Data taken from the NCEAS report A Global Map of Human Impacts to Marine Ecosystems.Cumulative human impact on the ocean results from the combination of each of nineteen individual stressors. Understanding the pattern and intensity of the individual stressors is therefore a key first step in the analyses. These results also provide important snapshots of the individual contribution of each stressor to the current condition of the ocean.Commercial shipping activity can lead to ship strikes of large animals, noise pollution, and a risk of ship groundings or sinkings. Ships from many countries voluntarily participate in collecting meteorological data globally, and therefore also report the location of the ship. We used data collected from 12 months beginning October 2004 (collected as part of the World Meteorological Organization Voluntary Observing Ships Scheme; http://www.vos.noaa.gov/vos_scheme.shtml) as this year had the most ships with vetted protocols and so provides the most representative estimate of global ship locations. The data include unique identifier codes for ships (mobile or a single datum) and stationary buoys and oil platforms (multiple data at a fixed location); we removed all stationary and single point ship data, leaving 1,189,127 mobile ship data points from a total of 3,374 commercial and research vessels, representing roughly 11% of the 30,851 merchant ships >1000 gross tonnage at sea in 2005 (S14). We then connected all mobile ship data to create ship tracks, under the assumption that ships travel in straight lines (a reasonable assumption since ships minimize travel distance in an effort to minimize fuel costs). Finally, we removed any tracks that crossed land (e.g. a single ship that records its location in the Atlantic and the Pacific would have a track connected across North America), buffered the remaining 799,853 line segments to be 1 km wide to account for the width of shipping lanes, summed all buffered line segments to account for overlapping ship tracks, and converted summed ship tracks to raster data. This produced 1 km2 raster cells with values ranging from 0 to 1,158, the maximum number of ship tracks recorded in a single 1 km2 cell.Because the VOS program is voluntary, much commercial shipping traffic is not captured by these data. Therefore our estimates of the impact of shipping are biased (in an unknown way) to locations and types of ships engaged in the program. In particular, high traffic locations may be strongly underestimated, although the relative impact on these areas versus low-traffic areas appears to be well-captured by the available data (Fig. S2), and areas identified as without shipping may actually have low levels of ship traffic. Furthermore, because ships report their location with varying distance between signals, ship tracks are estimates of the actual shipping route taken.All data used and developed for the Global Map of Cumulative Human Impact project are freely available and can be downloaded directly via the KNB Data Repository.

Global Shipping Lanes / Routes GIS Data

Arctic lines of communication. In the case of the Northern Sea Route, the route is based on the actual route used by Russian icebreakers and cargo ships. The Northwest Passage is based on the channels that would be able to support large cargo ships. The transpolar route is a hypothetical route that could be used either as a result of ice-free summers or the extensive use of icebreakers and ice-hardened ships.

Geospatial data about US Shipping Lanes. Export to CAD, GIS, PDF, CSV and access via API.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

Polygon data for all major shipping lanes associated with all ports in the Gulf of Mexico are presented. These layers were modified from GIS data acquired from the U. S. Department of the Interior Bureau of Ocean Energy Management (BOEM) website. Although file description reports are available, no FGDC-compliant metadata are available for the original files from BOEM. The current layers are non-projected with coordinates in decimal degrees.

Geometry shown for Shipping Fairways Lanes and Zones in Florida only. Shipping zones delineate activities and regulations for marine vessel traffic. Traffic lanes define specific traffic flow, while traffic separation zones assist opposing streams of marine traffic. Precautionary areas represent areas where ships must navigate with caution, and shipping safety fairways designate where artificial structures are prohibited. Recommended routes are predetermined routes for shipping adopted for reasons of safety. Areas to be avoided are within defined limits where navigation is particularly hazardous or it is exceptionally important to avoid casualties and should be avoided by all ships or certain classes of ships. Shipping Lanes and Regulations layer was created by extracting ENC (.000) files published by NOAAs Office of Coast Survey, Marine Chart Division (NOAA OCS). The web service was developed by CSDL/OCS/NOAA. Data will be refreshed weekly.

This is the dataset created for the Journal of Glaciology paper “Historical Occurrence of Antarctic Icebergs within Mercantile Shipping Routes and the Exceptional Events of the 1890s” (JOG-22-0139) by Robert Headland, Nick Hughes and Jeremy Wilkinson (doi: TBD). We have endeavoured to make the data as accessible as possible by providing it in a range of formats.

Please see the README.pdf for a detailed description of the files, and the paper for the dataset.

Freight related data grouped together and made up of major truck streets, freight network, over legal routes, and heavy haul network. Data is maintained by Seattle Department of Transportation.

