Data Strategy and Data Design for Unclassified Maritime Domain Awareness
Abstract
The Naval Postgraduate School Information Sciences Department is executing a multi-year campaign of integrated thesis research to explore issues, and develop ideas and concepts of operations (CONOPS), related to formalizing unclassified maritime domain awareness (MDA). This is the third thesis in this campaign. The first thesis in 2016 developed the CONOPS to leverage unclassified, sharable commercial satellite imagery in support of the United States Southeast Asia Maritime Security Initiative. A second thesis in 2017 developed an experiment plan for use in SEACAT 2018, a multilateral MDA information-sharing exercise held in the South China Sea region. This thesis leverages the recommendations from the previous theses to evaluate the effectiveness of SeaVision as compared to other platforms, and examine the usefulness of social network analysis (SNA) to uncover dark networks in MDA. To evaluate SeaVisions effectiveness, the authors use a quantitative approach that examines the variables for 59 sanctioned vessels designated by the UN for the Democratic Peoples Republic of Korea. To examine the usefulness of SNA to uncover dark networks, they use the same set of sanctioned vessels, and 67 non-sanctioned ones associated with any of the 271 companies affiliated with the sanctioned vessels. The authors further propose an information strategy that can uncover the dark network of an unclassified MDA so that this information can be used by and shared with international partners.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2019
- Accession Number
- AD1073653
Entities
People
- Soon H. Ng
- Vincent A. Mosley
Organizations
- Naval Postgraduate School