The Oil and Gas Rights dataset contains the digital boundaries for existing exploration licences, significant discovery licences, production licences, former permits, former leases and the Norman Wells Proven Area. These boundaries are available for download on the Northern petroleum pesources Website at https://www.rcaanc-cirnac.gc.ca/eng/1100100036087/1538585604719. The Oil and Gas Rights dataset is Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC) and Indigenous Services Canada (ISC) primary source for northern petroleum titles geographic location on maps.

Wells and reservoirs in the City of Trois-Rivières**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Mapping of watersheds in the territory of Quebec City.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.

This point-layer shows the locations of components that make up facilities (Facilities are stored in another layer). Examples of facility components are barbeques, picnic tables, benches, or kiosks. See the Comp domain for a complete list. NOTE: Although some of the items in the domain appear to be activities, they are actually physical entities that appear within a facility. A facility component point would be stored in this layer to show a more precise location of the kayak rental place of business. Data is not necessarily complete - updates will occur weekly.

This point layer shows the locations of named places that fall within Parks Canada areas of interest. Data is not necessarily complete - updates will occur weekly.

FluWatch is Canada's national surveillance system that monitors the spread of flu and flu-like illnesses on an on-going basis. Activity Level surveillance is a component of FluWatch that provides an overall assessment of the intensity and geographical spread of laboratory-confirmed influenza cases, influenza-like-illness (ILI) and reported outbreaks for a given surveillance region. Activity Levels are assigned and reported by Provincial and Territorial Ministries of Health. A surveillance region can be classified under one of the four following categories: no activity, sporadic, localized or widespread. For a description of the categories, see the data dictionary resource. For more information on flu activity in Canada, see the FluWatch report. (https://www.canada.ca/en/public-health/services/diseases/flu-influenza/influenza-surveillance/weekly-influenza-reports.html) Note: The reported activity levels are a reflection of the surveillance data available to FluWatch at the time of production. Delays in reporting of data may cause data to change retrospectively.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.

This data represents the dryness of the land surface based on vegetation conditions. The data is created weekly and uses weekly information on precipitation anomalies (namely the Standardized Precipitation Index or SPI) and satellite vegetation condition derived from Normalized Difference Vegetation Index (NDVI) from the MODIS Satellite. These dynamic data sets along with static data sets on land cover, soil water holding capacity, irrigation, ecozones and land surface elevation are used to model the drought severity, based on the Palmer Drought Severity Index (PDSI). The mapcubist model was trained on historical data and applied in real time to the dynamic inputs to produce drought severity ratings. The model is run at a 1km resolution and was developed by the AAFC, the United States Geological Survey and the United States Drought Monitor at the University of Nebraska Lincoln.