Automatic identification system (AIS) data are used to identify and track vessels for various purposes (primarily navigational safety). These data can be used to study vessel traffic, such as ship routing and speed over ground (SOG). Source data were obtained from the United States Coast Guard Navigation Center (USCG NAVCEN) for the period from June 2008 to December 2015. Derived data resulting from the processing of the source data are included here. This data set presents annual raster data (1 square kilometer grid size) off California from 2008-2015 for cumulative ship traffic density (kilometers/day) and mean SOG (knots; distance-weighted). The universe of data is limited to vessels with a length greater than or equal to 80 meters. The data are analyzed in three groups: freight vessels (container, general cargo, bulk carrier, refrigerated cargo, vehicle carrier, etc.), tanker vessels (crude oil, chemical/products, liquid petroleum gas, etc.) and all vessels (the previously noted vessels, plus passenger vessels and other vessel classes). Esri ArcGIS software (ArcGIS Desktop 10.4.1) was used to process the data. The data are contained in a file geodatabase format as raster data sets. Metadata for the overall data set are contained at the level of the file geodatabase. The data were generated and used for a research article (Moore et al. 2018): Moore, T.J., Redfern, J.V., Carver, M., Hastings, S., Adams, J.D., Silber, G.K., 2018. Exploring Ship Traffic Variability off California. Ocean and Coastal Management. https://doi.org/10.1016/j.ocecoaman.2018.03.010 See this manuscript for more information on the data description, issues, and processing methods.

The Digital Geomorphic-GIS Map of Ship Island (5-meter accuracy 2007 mapping), Mississippi is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (ship_geomorphology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (ship_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (ship_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (ship_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (ship_geomorphology_metadata.txt or ship_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:14,000 and United States National Map Accuracy Standards features are within (horizontally) 11.8 meters or 38.8 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Private non retail shipping locations in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

Dataset contains polygon features of deepwater ship channels, specifically the Sacramento and Stockton Deepwater Ship Channels in the California Delta. Data can be used for visualization or analysis requiring the locations of the deepwater ship channels.

EMODnet Vessel Density Map were created by Cogea in 2019 in the framework of EMODnet Human Activities, an initiative funded by the EU Commission. The maps are based on AIS data purchased by CLS and show shipping density in 1km*1km cells of a grid covering all EU waters (and some neighbouring areas). Density is expressed as hours per square kilometre per month. A set of AIS data had to be purchased from CLS, a commercial provider. The data consists of messages sent by automatic tracking system installed on board ships and received by terrestrial and satellite receivers alike. The dataset covers the whole 2017 for an area covering all EU waters. A partial pre-processing of the data was carried out by CLS: (i) The only AIS messages delivered were the ones relevant for assessing shipping activities (AIS messages 1, 2, 3, 18 and 19). (ii) The AIS DATA were down-sampled to 3 minutes (iii) Duplicate signals were removed. (iv) Wrong MMSI signals were removed. (v) Special characters and diacritics were removed. (vi) Signals with erroneous speed over ground (SOG) were removed (negative values or more than 80 knots). (vii) Signals with erroneous course over ground (COG) were removed (negative values or more than 360 degrees). (viii) A Kalman filter was applied to remove satellite noise. The Kalman filter was based on a correlated random walk fine-tuned for ship behaviour. The consistency of a new observation with the modeled position is checked compared to key performance indicators such as innovation, likelihood and speed. (ix) A footprint filter was applied to check for satellite AIS data consistency. All positions which were not compliant with the ship-satellite co-visibility were flagged as invalid.The AIS data were converted from their original format (NMEA) to CSV, and split into 12 files, each corresponding to a month of 2017. Overall the pre-processed dataset included about 1.9 billion records. Upon trying and importing the data into a database, it emerged that some messages still contained invalid characters. By running a series of commands from a Linux shell, all invalid characters were removed. The data were then imported into a PostgreSQL relational database. By querying the database it emerged that some MMSI numbers are associated to more than a ship type during the year. To cope with this issue, we thus created an unique MMSI/shyp type register where we attributed to an MMSI the most recurring ship type. The admissible ship types reported in the AIS messages were grouped into macro categories: 0 Other, 1 Fishing, 2 Service, 3 Dredging or underwater ops, 4 Sailing, 5 Pleasure Craft, 6 High speed craft, 7 Tug and towing, 8 Passenger, 9 Cargo, 10 Tanker, 11 Military and Law Enforcement, 12 Unknown and All ship types. The subsequent step consisted of creating points representing ship positions from the AIS messages. This was done through a custom-made script for ArcGIS developed by Lovell Johns. Another custom-made script reconstructed ship routes (lines) from the points, by using the MMSI number as a unique identifier of a ship. The script created a line for every two consecutive positions of a ship. In addition, for each line the script calculated its length (in km) and its duration (in hours) and appended them both as attributes to the line. If the distance between two consecutive positions of a ship was longer than 30 km or if the time interval was longer than 6 hours, no line was created. Both datasets (points and lines) were projected into the ETRS89/ETRS-LAEA coordinate reference system, used for statistical mapping at all scales, where true area representation is required (EPSG: 3035).The lines obtained through the ArcGIS script were then intersected with a custom-made 1km*1km grid polygon (21 million cells) based on the EEA's grid and covering the whole area of interest (all EU sea basins). Because each line had length and duration as attributes, it was possible to calculate how much time each ship spent in a given cell over a month by intersecting line records with grid cell records in another dedicated PostgreSQL database. Using the PostGIS Intersect tool, for each cell of the grid, we then summed the time value of each 'segment' in it, thus obtaining the density value associated to that cell, stored in calculated PostGIS raster tables. Density is thus expressed in hours per square kilometre per month. The final step consisted of creating raster files (TIFF file format) with QuantumGIS from the PostgreSQL vessel density tables. Annual average rasters by ship type were also created. The dataset was clipped according to the National Marine Planning Framework (NMPF) assessment area. None

