Data presented here comes from the 2016 Census of Canada, Statistics Canada. These datasets are part of the Official-Language Minority Communities Dashboard, an initiative of the Research Team of the Official Languages Branch at Canadian Heritage. Four datasets have been organized based on different population groups and universes and explore selected variables such as age, language, socio-economic and socio-demographic characteristics.

The “AAFC Annual Unit Runoff in Canada" data series illustrates runoff trends across the country by isolines of annual unit runoff for a variety of probabilities of exceedence commonly used by decision makers Annual unit runoff is a measure of runoff volume per square kilometre. This series uses units of cubic decametres (1000 m3) per square kilometre (dam3/km2), which is equivalent to millimetres depth on the landscape. It includes a point data set for the hydrologic stations that were analyzed and seven sets of line work to show the adjusted isolines for 10%, 25%, 50%, 70%, 75%, 80%, and 90% probability of exceedence.

Fish Habitat Assessment Output: 3 of 16 High Water Level (75.4m ASL) - Nursery Habitat - High Vegetation Association Species (All Temperature Windows) Habitat suitability was assessed for the Bay of Quinte Area of Concern, at a 3 m grid resolution, using the Habitat Ecosystem Assessment Tool (HEAT), temperature algorithms, vegetation models, and water level input. Habitat classifications were based on three variables: depth (elevation), vegetation, and substrate; and modified by temperature suitabilities. The final suitability maps were based on documented habitat and temperature associations for the fish in the area. Different life stages (spawning requirements, nursery habitat, adult habitat) were modeled for the years of 1972-2011. Suitability values were scaled from 0 (not suitable) to 1 (highly suitable) and converted to suitability classes of very low, low, medium, and high. The final maps for each guild – life stage combination are maximum suitability values from the 39-year period modelled.

The Agriculture and Agri-Food Canada’s (AAFC) Watersheds Project level series supplies a number of watershed and watershed related datasets for the Prairie Provinces. The levels are greater or smaller assemblages of hydrometric areas, or the components defining them. The Project is organized by hydrometric gauging stations which are sourced from Environment Canada, the United States, and Canadian provinces. Additional stations were generated to address structural issues, like river confluences or lake inlets. Collectively, they are referred to as the gauging stations, or simply, the stations. The drainage area that each station monitors, between itself and one or more of its upstream neighbours, is called an ‘incremental gross drainage area’. The incremental gross drainage areas are collected into larger or smaller groupings based on size or defined interest to generate the various ‘levels ’of the series. They include: Basins of varying size: 1. Major drainage systems (3): Arctic Ocean, Hudson Bay and Gulf of Mexico; 2. Major basins (23): associated with river or lake reaches; 3. Project sub-basins (47): created specifically for the project; 4. Sub-basins (51): based on specific Environment Canada hydrometric gauging station locations; 5. Sub-sub-basins (311): based on specific Environment Canada hydrometric gauging station locations); Incremental drainage areas: 6. Incremental gross drainage areas: one per gauging station. The incremental gross drainage areas are further subdivided into portions that either contribute or do not contribute to drainage to an average runoff event. The portions that do contribute are called ‘effective drainage’ areas, while those that don’t are called ‘non-contributing’. These generate the following levels: 7. Incremental effective drainage areas; and 8. Incremental non-contributing areas. Total drainage areas: 9. Total gross drainage areas; 10. Total effective drainage areas; and 11. Total non-contributing areas; And when combined for the entire project, yields the: 12. Effective drainage area. The series also includes the components: 13. The gauging stations; 14. The collection of boundaries (lines) of the gross incremental drainage areas as well as the boundaries that separate contributing from non-contributing areas for an average runoff event; and 15. A network of downstream-directed lines that connect the gauging stations. All linework is derived from large scale topographic data. One additional non-spatial dataset, a table of tallied values by gauging station, is provided: 16. The Project Gauging Station Table. The Project area, designed for the Prairie Provinces, covers all of Alberta, Saskatchewan and Manitoba, and those portions of British Columbia, Northwest Territories, Nunavut, Ontario, and the United States that are required to complete the trans-border sections of the watersheds. Since 1975 the AAFC Watersheds Project has systematically collected and refined watershed boundaries for the Prairies. The result is the authoritative source for gross and effective drainage areas in the Prairie Provinces. The initial 1:50,000 analog delineations were moved to their digital form in 1994. Since then, the delineations have increased in accuracy and extent, and the series levels have increased to 16 in number.

The Agri-Environmental Indicator Risk of Water Contamination by Pesticides dataset reports the annual and semi-decadal status of pesticide transport to surface water, the concentration of pesticide in ground water, and the risk of water contamination by pesticide. Products in this data series present results for predefined areas as defined by the Soil Landscapes of Canada (SLC v.3.2) data series, uniquely identified by SOIL_LANDSCAPE_ID values.

Precipitation percentiles represents the accumulated precipitation (mm) for the time period compared to historical information for the same time period. This comparison ranks the current precipitation amount and assigns it a percentile value based on a historic record. Time periods calculated for monthly precipitation percentiles are 1, 2, 3, 6, 9, 12, 18, 24 months. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday. Historical record goes back to 1980. The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

“Biomass Inventory Mapping and Analysis – Business Data” provides a number of datasets related to the yield and production of residues from the agricultural and forestry industry, agricultural crops, and municipal solid wastes across Canada. The datasets contain agricultural residue production information (i.e., straw or stover) for barley, wheat, flax, oats and corn, and crop production information for barley, wheat, flax, oats, corn, canola and soybean. They also include information about amounts of straw required for cattle bedding and feeding, the type of tillage used in an area, and the amount of residue needed for soil conservation purposes. Datasets in the series provide the yield, production and other information for the median year and 1-in-10 year and 1-in-20 year lows. The forestry inventory dataset provides information about the location and quantity of residues from the forestry industry, as well as urban wood waste and potential sites and productivity of plantations of fast-growing trees that are grown as feedstock. Forestry residues include material left at the roadside after harvesting and excess and waste materials from mills. The municipal solid waste inventory dataset provides information about the approximate location and quantity of different types of municipal solid wastes, such as organics (including food and yard), paper and total. A transportation network dataset and datasets that are used to calculate cost to harvest and transport biomass are also included in this series.

A soil survey is an inventory of soils and their spatial distribution over a landscape. Soil survey reports contain two parts. The first part is a soil map or series of maps at a particular scale with coding for each soil. Soil survey reports also include a supporting document that contains background information such as how the soil survey was conducted, and an explanation of interpretive criteria and a summary of the area occupied by various soil types. The detailed soil surveys identify more of the variation in soil types across smaller landscapes, as compared to Generalized (1:100 000, i.e. provincial overview) and Reconnaissance or General (1:125 000, or 1/2 inch to 1 mile.) soil surveys. Detailed soil survey information is much more accurate and reliable for making decisions at the farm-level. Soil surveys have been published for most of the agricultural areas, and many surrounding areas, across Canada. Data from these surveys comprise the most detailed soil inventory information in the National Soil Database (NSDB). Version 3 was created by Agriculture and Agri-Food Canada in the 2010's by amalgamating version 2 data. It introduced some minor refinements to the version 2 data structure to provide closer alignment with the Soil Landscapes of Canada data structure.

Multi-model ensembles of sea ice concentration based on projections from twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of sea ice concentration as represented as the percentage (%) of grid cell area, are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Mean Temperature Difference From Normal values are computed by subtracting the normal monthly average temperature from the average monthly temperature of the month. The average monthly temperature is computed by obtaining the mean value of average daily temperatures for a month. If the month was colder than normal the value computed will be negative and if it was warmer the value will be positive.