Institute of Informatics and Telecommunications – NCSR “Demokritos” Shipping Routes Project Scott Phan An Nguyen Presentation 27, June 2011 studyabroad.iit.demokritos.gr

Shipping in the Aegean Sea  Mediterranean Sea supports between 4-18% of the worlds species  Aegean Sea is an area of the Mediterranean which carries high biological importance, due to the relatively low coastal development.  However, the preservation of this ecosystem is being left largely to chance, with few protection measures in place. If damage to the area increases or a major event the results could be severe. 27/06/2011 ML & DM Lab 2

Shipping in the Aegean Sea  Hundreds of cargo, tanker and passenger ships pass through the Aegean Sea every day. The potential impacts of shipping, commercial and recreational, are vast.  Ships can affect marine biota in the following ways: –Underwater noise created by ships –Anchoring –Grounding –Direct collisions –Carrying invasive species –Operational oil discharges –Accidental oil discharges –Thermal Discharges ML & DM Lab 3 27/06/2011

Project Motivation  The Aegean Sea lacks efficient mechanisms to manage, monitor and regulate ship traffic conditions.  To decrease the chance of ecological disturbance events, strict shipping lanes must be established and enforced. ML & DM Lab 4 27/06/2011

Project Motivation  Over the last few months, the marine and GIS teams from Archipelagos have been working on a major shipping project.  From November 12, 2009 to April 29, 2010, data on all of the tankers and cargos that travelled between Samos, Ikaria, Mykonos, Andros, and Nisos Evia were recorded.  The data was received from and which track and record data from shipping vessels. ML & DM Lab 5 27/06/2011

ML & DM Lab 6 Accumulated Ship Trajectories in the Aegean Article: “Update on Shipping Data collection at Archipelagos” -Chris Fletcher Link Project Motivation 27/06/2011

Project Goals ML & DM Lab 7 27/06/2011 Database Design/Implementations Import/Decode AIS data GUI Design/Data Visualization Data Mining / Risk Management

Project Goals: Details ML & DM Lab 8 1.C reate a Graphical User Interface (GUI) [done] 2.Place ship markers at arbitrary pixel locations on the map [done] 3.Parse the AIS database and plot each ship at a given time interval on the map [done] 27/06/2011

Project Goals: Details ML & DM Lab 9 4.Create a function to assign a scalar “risk value” for each ship given certain database attributes for each ship (cargo, size, proximity to coast and other ships, flag, etc) [In Progress] 5.Create a visual identifier to show the risk of each ship (change color, display numeric label, etc) [done] 27/06/2011

Project Goals ML & DM Lab 10 7.Create a function to assess the risk of a particular map pixel location based on the number and proximity of ships within a given radius and proximity to land [In Progress] 8.Create a map view which shows risky areas in red and less risky areas in green/blue. This “risk density map [In Progress] 27/06/2011

ML & DM Lab 11 Approach: Area of Interest Spatial Bounds Lat [35, 39] Lon [21, 29] Aegean Sea Ignore all data that falls outside the boundaries 27/06/2011

ML & DM Lab 12 Approach: About the Input  Huge data collections  Good facilities to collect real time data  Accurate information to predict the trend of routes and risk management  Used dataset provided by International Maritime Information Systems (IMIS) –Covers 2 days worth of AIS messages  Challenges with the IMIS dataset: –Raw data - not decoded –Not well managed Redundant data (3x redundant) –Database not as supportive for GIS development 27/06/2011

ML & DM Lab 13 Approach: About the Input  Automatic Identification System (AIS) –is an automated tracking system used on ships and by Vessel Traffic Services (VTS) for identifying and locating vessels by electronically exchanging data with other nearby ships and VTS stations. –AIS information supplements marine radar, which continues to be the primary method of collision avoidance for water transport.  Types of Info. Encapsulated within the Messages –Static [MMSI number, IMO number, callsign, ship name and type, dimension] –Dynamic [position, time, speed, heading, course over ground, rate of turn, navigational status] –Trajectory-based [destination, estimated time of arrival, draught] 27/06/2011

ML & DM Lab 14 Approach: About the Input  Encoded Spatial Data within the Database 27/06/2011

ML & DM Lab 15 Approach: About the Input  Decoding the Encoded Spatial Data Input : E A E0FBB14240 Decoding 27/06/2011

ML & DM Lab 16 Approach: Software Architecture Database IMIS Database NASA 3D maps API Graphic User Interface GUI Control Raw Data3D maps Controller SQL Query 27/06/2011

ML & DM Lab 17 Approach: Graphical User Interface 27/06/2011

ML & DM Lab 18 Approach: Data Flow Architecture (Part I) 27/06/2011

ML & DM Lab 19 Approach: Data Flow Architecture 27/06/2011

ML & DM Lab 20 Approach: Output - Visualization 27/06/2011

ML & DM Lab 21 Approach: Output - Visualization 27/06/2011

One more thing 27/06/2011

How we get data? 27/06/2011 MySQL dump files Extracted AIS Data PostgreSQL/PostGIS

How long does it take? 3hours/file 27/06/2011

SQL Importing Tool 27/06/2011

Demo 27/06/2011

ML & DM Lab 27 Future Work 1.Prepare paper for publishing 2.Implement proximity function to find nearest land from a given point in the sea 3.Design and implement the data mining API and UI 4.Research shipping risk assessment methods 5.Implement density plotting with respect to risk assessment of a spatial area 27/06/2011

ML & DM Lab 28 Acknowledgements  Archipelagos - Institute of Marine Conservation  “KNOWLEDGE DISCOVERY FROM MARITIME MOVING OBJECTS - APPLICATION TO AEGEAN SEA” – Cyril Ray, Naval Academy Research Lab, December 2010  NCSR Demokritos  University of The Aegean  International Maritime Information Systems (IMIS)  CSE University of Texas at Arlington 27/06/2011

Questions? 27/06/2011