Name: Guam AIS Vessel Count 2017 to 2023 Average

Display Field: PROT_NAME

Type: Feature Layer

Geometry Type: esriGeometryPolygon

Description: A series of scripts/steps were used to produce this dataset.1. Download all AIS data for years 2017 to 2013 from https://coast.noaa.gov/htdata/CMSP/AISDataHandler/{yr}/AIS_{yr}_{mo}_{d}.zip. This resulted in ~650GB of data.2. Extract out just the points within Guam using a bounding box crop in the pandas python library and a spatial dataframe. 3. Merge the points by year.4. Generate tracks using the AIS Utilities toolbox downloaded from the AIS website (https://marinecadastre.gov/ais)5. Add a custom field for each track to categorize the vessel_type code to refine the groupings of vessel types. 6. Run pairwise intersect with the aliquots to cut the tracks by aliquot to get a vessel count in the next step.7. Count all intersected vessel tracks per aliquot by vessel type and add to attribute table by type and year.Fields contain a vessel integer counts that pass through each aliquot summarized by vessel type and total vessels. The All_Ave field is the average of all vessel counts for all years 2017 to 2023.Contact pacgis@boem.gov for details on scripts and methodology used.

Service Item Id: d1054c34524945a8b608b5e8cf9a79b2

Copyright Text: BOEM, NOAA

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Spatial Reference: 4326  (4326)  LatestVCSWkid(0)

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Renderer:
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Type ID Field: null

Fields:

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• Shape.STArea() ( type: esriFieldTypeDouble, alias: Shape.STArea() )
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• Other_2017 ( type: esriFieldTypeInteger, alias: Other_2017 )
• Passenger_2017 ( type: esriFieldTypeInteger, alias: Passenger_2017 )
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