Set of geographic data concerning the trucking regulations of the City of Montreal and the agglomeration of Montreal. A wired data set identifies regulatory requirements and a surface data set identifies areas prohibited to trucks except for local delivery.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Graphic representation (linear element) of the streets on which trucks, tool vehicles and equipment vehicles are authorized to drive at all times or during the day only (7:00 a.m. to 7:00 p.m.) as well as the traffic signs (point element) attached to them (L-10400). Note that the display of the panels requires the use of the Laval_signalisation.ttf font**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Data collected between 2005 to 2007, 3% of sample collected in 2005, 51% in 2006 and 46% in 2007. This dataset contains a compilation of data collected from different sources. **VOG 06/** **VOG** **08 (Value of Goods)** : The data is derived from the information collected in the 2006 Ontario Commercial Vehicle Survey. This survey is a roadside intercept survey of truck drivers, which collects information about the trip, commodity and the vehicle. The survey primarily captures intercity trucking activity and under-represents truck flows in urban areas. The value of goods provided in this table is derived from the Commercial Vehicle Survey, but factored up to represent the overall trucking activity on the network segment for 2006 and 2008. **AADTT 2006 and ****AADTT** **2008:** The data is derived from the Ministry of Transportation's (MTO) inventory of annual traffic data for the Provincial Highways. The commercial volumes are first calculated using the AADT and the Commercial Percentage values for each traffic segment. These values are then adjusted to remove variations between segments caused by fluctuations in AADT. The volume given for each direction is one-half of the total value. MTO does not maintain volume by direction. For freeway segments with core/collector configuration, the total volume is divided into four equal portions and assigned to each stream. **Hourly Truck Volumes ( WD00-23 and WN00-23): ** These fields contain estimates of average hourly volumes for a typical weekday and weekend day. The estimates are based on observed hourly distribution at more than 100 directional Commercial Vehicle Survey sites across the province, **AADTT** and other information. **RD _NAME:** Name of the road **VOG** **06:** 2006 average daily value of goods assigned to road network link by directions. **VOG** **08:** 2008 average daily value of goods assigned to road network link by directions. **AADTT** **2006:** 2006 Annual Average Daily Truck Traffic; it is the truck volume assigned to road network link by directions. **AADTT** **2008:** 2008 Annual Average Daily Truck Traffic; it is the truck volume assigned to road network link by directions. **WD** **00-23:** 2008 Weekday ( **WD** ) hourly truck volume; 00 - 23 represents starting hour of the day (e.g. 12 represents 12 P.M. - 1 P.M.). **WN** **00-23:** 2008 Weekend ( **WN** ) hourly truck volume; 00 - 23 represents starting hour of the day (e.g. 12 represents 12 P.M. - 1 P.M.). *[ WD]: Week day *[VOG]: Value of Goods *[AADTT]: Annual Average Daily Truck Traffic *[WN]: Week end *[RD]: Road *[WD]: Week day *[MTO]: Ministry of Transportation *[AADT]: Annual Average Daily Traffic

This dataset identifies locations of wilderness and recreation tourism activities. Activities are: fishing, biking, hiking, snowmobiling, rafting, boating, flight seeing, dog mushing, driving tour, off-road vehicle, cross country skiing, canoeing, wildlife viewing. The locations were collected through interviews. Locations and areas were indicated on paper maps and transferred to digital. This is not a complete or up to date dataset. Data was collected in 2009.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

This dataset provides locations of highway maintenance camps. Maintenance Camps house the staff and equipment that maintain Yukon highways.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

Land authorizations representing the road right of way for road activities. The spatial data includes polygon data for approved and post-construction road rights of way collected on or after October 30, 2006. This dataset is updated nightly.

Highway Maintenance Class defines the level of service for maintenance of the highway, normally, a function of the traffic usage type and traffic volume. This layer contains polylines

Rent calculation for the use of Crown land for log handling purposes is based on zonal land values. There are currently 11 zones covering the province. The dataset outlines the boundaries for these zones.

All stops in the public transport network managed by the City of Rouyn-Noranda**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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.