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Service Description: <div style='text-align:Left;'><div><div><p><span>A series of scripts/steps were used to produce this dataset.</span></p><p><span>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.</span></p><p><span>2. Extract out just the points within Guam using a bounding box crop in the pandas python library and a spatial dataframe. </span></p><p><span>3. Merge the points by year.</span></p><p><span>4. Generate tracks using the AIS Utilities toolbox downloaded from the AIS website (https://marinecadastre.gov/ais)</span></p><p><span>5. Add a custom field for each track to categorize the vessel_type code to refine the groupings of vessel types. </span></p><p><span>6. Run pairwise intersect with the aliquots to cut the tracks by aliquot to get a vessel count in the next step.</span></p><p><span>7. Count all intersected vessel tracks per aliquot by vessel type and add to attribute table by type and year.</span></p><p><span>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.</span></p><p><span>Contact pacgis@boem.gov for details on scripts and methodology used. </span></p></div></div></div>

Map Name: Guam AIS Vessel Count 2017 to 2023 Average

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• Guam AIS Vessel Count 2017 to 2023 Average (24)

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: 8a81a0e52035492b95149b59525e56f3

Copyright Text: BOEM, NOAA

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Title: Guam AIS Summary 2017 to 2023
Author:
Comments: 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.
Subject: Summary of Automatic Identification System (AIS) vessel traffic per aliquot in the Guam EEZ. Data courtesy of https://coast.noaa.gov and processed by BOEM Pacific GIS staff. Due to the remote nature of the area, AIS data may be incomplete.
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Keywords: guam,ais,boem,noaa
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