1. facebook.com/statisticssweden @SCB_nyheterstatistiska_centralbyran_scb Marcus Justesen, EFGS 2015 Exploration of AIS data

2. What is AIS?  Automatic identification system  Used by ships and vessel traffic services to exchange data, for navigation and survaillance  Information provided as often as every six(?) seconds  Information consists of unique id (MMSI), position, type of ship, direction, speed etc. etc.  It´s positional big data! Like GPS. Or mobile phone data.

3. The Project  A pilot study  Funded by Vinnova ( Swedens innovation agency)  Joint venture between Transport Analysis and Statistics Sweden.  Work in progress

4. The Objectives  Improve quality in maritime transport statistics using AIS data.  Evaluate opportunities for new statistics using AIS  To gather experience working with Big Data  Evaluate possible uses of other positional Big Data

5. The Objectives  Improve quality in maritime transport statistics using AIS data.

6. The Objectives  Improve quality in maritime transport statistics using AIS data. Create distance matrix for Swedish ports Compare data reported from ports with data from AIS Split routes in Swedish and International waters

7. The Objectives  Improve quality in maritime transport statistics using AIS data. Create distance matrix for Swedish ports Compare data reported from ports with data from AIS Split routes in Swedish and International waters

8. Eurostat´s distance matrix (port distance calculation tool)

10. We wanted raster!  The sea is a continous surface  Rasters can hold a lot of data- It´s just values in a cell  Fast processing  Flexible  Easy to experiment with  New data can be added

11. The data  Historical data (2014) extracted and delivered from the AIS database by Swedish maritime administration  One csv file per day for a two week period (each containing approx. 900 000 positions) 7,7 million points

12. The data  Geographically filtered to remove redundant data (North Sea and Russia interior)  Filtered by ship type: only cargo & tankers  unecessary attributes removed  csv > sql > points > lines

13. The data  Geographically filtered to remove redundant data (North Sea and Russia interior)  Filtered by ship type: only cargo & tankers  unecessary attributes removed  csv > sql > points > lines This is where we structure the data

14. Point in polygon operation Swedish ports Foreign land ”dummy” Points from AIS

15. Result of point in polygon operation Hamn = Port

16. Connect points by ID and Port

17. 1 2 3 4 5

18. The result of connected points Origin Destination Trip 7,7 million points is now 20 000 lines, each with attribute of origin and destination

19. Benifits of this method All trips between Swedish ports selected  We have structured our data, making it MUCH easier to work with  We now know the origin and destination of each line  We can start producing some statistical result and answer questions like ”How much of the transports between Swedish ports takes place on Swedish territorial water ?” (66%)

20. Benifits of this method A first examination of port routes port to port Eurostat, km SCB/AIS, km difference % Göteborg (SEGOT) - Lysekil Preemraff (SELYS)8811530,7% Malmö (SEMMA) - Karlshamn (SEKAN)2652764,2% Göteborg (SEGOT) - Luleå (SELLA)172917642,0% Karlshamn (SEKAN) - Jätterön (SEJAT)2162296,0% Karlshamn (SEKAN) - Norrköping (SENRK)42848613,6% Norrköping (SENRK) - Gävle (SEGVX)42848413,1% Norrköping (SENRK) - Stockholm (SESTO)24229722,7% Norrköping (SENRK) - Kalmar (SEKLR)2702751,9% Västerås (SEVST) - Södertälje (SESOE)8910012,4% Stockholm (SESTO) - Gävle (SEGVX)26029312,7% All trips between Swedish ports selected

21. Least cost path analysis  Lines converted to raster  Line density used as friction  (cost = distance * friction)  Cost distance raster created from Norrköping  Friction needs to be modelled

22. Least cost path analysis  Lines converted to raster  Line density used as friction  (cost = distance * friction)  Cost distance raster created from Norrköping  Friction needs to be modelled Norrköping

23. A first result

24. Problem A first result

25. Destination added to the friction Problem Real routes to Norrköping

26. Destination and density as friction Real routes to Norrköping

27. Destination and density as friction

28. Some conclusions  Two weeks is not enough data  Raster analysis seems promising for route calculations – more testing is needed  Structuring the data by creating lines between ports works really well and creates many possiblitis for further analysis.  The method can most likely be used for other transport Flows and data sources such as mobile phone data.  AIS data can be used to improve maritime transport statistics

29. Thank you! Marcus justesen T:+46 850694961 Marcus.justesen@scb.se Jerker Moström Tel: +46 850694031 Jerker.mostrom@scb.se