Street segments of the road network.attributes:ID - Unique identifierToponymy - Full street nameNorte - Road numberCivic NumberOriginLeft - Civic number that originated the segment on the left side according to the direction of digitalizationNumberCivicOriginRight - Civic number that originated the segment on the right side according to the direction of digitalizationCivic NumberDestinationLeft - Civic number destined for segment on the left side according to the direction of digitalizationCivic NumberDestinationRight - Civic number to the segment on the right side according to the direction of DigitalNameGeneric - Short name of the streetType - Street typeStreet typeStreet typeSegmentStreet - Hierarchical class of the segment in the networkSpeed - Speed limit displayTypesUnique - Indication relating to the presence of a one-way wayMunicipal - Municipal code - Municipal codeHeavy traffic - Indication relating to heavy traffic**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Municipal public parking.attributes:ID - Unique IDName - Parking nameNumber of parking spaces - Number of parking spaces**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The fire regime describes the patterns of fire seasonality, frequency, size, spatial continuity, intensity, type (e.g., crown or surface fire) and severity in a particular area or ecosystem.The number of large fires refers to the annual number of fires greater than 200 hectares (ha) that occur per units of 100,000 ha. It was calculated per Homogeneous Fire Regime (HFR) zones. These HFR zones represent areas where the fire regime is similar over a broad spatial scale (Boulanger et al. 2014). Such zonation is useful in identifying areas with unusual fire regimes that would have been overlooked if fires had been aggregated according to administrative and/or ecological classifications.Fire data comes from the Canadian National Fire Database covering 1959–1999 (for HFR zones building) and 1959-1995 (for model building). Multivariate Adaptive Regression Splines (MARS) modeling was used to relate monthly fire regime attributes with monthly climatic/fire-weather in each HFR zone. Future climatic data were simulated using the Canadian Earth System Model version 2 (CanESM2) and downscaled at a 10 Km resolution using ANUSPLIN for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century.Provided layer: the number of large fires (>200 ha) across Canada for a reference period (1981-2010).Reference: Boulanger, Y., Gauthier, S., et al. 2014. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Canadian Journal of Forest Research 44, 365–376.

The fire regime describes the patterns of fire seasonality, frequency, size, spatial continuity, intensity, type (e.g., crown or surface fire) and severity in a particular area or ecosystem.The number of large fires refers to the annual number of fires greater than 200 hectares (ha) that occur per units of 100,000 ha. It was calculated per Homogeneous Fire Regime (HFR) zones. These HFR zones represent areas where the fire regime is similar over a broad spatial scale (Boulanger et al. 2014). Such zonation is useful in identifying areas with unusual fire regimes that would have been overlooked if fires had been aggregated according to administrative and/or ecological classifications.Fire data comes from the Canadian National Fire Database covering 1959–1999 (for HFR zones building) and 1959-1995 (for model building). Multivariate Adaptive Regression Splines (MARS) modeling was used to relate monthly fire regime attributes with monthly climatic/fire-weather in each HFR zone. Future climatic data were simulated using the Canadian Earth System Model version 2 (CanESM2) and downscaled at a 10 Km resolution using ANUSPLIN for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century.Provided layer: projected number of large fires (>200 ha) across Canada for the medium-term (2041-2070) under the RCP 8.5 (continued emissions increases).Reference: Boulanger, Y., Gauthier, S., et al. 2014. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Canadian Journal of Forest Research 44, 365–376.

The fire regime describes the patterns of fire seasonality, frequency, size, spatial continuity, intensity, type (e.g., crown or surface fire) and severity in a particular area or ecosystem.The number of large fires refers to the annual number of fires greater than 200 hectares (ha) that occur per units of 100,000 ha. It was calculated per Homogeneous Fire Regime (HFR) zones. These HFR zones represent areas where the fire regime is similar over a broad spatial scale (Boulanger et al. 2014). Such zonation is useful in identifying areas with unusual fire regimes that would have been overlooked if fires had been aggregated according to administrative and/or ecological classifications.Fire data comes from the Canadian National Fire Database covering 1959–1999 (for HFR zones building) and 1959-1995 (for model building). Multivariate Adaptive Regression Splines (MARS) modeling was used to relate monthly fire regime attributes with monthly climatic/fire-weather in each HFR zone. Future climatic data were simulated using the Canadian Earth System Model version 2 (CanESM2) and downscaled at a 10 Km resolution using ANUSPLIN for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century.Provided layer: projected number of large fires (>200 ha) across Canada for the short-term (2011-2040) under the RCP 8.5 (continued emissions increases).Reference: Boulanger, Y., Gauthier, S., et al. 2014. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Canadian Journal of Forest Research 44, 365–376.

