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Dataset guide · 地理遥感与城市

Vessel Density Grid 2014

This dataset contains the average weekly shipping density for the whole of the UK at a 2km grid resolution for 2014, based on AIS sampling.

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地理遥感与城市Free to access

Key facts

InstitutionMarine Management Organisation
CoverageUnited Kingdom
Time span2014
Scale1 file
LicenseLicense pending verification
AccessDirect download (ZIP file)

Contents & fields

The dataset contains average weekly shipping density for the whole UK at 2km grid resolution for 2014, calculated from AIS data sampled from the first seven days of each month (12 weeks), divided by 12 for weekly average, then multiplied by 52 for annual estimate. The attribute table includes the following fields:

  • 0 - Unknown Vessels——Density of unknown vessel types
  • 1 - Non-Port service craft——Density of non-port service craft
  • 2 - Port service craft——Density of port service craft
  • 3 - Vessels engaged in dredging or underwater operations——Density of dredging or underwater vessels
  • 4 - High Speed Craft——Density of high speed craft
  • 5 - Military or Law enforcement——Density of military or law enforcement vessels
  • 6 - Passenger——Density of passenger vessels
  • 7 - Cargo——Density of cargo vessels
  • 8 - Tankers——Density of tankers
  • 9 - Fishing vessels——Density of fishing vessels
  • 10 - Recreational vessels——Density of recreational vessels
  • Weekly_Ave——Weekly average of all vessels
  • Annual_Ave——Estimated annual density for all vessels

Research uses

Suitable for marine spatial planning, shipping traffic analysis, environmental impact assessment, fisheries management, and maritime safety research.

This card was drafted from the source page; institution, coverage, time span, scale, fields and license are subject to the official page (pending human review).

Keywords

vessel densityshippingAISUKgrid datamarine management

Access & license

License: License pending verification | Free to access

Why this is hard to get on your own

When obtaining high-resolution vessel density grid data, one often needs to handle inconsistencies between grids of different years.

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