This GIS dataset provides location information and details about commodities exported from shipping ports around Australia. This dataset has been collated by Geoscience Australia from publicly …Show full descriptionThis GIS dataset provides location information and details about commodities exported from shipping ports around Australia. This dataset has been collated by Geoscience Australia from publicly available information as a guide only.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.

The National Highway Freight Network (NHFN) dataset was compiled on January 27, 2023 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Congress established a new National Highway Freight Program (NHFP) in 23 U.S.C. 167 to improve the efficient movement of freight on the National Highway Freight Network (NHFN) and support several goals. The law required the FHWA Administrator to strategically direct Federal resources and policies toward improved performance of the network. The NHFP provides formula funding apportioned annually to States, for use on the NHFN. The definition of the NHFN is established under 23 U.S.C. 167(c) and consists of four separate highway network components: the PHFS; Critical Rural Freight Corridors (CRFCs); Critical Urban Freight Corridors (CUFCs); and those portions of the Interstate System that are not part of the PHFS. Primary Highway Freight System (PHFS): This is a network of highways identified as the most critical highway portions of the U.S. freight transportation system determined by measurable and objective national data. The network consists of 41,800 centerlines miles, including 38,014 centerline miles of Interstate and 3,785 centerline miles of non-Interstate roads. Other Interstate portions not on the PHFS: These highways consist of the remaining portion of Interstate roads not included in the PHFS. These routes provide important continuity and access to freight transportation facilities. These portions amount to an estimated 10,265 centerline miles of Interstate, nationwide, and will fluctuate with additions and deletions to the Interstate Highway System. Critical Rural Freight Corridors (CRFCs): These are public roads not in an urbanized area which provide access and connection to the PHFS and the Interstate with other important ports, public transportation facilities, or other intermodal freight facilities. Nationwide, there are 5,389 centerline miles designated as CRFCs as of January 27, 2023. CRFCs are not included in GIS data base. Critical Urban Freight Corridors (CUFCs): These are public roads in urbanized areas which provide access and connection to the PHFS and the Interstate with other ports, public transportation facilities, or other intermodal transportation facilities. Nationwide, there are 2,656 centerline miles designated as CUFC as of January 27, 2023. CUFCs are not included in GIS data base.

Various shipping zones delineate activities and regulations for marine vessel traffic. Traffic lanes define specific traffic flow, while traffic separation zones assist opposing streams of marine traffic. Precautionary areas represent areas where ships must navigate with caution, and shipping safety fairways designate where artificial structures are prohibited. Recommended Routes are predetermined routes for shipping adopted for reasons of safety. Along certain zones of the East Coast of the United States, ships are required to reduce speeds to 10 knots or less over ground during seasonal periods within designated endangered species areas, such as the North Atlantic Right Whales. Particularly Sensitive Sea Areas need special protection because of their vulnerability to damage by international maritime activities. Areas to be avoided are within defined limits where navigation is particularly hazardous or it is exceptionally important to avoid casualties and should be avoided by all ships or certain classes of ships. Direct data download | Metadata

These data provide an accurate high-resolution shoreline compiled from imagery of Ship Island, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Sour...

This dataset contains the average weekly shipping density for the whole of the UK at a 2km grid resolution. For each year, AIS datasets were s&led from the first seven days of each month, commencing with January, at two-month intervals. The total value for all 6 weeks was divided by 6 to determine the weekly average, therefore decimal values may occur for certain cells. The weekly average was also multiplied by 52 to provide the estimated annual average.

The field headings in the attribute table are as follows:

0 - Unknown Vessels 1 - Non-Port service craft 2 - Port service craft 3 - Vessels engaged in dredging or underwater operations 4 - High Speed Craft 5 - Military or Law enforcement 6 - Passenger 7 - Cargo 8 - Tankers 9 - Fishing vessels 10 - Recreational vessels Week_Ave - Weekly Average Density Annual_Ave - Estimated annual density