-time awareness and tracking for operational purposes.</p>
<p>The maritime network data set was populated by, and dynamically updated through, the integration of unclassified data using algorithms developed as part of the research. Longitudinal topographic metrics  –  average degree, average clustering coefficient, and centralization  –  were used to analyze the multi-mode (e.g., ship to ship, ship to owners/operators, owner/operators to owner/operators, ships to locations) relationships within the gray maritime network. Additionally, the network of ports and reefs in the area of operations was mapped and insights were gained by leveraging directed centrality measures  –  hubs and authorities  –  connecting them.</p>
</abstract>

This research focused on the identification and tracking of subgroups of vessels of interest, owners, operators, ports, cargoes, and specific activities associated with artificial reef enhancement and construction in the South China Sea. Historical automated information system (AIS) tracks and current maritime databases were used to develop sociogram depictions of the gray (licit but only partially transparent) maritime network that connects these nodes (ships, events, organizations, ports, activities). Social network matrices were dynamically updated by open source databases to provide insights into real-time awareness and tracking for operational purposes.
The maritime network data set was populated by, and dynamically updated through, the integration of unclassified data using algorithms developed as part of the research. Longitudinal topographic metrics  –  average degree, average clustering coefficient, and centralization  –  were used to analyze the multi-mode (e.g., ship to ship, ship to owners/operators, owner/operators to owner/operators, ships to locations) relationships within the gray maritime network. Additionally, the network of ports and reefs in the area of operations was mapped and insights were gained by leveraging directed centrality measures  –  hubs and authorities  –  connecting them.

Mapping Gray Maritime Networks

Connections

This work is licensed under the Creative Commons Attribution 4.0 International License.

running-head

This research focused on the identification and tracking of subgroups of vessels of interest, owners, operators, ports, cargoes, and specific activities...

Mapping Gray Maritime Networks

Publié en ligne: 23 oct. 2019

Pages: 1 - 12

DOI: https://doi.org/10.21307/connections-2019-006

Mots clés
Topographic/sociogram depictions, Maritime domain awareness, Real-time awareness and tracking, Gray maritime network, Artificial reef enhancement, South China Sea, Social (maritime) network analysis

© 2019 Wayne Porter et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mapping Gray Maritime Networks

Publié en ligne: 23 oct. 2019

Pages: 1 - 12

DOI: https://doi.org/10.21307/connections-2019-006

Mots clésTopographic/sociogram depictions, Maritime domain awareness, Real-time awareness and tracking, Gray maritime network, Artificial reef enhancement, South China Sea, Social (maritime) network analysis

© 2019 Wayne Porter et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Topographic/sociogram depictions, Maritime domain awareness, Real-time awareness and tracking, Gray maritime network, Artificial reef enhancement, South China Sea, Social (maritime) network analysis