This product provides population counts for 2001 and 2016 for 105 census subdivisions (CSDs) for which the forest sector is a major source of employment income—defined by Natural Resources Canada as 20% or more of total CSD income excluding government transfers. These files were produced by Statistics Canada, Environment, Energy and Transportation Statistics Division, 2018, special tabulation from the 2001 and 2016 Census of Population; Natural Resources Canada, Canadian Forest Services, Economic Analysis Division; Canada’s National Forest Inventory (NFI), 2016, Grouped kNN Map layers, http://tree.pfc.forestry.ca (accessed April 7, 2017). Data from the 2016 Census of Population were used to identify the 105 census subdivisions. Note that changes occur to the number and the boundaries of CSDs between censuses. Adjustments were made to CSD boundaries to account for changes.Some data were suppressed for data quality reasons or to meet the confidentiality requirements of the Statistics Act. Income data were available for 3,675 of 5,162 CSDs. This analysis may therefore underreport the total number of communities for which the forest sector is a major economic driver. Note that a decline in the percentage of forest sector income may be due to a decrease in forest sector income or an increase in income from other sources. The reference period for income data in the Census of Population is the calendar year prior to the census.The forest sector includes North American Industry Classification codes 113 – forestry and logging, 1153 – support activities for forestry and logging, 321 – wood product manufacturing and 322 – paper product manufacturing.

The NRN product is distributed in the form of thirteen provincial or territorial datasets and consists of two linear entities (Road Segment and Ferry Connection Segment) and three punctual entities (Junction, Blocked Passage, Toll Point) with which is associated a series of descriptive attributes such as, among others: First House Number, Last House Number, Street Name Body, Place Name, Functional Road Class, Pavement Status, Number Of Lanes, Structure Type, Route Number, Route Name, Exit Number. The development of the NRN was realized by means of individual meetings and national workshops with interested data providers from the federal, provincial, territorial and municipal governments. In 2005, the NRN edition 2.0 was alternately adopted by members from the Inter-Agency Committee on Geomatics (IACG) and the Canadian Council on Geomatics (CCOG). The NRN content largely conforms to the ISO 14825 from ISO/TC 204.

This map, created in 2002 using ArcGIS, describes the number of animal and plant species that are at risk in Alberta. 'Species at risk' is a term used by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) that includes the following categories of plants and animals:Extirpated species - no longer in the wild in Canada.Endangered species - species facing imminent extirpation or extinction.Threatened species - likely to become an endangered species if nothing is done to reverse factors leading to its extirpation or extinction.Species of special concern - species that may become threatened or endangered due to biological characteristics or identified threats.

The Alberta Regeneration Information System (ARIS) requires a unique identifier assigned to a cutblock to enable tracking within ARIS. This number is generated from a point roughly derived from the centre of the cutblock. The number is a concatenation of the point's legal description plus a grid cell number. The format is MRRTTTSSGG where M - Meridian, RR - Range, TTT - Township, SS - Section, GG - grid cell. The MRRTTSS information is derived with reference to the Alberta Township System. The grid cell is derived from a 10 by 10 grid that is overlaid on the section that the centre of the cutblock is contained in. Grid cells are numbered between 00 - 99 with the grid origin at the bottom left corner of the section and anchored to the centre of grid cell 00. The first digit represents the grid column and the second digit is the grid row of the 10 by 10 matrix. Note that in some cases a letter may be appended to the end of the opening number where an opening number had to be split between two cutblocks for some reason. For example, cutblocks may have the same basic opening number but one is differentiated from the other with one having an A and the other having a B appended to the end of the base opening number.This dataset contains all the potential opening numbers in the Green Area of the province, either as a whole or by Forest Management Unit (FMU).

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.