UNDERSTANDING AUTOMATIC IDENTIFICATION SYSTEM DATA AS APPLIED TO SOCIAL NETWORK RELATIONSHIPS AND ACTIVITIES
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Authors
Cline, Rachel
Subjects
Automatic Identification System
network science
criminal organization
South China Sea
social network inference
network science
criminal organization
South China Sea
social network inference
Advisors
Gera, Ralucca
Alderson, David L., Jr.
Date of Issue
2018-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This 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.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Applied Mathematics (MA)
Organization
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NPS Report Number
Sponsors
Funder
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This 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.