Emodnet_HA_Vessel_Density_Map_20190311engutf8datasetCogea srllfalco@cogea.itapititto@cogea.itpointOfContact2019-03-11ISO191152003/Cor.1:2006EPSG:3035ISO 19108 calendarEMODnet Human Activities: Vessel Density 20172019-03-11publication2019-03-11revision2019-03-11creationEMODnet_HA_Vessel_Density_2017_20190311EMODnet Vessel Density Map were created by Cogea in 2019 in the framework of EMODnet Human Activities, an initiative funded by the EU Commission. The maps are based on AIS data purchased by CLS and show shipping density in 1km*1km cells of a grid covering all EU waters (and some neighbouring areas). Density is expressed as hours per square kilometre per month. The following ship types are available:0 Other, 1 Fishing, 2 Service, 3 Dredging or underwater ops, 4 Sailing, 5 Pleasure Craft, 6 High speed craft, 7 Tug and towing, 8 Passenger, 9 Cargo, 10T anker, 11 Military and Law Enforcement, 12 Unknown and All ship types. Data are available by month of year. Yearly averages are also available.Cogea srllfalco@cogea.itapititto@cogea.itoriginatorLovell Johnswilliam.adnams@lovelljohns.comauthorCLSpdebusschere@cls.frresourceProviderMarine LayerMarine WaterwayTransport AreaINSPIRE feature concept dictionary (Version: Linked Data 1.0)2014-09-14revisionmerchant shippingmaritime navigationmaritime transportGEMET (version 3.1, 2012-07-20)2014-06-08revisionTransport networksGeographical grid systemsGEMET - INSPIRE themes, version 1.02008-06-01publicationRe-use of content for commercial or non-commercial purposes is permitted free of charge, provided that the sources (both EMODnet - Human Activities, and CLS) are acknowledged. EMODnet - Human Activities accepts no responsibility or liability whatsoever for the re-use of content accessible on its website.otherRestrictionsno limitationgrid1000engoceans-87.70648497.63302714.96111186.8427272017-01-012017-12-31TIFF6.00http://www.emodnet-humanactivities.euEMODnet Human ActivitiesEMODnet Human Activities aims to facilitate access to existing marine data on activities carried out in EU waters, by building a single entry point for geographic information on human uses of the ocean. The portal makes available information such as geographical position, spatial extent of a series of activities related to the sea, their temporal variation, time when data was provided, and attributes to indicate the intensity of each activity. The data are aggregated and presented so as to preserve personal privacy and commercially-sensitive information. The data also include a time interval so that historic as well as current activities can be included.downloaddatasetCOMMISSION REGULATION (EC) No 1205/2008 of 3 December 2008 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards metadata2008-12-04publicationSee the referenced specificationtrueCorrigendum to INSPIRE Metadata Regulation published in the Official Journal of the European Union, L 328, page 832009-12-15publicationSee the referenced specificationCommission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services2010-12-08publicationSee the referenced specificationtrueA set of AIS data had to be purchased from CLS, a commercial provider. The data consists of messages sent by automatic tracking system installed on board ships and received by terrestrial and satellite receivers alike. The dataset covers the whole 2017 for an area covering all EU waters. A partial pre-processing of the data was carried out by CLS: (i) The only AIS messages delivered were the ones relevant for assessing shipping activities (AIS messages 1, 2, 3, 18 and 19). (ii) The AIS DATA were down-sampled to 3 minutes (iii) Duplicate signals were removed. (iv) Wrong MMSI signals were removed. (v) Special characters and diacritics were removed. (vi) Signals with erroneous speed over ground (SOG) were removed (negative values or more than 80 knots). (vii) Signals with erroneous course over ground (COG) were removed (negative values or more than 360 degrees). (viii) A Kalman filter was applied to remove satellite noise. The Kalman filter was based on a correlated random walk fine-tuned for ship behaviour. The consistency of a new observation with the modelised position is checked compared to key performance indicators such as innovation, likelihood and speed. (ix) A footprint filter was applied to check for satellite AIS data consistency. All positions which were not compliant with the ship-satellite co-visibility were flagged as invalid.The AIS data were converted from their original format (NMEA) to CSV, and split into 12 files, each corresponding to a month of 2017. Overall the pre-processed dataset included about 1.9 billion records. Upon trying and importing the data into a database, it emerged that some messages still contained invalid characters. By running a series of commands from a Linux shell, all invalid characters were removed. The data were then imported into a PostgreSQL relational database. By querying the database it emerged that some MMSI numbers are associated to more than a ship type during the year. To cope with this issue, we thus created an unique MMSI/shyp type register where we attributed to an MMSI the most recurring ship type. The admissible ship types reported in the AIS messages were grouped into macrocategories: 0 Other, 1 Fishing, 2 Service, 3 Dredging or underwater ops, 4 Sailing, 5 Pleasure Craft, 6 High speed craft, 7 Tug and towing, 8 Passenger, 9 Cargo, 10 Tanker, 11 Military and Law Enforcement, 12 Unknown and All ship types. The subsequent step consisted of creating points representing ship positions from the AIS messages. This was done through a custom-made script for ArcGIS developed by Lovell Johns. Another custom-made script reconstructed ship routes (lines) from the points, by using the MMSI number as a unique identifier of a ship. The script created a line for every two consecutive positions of a ship. In addition, for each line the script calculated its length (in km) and its duration (in hours) and appended them both as attributes to the line. If the distance between two consecutive positions of a ship was longer than 30 km or if the time interval was longer than 6 hours, no line was created. Both datasets (points and lines) were projected into the ETRS89/ETRS-LAEA coordinate reference system, used for statistical mapping at all scales, where true area representation is required (EPSG: 3035).The lines obtained through the ArcGIS script were then intersected with a custom-made 1km*1km grid polygon (21 million cells) based on the EEA's grid and covering the whole area of interest (all EU sea basins). Because each line had length and duration as attributes, it was possible to calculate how much time each ship spent in a given cell over a month by intersecting line records with grid cell records in another dedicated PostgreSQL database. Using the PostGIS Intersect tool, for each cell of the grid, we then summed the time value of each 'segment' in it, thus obtaining the density value associated to that cell, stored in calculated PostGIS raster tables. Density is thus expressed in hours per square kilometre per month. The final step consisted of creating raster files (TIFF file format) with QuantumGIS from the PostgreSQL vessel density tables. Annual average rasters by ship type were also created.