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Marcus Justesen, EFGS 2015
Exploration of AIS data
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.
The Project
A pilot study Funded by Vinnova ( Swedens innovation agency) Joint venture between Transport Analysis and
Statistics Sweden. Work in progress
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
The Objectives
Improve quality in maritime transport statistics using AIS data.
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
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
Eurostat´s distance matrix (port distance calculation tool)
Eurostat´s distance matrix (port distance calculation tool)
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
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
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
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
Point in polygon operationSwedish ports Foreign land ”dummy” Points from AIS
Result of point in polygon operation
Hamn = Port
Connect points by ID and Port
Connect points by ID and Port1
2
3
4
5
The result of connected points
Origin
Destination
Trip
7,7 million points is now 20 000 lines, each with attribute of origin and destination
Benifits of this methodAll 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%)
Benifits of this method
A first examination of port routes
port to portEurostat, km SCB/AIS, km
difference %
Göteborg (SEGOT) - Lysekil Preemraff (SELYS) 88 115 30,7%
Malmö (SEMMA) - Karlshamn (SEKAN) 265 276 4,2%
Göteborg (SEGOT) - Luleå (SELLA) 1729 1764 2,0%
Karlshamn (SEKAN) - Jätterön (SEJAT) 216 229 6,0%
Karlshamn (SEKAN) - Norrköping (SENRK) 428 486 13,6%
Norrköping (SENRK) - Gävle (SEGVX) 428 484 13,1%
Norrköping (SENRK) - Stockholm (SESTO) 242 297 22,7%
Norrköping (SENRK) - Kalmar (SEKLR) 270 275 1,9%
Västerås (SEVST) - Södertälje (SESOE) 89 100 12,4%
Stockholm (SESTO) - Gävle (SEGVX) 260 293 12,7%
All trips between Swedish ports selected
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
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
A first result
Problem
A first result
Destination added to the friction
Problem
Real routes to Norrköping
Destination and density as friction
Real routes to Norrköping
Destination and density as friction
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
Thank you!Marcus justesen
T:+46 850694961
Jerker Moström
Tel: +46 850694031