Transportation networks (transportation)The means and aids for conveying people and goods. For example, resources describing roads, airports and airstrips, shipping routes, tunnels, nautical charts, vehicle or vessel location, aeronautical charts, and railways.

The locations of coastal British Columbia marine navigation aids. The Coastal BC datasets are circa 2004 and legacy in nature. Caution should be exercised when using this data, as it may not be accurate or complete. There are currently no plans to update.

Businesses licensed to test, fit and/or sell hearing aids

The transport features of the CanVec series are derived from the National Road Network (NRN) and the National Railway Network (NRWN). The dataset describes roads, trails, bridges, railroads and their related information. The transport features include nautical facilities, track segments, track junctions, railway stations, track crossings, track marker posts, track structures, rail ferry segments, road segments, road ferry segments, road junctions, blocked passages, toll points, aerial cableway features, footbridges, trails, navigational aids, marinas and runways.The CanVec multiscale series is available as prepackaged downloadable files and by user-defined extent via a Geospatial data extraction tool.Related Products (Open Maps Links):[Topographic Data of Canada - CanVec Series](https://open.canada.ca/data/en/dataset/8ba2aa2a-7bb9-4448-b4d7-f164409fe056)

This polygon layer presents the spatial distribution of forecasted accumulated precipitation from the Global Forecast System (GFS) over watershed sub‑basins. GFS APCP raster data are overlaid with global watershed boundaries, and zonal statistics are computed to derive average precipitation per sub‑basin over a 7‑day (168‑hour) period. This product aids in global disaster preparedness and water management planning.GFS model output is processed into APCP rasters that capture accumulated precipitation over a 7‑day forecast period. These rasters are then combined with watershed boundary data, and zonal statistics are applied to compute average precipitation for each sub‑basin. The final polygon features provide a clear depiction of global rainfall and snowfall patterns, offering critical information for disaster risk management and international water resource planning.

This polygon layer displays ensemble-based, medium-range precipitation forecasts from the Global Ensemble Prediction System (GEPS), offering a probabilistic view of future rainfall or snowfall over a 16‑day horizon. It aids in uncertainty analysis, risk assessment, and strategic resource planning.Ensemble Approach: GEPS runs multiple perturbed members of ECCC’s GEM model, capturing a range of atmospheric evolutions and yielding probability distributions for precipitation. Global Domain: Similar coverage to the GDPS but focuses on ensemble mean, spreads, and probabilities rather than a single deterministic outcome. Longer-Range Outlook: Extends up to 16 days, supporting risk-based planning for potential floods, extended rainfall events, or dryness. Data Utility: Allows decision-makers to weigh confidence levels in precipitation scenarios, vital for water management, agriculture, and emergency contingency strategies.

This polygon layer displays sub-basin-level average precipitation derived from the ECMWF (European Centre for Medium-Range Weather Forecasts) model. This layer helps hydrologists, forecasters, and planners see how much rainfall/snowfall is predicted or has occurred in each sub-basin, supporting medium-range water resource and flood management. We are intersested in the forecast period of 7 days.This layer aggregates ECMWF forecast precipitation over polygonal sub-basins. Each feature includes attributes for average accumulated precipitation, forecast run/valid times, and sub-basin identifiers. ECMWF is a leading global model offering medium-range (up to 10 days) forecasts at a high skill level. By focusing on sub-basins, this layer aids in local-scale decision-making—enabling more precise flood risk assessments, reservoir inflow estimates, and water resource planning across the region of interest.

As the COVID-19 pandemic spreads, researchers and health professionals have noted large differences in the impact that the infection has on individuals. Whereas some remain asymptomatic and unaware of their infection or experience only mild symptoms, others require hospitalization, ventilation, and may even die. As research evidence accumulates, both nationally and internationally, it appears that certain health characteristics, such as obesity or the presence of chronic conditions, increase the risk of severe outcomes among those who are infected with the novel coronavirus.To better understand which segments of the Canadian population may be vulnerable to severe health outcomes related to COVID-19, Statistics Canada and the Public Health Agency of Canada have worked collaboratively to build an index of underlying health conditions in the adult household population. Using information from the 2017/2018 Canadian Community Health Survey, new data tables released today estimate the proportion of the adult household population who may be at greater risk of severe health outcomes related to COVID-19 due to the presence of underlying health conditions.

Table containing information relevant to animal disease investigations in Manitoba from 2012 to present.This table contains information relevant to animal disease investigations in Manitoba from 2012 to present, conducted by the Chief Veterinary Office (CVO). Information includes year, number of sites, number of linked sites, animal species, disease types and results. Updated on a weekly basis.  It is important that users are aware of the following caveats when reviewing data presented in the Animal Disease Investigations Dashboard: 1. Each investigation can have one or more cases involved depending on the number of herds or animals exposed.  Not all disease investigations are handled the same due to a partnership approach.  Diseases can be detected via surveillance, ad hoc reporting, or through other programs. 2. Rabies is a separate program. Please see Manitoba's Provincial Rabies Management Program for data related to Rabies Surveillance.3. Certain zoonotic diseases, such as salmonella or influenza, are also captured in more detail through other means.  The total occurrence of a zoonotic disease represented in this dashboard reflects occurrences where risks or exposures were deemed significant enough to warrant further investigation.  4. Historically, One Health Investigations that were predominantly focused on Public Health issues rather than Animal Health concerns were not captured in this system and will be underrepresented here. Fields included ( Alias (Field Name): Field description.) Year (Year): Year of the disease investigation Number of Sites (Number_of_Sites): Number of investigation sites Number of Linked Sites (Number_of_Linked_Sites): Number of sites linked to investigation sites Species/Class (Species__Class): Group of animal species Disease Type (Disease_Type): The type of disease that is being investigated Result (Result): The outcome (positive/negative) for the corresponding animal disease investigation

Boundaries developed for use when aggregating cases and reporting during the COVID-19 pandemic in Saskatchewan.The province of Saskatchewan has been divided into six distinct areas for use in reporting the infection rates of COVID-19 during the 2020 outbreak of the virus. This dataset is 'Deprecated'. Please use updated source here.