Shipping Routes Project

1. Shipping Routes Project Scott Phan An Nguyen Presentation 27, June 2011 studyabroad.iit.demokritos.gr

2. 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. ML & DM Lab

3. 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

4. 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

5. 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 NisosEviawere recorded. • The data was received from www.marinetraffic.com and www.mariweb.gr, which track and record data from shipping vessels. ML & DM Lab

6. Project Motivation • Accumulated Ship Trajectories in the Aegean • Article: • “Update on Shipping Data collection at Archipelagos” • Chris Fletcher • Link • http://workjournal.archipelago.gr/?p=1348 ML & DM Lab

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

8. Project Goals: Details • Create a Graphical User Interface (GUI) [done] • Place ship markers at arbitrary pixel locations on the map [done] • Parse the AIS database and plot each ship at a given time interval on the map [done] ML & DM Lab

9. Project Goals: Details • 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] • Create a visual identifier to show the risk of each ship (change color, display numeric label, etc) [done] ML & DM Lab

10. Project Goals • 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] • Create a map view which shows risky areas in red and less risky areas in green/blue. This “risk density map [In Progress] 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 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 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] ML & DM Lab

14. Approach: About the Input • Encoded Spatial Data within the Database ML & DM Lab

15. Approach: About the Input • Decoding the Encoded Spatial Data Decoding Input : 0101000020E6100000000000604A653840000000E0FBB14240 ML & DM Lab

16. Database IMIS Database NASA 3D maps API Control SQL Query Raw Data 3D maps Controller Graphic User Interface GUI Approach: Software Architecture ML & DM Lab

17. Approach: Graphical User Interface ML & DM Lab

18. Approach: Data Flow Architecture (Part I) ML & DM Lab

19. Approach: Data Flow Architecture ML & DM Lab

20. Approach: Output - Visualization ML & DM Lab

21. Approach: Output - Visualization ML & DM Lab

22. One more thing

23. How we get data? MySQL dump files Extracted AIS Data PostgreSQL/PostGIS

24. How long does it take? 3hours/file

25. SQL Importing Tool

26. Demo

27. Future Work • Prepare paper for publishing • Implement proximity function to find nearest land from a given point in the sea • Design and implement the data mining API and UI • Research shipping risk assessment methods • Implement density plotting with respect to risk assessment of a spatial area 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 Dept. @ University of Texas at Arlington ML & DM Lab

29. Questions?