North Pacific and Arctic marine traffic dataset (2015–2020)
In this paper, we present a spatially explicit dataset of monthly shipping intensity in the Pacific Arctic region from January 1, 2015 to December 31, 2020. We calculated shipping intensity based on Automatic Identification System (AIS) data, a type of GPS transmitter required by the International M...
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ftdoajarticles:oai:doaj.org/article:dc6a7f10aa4b4bab871cb0150b36942d 2023-05-15T14:54:31+02:00 North Pacific and Arctic marine traffic dataset (2015–2020) Kelly Kapsar Benjamin Sullender Jianguo Liu Aaron Poe 2022-10-01T00:00:00Z https://doi.org/10.1016/j.dib.2022.108531 https://doaj.org/article/dc6a7f10aa4b4bab871cb0150b36942d EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2352340922007387 https://doaj.org/toc/2352-3409 2352-3409 doi:10.1016/j.dib.2022.108531 https://doaj.org/article/dc6a7f10aa4b4bab871cb0150b36942d Data in Brief, Vol 44, Iss , Pp 108531- (2022) Arctic Shipping Vessel traffic Automatic Identification System Satellite AIS Pacific Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 article 2022 ftdoajarticles https://doi.org/10.1016/j.dib.2022.108531 2022-12-30T20:35:29Z In this paper, we present a spatially explicit dataset of monthly shipping intensity in the Pacific Arctic region from January 1, 2015 to December 31, 2020. We calculated shipping intensity based on Automatic Identification System (AIS) data, a type of GPS transmitter required by the International Maritime Organization on all ships over 300 gross tonnes on an international voyage, all cargo ships over 500 gross tonnes, and all passenger ships. We used AIS data received by the exactEarth satellite constellation (64 satellites as of 2020), ensuring spatial coverage regardless of national jurisdiction or remoteness. Our analytical approach converted raw AIS input into monthly raster and vector datasets, separated by vessel type. We first filtered raw AIS messages to remove spurious records and GPS errors, then joined remaining vessel positional records with static messages including descriptive attributes. We further categorized these messages into one of four general ship types (cargo; tanker; fishing; and other). For the vector dataset, we spatially intersected AIS messages with a hexagon (hex) grid and calculated the number of unique ships, the number of unique ships per day (summed over each month), and the average and standard deviation of the speed over ground. We calculated these values for each month for all vessels as well as vessels subdivided by ship type and for messages from vessels > 65 feet long and traveling > 10 knots. For the raster dataset, we created a series of spatially explicit daily vessel tracks according to unique voyages and aggregated tracks by ship type and month. We then created a raster grid and calculated the total length, in meters, of all vessel tracks within each raster cell. These monthly datasets provide a critical snapshot of dynamic commercial and natural systems in the Pacific Arctic region. Recent declines in sea ice have lengthened the duration of the shipping season and have expanded the spatial coverage of large vessel routes, from the Aleutian Islands through the ... Article in Journal/Newspaper Arctic Pacific Arctic Sea ice Aleutian Islands Directory of Open Access Journals: DOAJ Articles Arctic Pacific Data in Brief 44 108531 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic Shipping Vessel traffic Automatic Identification System Satellite AIS Pacific Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 |
spellingShingle |
Arctic Shipping Vessel traffic Automatic Identification System Satellite AIS Pacific Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Kelly Kapsar Benjamin Sullender Jianguo Liu Aaron Poe North Pacific and Arctic marine traffic dataset (2015–2020) |
topic_facet |
Arctic Shipping Vessel traffic Automatic Identification System Satellite AIS Pacific Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 |
description |
In this paper, we present a spatially explicit dataset of monthly shipping intensity in the Pacific Arctic region from January 1, 2015 to December 31, 2020. We calculated shipping intensity based on Automatic Identification System (AIS) data, a type of GPS transmitter required by the International Maritime Organization on all ships over 300 gross tonnes on an international voyage, all cargo ships over 500 gross tonnes, and all passenger ships. We used AIS data received by the exactEarth satellite constellation (64 satellites as of 2020), ensuring spatial coverage regardless of national jurisdiction or remoteness. Our analytical approach converted raw AIS input into monthly raster and vector datasets, separated by vessel type. We first filtered raw AIS messages to remove spurious records and GPS errors, then joined remaining vessel positional records with static messages including descriptive attributes. We further categorized these messages into one of four general ship types (cargo; tanker; fishing; and other). For the vector dataset, we spatially intersected AIS messages with a hexagon (hex) grid and calculated the number of unique ships, the number of unique ships per day (summed over each month), and the average and standard deviation of the speed over ground. We calculated these values for each month for all vessels as well as vessels subdivided by ship type and for messages from vessels > 65 feet long and traveling > 10 knots. For the raster dataset, we created a series of spatially explicit daily vessel tracks according to unique voyages and aggregated tracks by ship type and month. We then created a raster grid and calculated the total length, in meters, of all vessel tracks within each raster cell. These monthly datasets provide a critical snapshot of dynamic commercial and natural systems in the Pacific Arctic region. Recent declines in sea ice have lengthened the duration of the shipping season and have expanded the spatial coverage of large vessel routes, from the Aleutian Islands through the ... |
format |
Article in Journal/Newspaper |
author |
Kelly Kapsar Benjamin Sullender Jianguo Liu Aaron Poe |
author_facet |
Kelly Kapsar Benjamin Sullender Jianguo Liu Aaron Poe |
author_sort |
Kelly Kapsar |
title |
North Pacific and Arctic marine traffic dataset (2015–2020) |
title_short |
North Pacific and Arctic marine traffic dataset (2015–2020) |
title_full |
North Pacific and Arctic marine traffic dataset (2015–2020) |
title_fullStr |
North Pacific and Arctic marine traffic dataset (2015–2020) |
title_full_unstemmed |
North Pacific and Arctic marine traffic dataset (2015–2020) |
title_sort |
north pacific and arctic marine traffic dataset (2015–2020) |
publisher |
Elsevier |
publishDate |
2022 |
url |
https://doi.org/10.1016/j.dib.2022.108531 https://doaj.org/article/dc6a7f10aa4b4bab871cb0150b36942d |
geographic |
Arctic Pacific |
geographic_facet |
Arctic Pacific |
genre |
Arctic Pacific Arctic Sea ice Aleutian Islands |
genre_facet |
Arctic Pacific Arctic Sea ice Aleutian Islands |
op_source |
Data in Brief, Vol 44, Iss , Pp 108531- (2022) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2352340922007387 https://doaj.org/toc/2352-3409 2352-3409 doi:10.1016/j.dib.2022.108531 https://doaj.org/article/dc6a7f10aa4b4bab871cb0150b36942d |
op_doi |
https://doi.org/10.1016/j.dib.2022.108531 |
container_title |
Data in Brief |
container_volume |
44 |
container_start_page |
108531 |
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