The locations of coastal ferry routes in British Columbia. This includes ferries in coastal waters like BC Ferries, barges, water taxis, etc. but does not include freshwater ferries on inland rivers or lakes.

Public Works operates seven ferries. All are links in our provincial highway system and include: Four cable ferries: LaHave - Lunenburg County; Country Harbour, Guysborough County; Little Narrows, Victoria County; Englishtown, Victoria County. Three ocean-going, self-propelled ferries: Tancook Island – Chester; Petit Passage at Digby Neck; Grand Passage at Digby Neck. Content within this dataset relates to the operations schedules for all ferries. Refer to 511 for current availability of service: http://511.gov.ns.ca/en/map/

FERRYSEG_OFFICIAL provides the information about the Ferry Connection Segments. Ferry Connection Segment is the specific representation of a portion of a ferry connection with uniform characteristics. FERRYSEG_OFFICIAL is one of the base layer used to create "Ferryseg" layer for Saskatchewan Upgraded Road Network (SURN) and National Road Network (NRN). This data layer has been created for mainly MHI data users, that require relatively up-to-date and accurate description of all ferry routes in the Province of Saskatchewan.FERRYSEG_OFFICIAL consists of the data for the average route a ferryboat takes when transporting vehicles between two fixed locations on the Road Network. "NID" National Identifier is used to manage the updates between data producer and data users. FERRYSEG_OFFICIAL data layer serves as a foundation for SURN, NRN, SASK911 and other applications.

Routes that cruise ships travel off the coast of BC

This dataset identifies the geographic locations of marine ports, terminals, shipyards, and harbours on the west coast of British Columbia. The points were reviewed and cross referenced with government and industry data sources for geographic and attribute data accuracy.

This service provides routeing measures. These include established (mandatory) direction of traffic flow, recommended direction of traffic flow, separation lines, separation zones, limits of restricted routeing measure, limits of routeing measures, precautionary areas, archipelagic sea lanes (axis line and limit beyond which vessels shall not navigate) and fairways designated by regulatory authority.

Map of harbours critical to fishing and aquaculture industries managed by harbour authorities (Core fishing harbours), harbours that support fishing and aquaculture industries that aren’t managed by harbour authorities (Non-core fishing harbours), and harbours that support the recreational community (Recreational harbours).

This report describes an analysis of Maritimes Region inshore lobster logbook data reported at a grid level, including Bay of Fundy Disputed Zone data reported at the coordinate level. Annual and composite (2015–2019) grid maps were produced for landings, number of trap hauls, and the same series standardized by grid area, as well as maps of catch weight per number of trap hauls as an index of catch per unit effort (CPUE). Spatial differences in fishing pressure, landings, and CPUE are indicated, and potential mapping applications are outlined. Mapping the distribution and intensity of inshore lobster fishing activity has management applications for spatial planning and related decision support. The lack of region-wide latitude and longitude coordinates for inshore lobster effort and landings limits the utility of commercial logbook data for marine spatial planning purposes.

Examples include: * shoreline access * enhanced shoreline access (with a dock or pier) * boat launches This data was created to be used as part of the Fish ON-Line mapping application.

Description:Data on recreational boating are needed for marine spatial planning initiatives in British Columbia (BC). Vessel traffic data are typically obtained by analyzing automatic identification system (AIS) vessel tracking data, but recreational vessels are often omitted or underrepresented in AIS data because they are not required to carry AIS tracking devices. Transport Canada’s National Aerial Surveillance Program (NASP) conducted aerial surveys to collect information on recreational vessels along several sections of the BC coast between 2018 and 2022. Recreational vessel sightings were modeled against predictor variables (e.g., distance to shore, water depth, distance to, and density of marinas) to predict the number of recreational vessels along coastal waters of BC.The files included here are:--A Geodatabase (‘Recreational_Boating_Data_Model’), which includes: (1) recreational vessel sightings data collected by NASP in BC and used in the recreational vessel traffic model (‘Recreational_Vessels_PointData_BC’); (2) aerial survey effort (or number of aerial surveys) raster dataset (‘surveyeffort’); and (3) a vector grid dataset (2.5 km resolution) containing the predicted number of recreational vessels per cell and predictor variables (‘Recreational_Boating_Model_Results_BC).--Scripts folder which includes R Markdown file with R code to run the modelling analysis (‘Recreational_Boating_Model_R_Script’) and data used to run the code.Methods:Data on recreational vessels were collected by NASP during planned aerial surveys along pre-determined routes along the BC coast from 2018 to 2022. Data on non-AIS recreational vessels were collected using video cameras onboard the aircraft, and data on AIS recreational vessels using an AIS receiver also onboard the aircraft. Recreational boating predictors explored were: water depth, distance to shore, distance to marinas, density of marinas, latitude, and longitude. Recreational vessel traffic models were fitted using Generalized Linear Models (GLM) R packages and libraries used here include: AED (Roman Lustrik, 2021) and MASS (Venables, W. N., Ripley, 2002), pscl package (Zeileis, Kleiber, and Jackman, 2008) for zeroinfl() and hurdle() function. Final model was selected based on the Akaike’s information criterion (AIC) and the Bayes’ information criterion (BIC). An R Markdown file with code use to run this analysis is included in the data package in a folder called Script. Spatial Predictive Model: The selected model, ZINB, consist of two parts: one with a binomial process that predicts the probability of encountering a recreational vessel, and a second part that predicts the number of recreational vessels via a count model. The closer to shore and to marinas, and the higher the density of marinas, the higher the predicted number of recreational vessels. The probability of encountering recreational vessels is driven by water depth and distance to shore. For more information on methodology, consult metadata pdf available with the Open Data record.References:Serra-Sogas, N. et al. 2021. Using aerial surveys to fill gaps in AIS vessel traffic data to inform threat assessments, vessel management and planning. Marine Policy 133: 104765. https://doi.org/10.1016/j.marpol.2021.104765Data Sources:Recreational vessel sightings and survey effort: Data collected by NASP and analyzed by Norma Serra to extract vessel information and survey effort (more information on how this data was analyzed see SerraSogas et al, 2021). Bathymetry data for the whole BC coast and only waters within the Canadian EEZ was provided by DFO – Science (Selina Agbayani). The data layer was presented as a raster file of 100 meters resolution. Coastline dataset used to estimate distance to shore and to clip grid was provided by DFO – Science (Selina Agbayani), created by David Williams and Yuriko Hashimoto (DFO – Oceans). Marinas dataset was provided by DFO – Science (Selina Agbayani), created by Josie Iacarella (DFO – Science). This dataset includes large and medium size marinas and fishing lodges. The data can be downloaded from here: Floating Structures in the Pacific Northwest - Open Government Portal (https://open.canada.ca/data/en/dataset/049770ef-6cb3-44ee-afc8-5d77d6200a12)Uncertainties:Model results are based on recreational vessels sighted by NASP and their related predictor variables and not always might reflect real-world vessel distributions. Any biases caused by the opportunistic nature of the NASP surveys were minimized by using survey effort as an offset variable.