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dc.contributor.advisorGera, Ralucca
dc.contributor.advisorAlderson, David L. Jr.
dc.contributor.authorCline, Rachel
dc.date.accessioned2018-08-24T22:35:02Z
dc.date.available2018-08-24T22:35:02Z
dc.date.issued2018-06
dc.identifier.urihttp://hdl.handle.net/10945/59639
dc.descriptionApproved for public release. distribution is unlimiteden_US
dc.description.abstractThis thesis considers the problem of how to infer a social network of entities based solely on periodic location information as they move in space and time. Specifically, we consider social networks implied by vessel traffic within the South China Sea as captured by Automatic Identification System data consisting of vessel identifier, position coordinates (latitude and longitude), heading, speed, and other information about its course. We create customized data structures in the Python programming language and implement an algorithm to quickly and efficiently create a social network of entities based on proximity parameters for both space and time. As we see from our analysis, there is no single network depiction of our data. Rather, the topology is largely influenced by the parameters we use to define a connection between entities. Because we can efficiently query the data, we quickly draw conclusions about the most active time of day and most active location within the region. From this, we are able to look into specific entities and examine the relationships, activities, and behaviors that we wish to understand.en_US
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleUNDERSTANDING AUTOMATIC IDENTIFICATION SYSTEM DATA AS APPLIED TO SOCIAL NETWORK RELATIONSHIPS AND ACTIVITIESen_US
dc.typeThesisen_US
dc.contributor.secondreaderHuddleston, Samuel H.
dc.contributor.departmentOperations Research (OR)
dc.contributor.departmentApplied Mathematics (MA)
dc.subject.authorAutomatic Identification Systemen_US
dc.subject.authornetwork scienceen_US
dc.subject.authorcriminal organizationen_US
dc.subject.authorSouth China Seaen_US
dc.subject.authorsocial network inferenceen_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceCaptain, United States Marine Corpsen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.nameMaster of Science in Applied Mathematicsen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.disciplineApplied Mathematicsen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.identifier.thesisid30002


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