Kapsar Kelly, Sullender Benjamin, Liu Jianguo, Poe Aaron
Center for Systems Integration and Sustainability, Michigan State University, 1405 S Harrison Road, East Lansing, MI 48823, USA.
Kickstep Approaches, 4303 Needle Cir., Anchorage, AK 99508, USA.
Data Brief. 2022 Aug 8;44:108531. doi: 10.1016/j.dib.2022.108531. eCollection 2022 Oct.
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 Bering Strait and into the southern Chukchi Sea. As vessel traffic has increased, the social and natural systems of these regions have been increasingly exposed to the risks posed by large ships, including oil spills, underwater noise pollution, large cetacean ship-strikes, and discharges of pollutants. This dataset provides scientific researchers, regulatory managers, local community members, maritime industry representatives, and other decision makers with a quantitative means to evaluate the distribution and intensity of shipping across space and through time.
在本文中，我们展示了一个空间明确的数据集，该数据集涵盖了2015年1月1日至2020年12月31日太平洋北极地区每月的航运强度。我们根据自动识别系统（AIS）数据计算航运强度，AIS是国际海事组织要求所有国际航行的300总吨以上船舶、所有500总吨以上货船以及所有客船配备的一种GPS发射器。我们使用了exactEarth卫星星座（截至2020年有64颗卫星）接收的AIS数据，确保了无论国家管辖范围或偏远程度如何都能实现空间覆盖。我们的分析方法将原始AIS输入转换为按月划分、按船舶类型分类的栅格和矢量数据集。我们首先对原始AIS消息进行过滤，以去除虚假记录和GPS错误，然后将剩余的船舶位置记录与包括描述性属性的静态消息进行合并。我们进一步将这些消息分类为四种一般船舶类型之一（货船；油轮；渔船；以及其他）。对于矢量数据集，我们将AIS消息与六边形（hex）网格进行空间相交，并计算独特船舶的数量、每天独特船舶的数量（按月汇总）以及对地速度的平均值和标准差。我们针对所有船舶以及按船舶类型细分的船舶，以及长度超过65英尺且航速超过10节船舶的消息，每月计算这些值。对于栅格数据集，我们根据独特航程创建了一系列空间明确的每日船舶航迹，并按船舶类型和月份汇总航迹。然后我们创建了一个栅格网格，并计算每个栅格单元内所有船舶航迹的总长度（以米为单位）。这些月度数据集提供了太平洋北极地区动态商业和自然系统的关键快照。近期海冰的减少延长了航运季节的持续时间，并扩大了大型船舶航线的空间覆盖范围，从阿留申群岛经白令海峡延伸至楚科奇海南部。随着船舶交通量的增加，这些地区的社会和自然系统越来越多地面临大型船舶带来的风险，包括石油泄漏、水下噪声污染、大型鲸类船舶碰撞以及污染物排放。该数据集为科研人员、监管管理人员、当地社区成员、海运行业代表及其他决策者提供了一种定量手段，用以评估航运在空间和时间上的分布及强度。
