Keyword

Geography

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  • This dataset was compiled as part of a multiyear effort lead by Fisheries and Oceans Canada (DFO) to support sustainable aquaculture regulation in the Coast of Bays, an area of the south coast of Newfoundland. It is the first of a series aiming to provide an oceanographic knowledge baseline of the Coast of Bays. This dataset consists of GIS products and analyses summarized in a spreadsheet. The GIS data include vector shapefiles and raster TIFF images, providing information on the area of interest physical dimensions (e.g. bays area, volume, perimeter, length and width) and other physical characteristics (e.g. tidal volume and freshwater input). A full description of the data and of its use in the context of the motivating project can be found in http://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2017/2017_076-eng.html. Analyses from this dataset were presented during a Canadian Science Advisory Secretariat (CSAS) meeting which took place in St John’s in March 2015 (http://www.dfo-mpo.gc.ca/csas-sccs/schedule-horraire/2015/03_25-26b-eng.html) and from which a Science Advisory Report (http://www.dfo-mpo.gc.ca/csas-sccs/Publications/SAR-AS/2016/2016_039-eng.html) and Proceedings (http://www.dfo-mpo.gc.ca/csas-sccs/Publications/Pro-Cr/2017/2017_043-eng.html) were published.

  • The dataset includes timeseries of horizontal current speed and direction, vertical current speed, water depth, and temperature at instrument depth from Acoustic Doppler Current Profiler (ADCP) moorings. Data were collected as part of a multiyear effort lead by Fisheries and Oceans Canada (DFO) to support sustainable aquaculture regulation in the Coast of Bays, an area of the south coast of Newfoundland. This dataset is the third of a series aiming to provide an oceanographic knowledge baseline of the Coast of Bays, Newfoundland. It consists of 73 ADCP timeseries varying in length from about 26 days to 235 days collected between 2009 and 2014. Analyses from this dataset were presented during a Canadian Science Advisory Secretariat (CSAS) meeting which took place in St John’s in March 2015 (http://www.dfo-mpo.gc.ca/csas-sccs/schedule-horraire/2015/03_25-26b-eng.html) and from which a Science Advisory Report (http://www.dfo-mpo.gc.ca/csas-sccs/Publications/SAR-AS/2016/2016_039-eng.html), Proceedings (http://www.dfo-mpo.gc.ca/csas-sccs/Publications/Pro-Cr/2017/2017_043-eng.html) and several research documents were published.

  • Community Health Service Area (CHSA) boundaries; 2018 boundary configuration. On April 1, 2019, the Ministry of Health released a new geography classification that introduces community-level geographies nested within the Local Health Areas. The CHSAs are a mutually exclusive and exhaustive classification of the land area in BC. CHSAs are contiguous (land area is geographically adjacent) and fit within the existing geographical hierarchy (cannot violate higher-level geography boundaries such as the Local Health Area).

  • In 2015, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

  • In 2010 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) continued the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5, DMC) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.

  • The digital Advance Polling District boundary files provided are made available from Elections Canada. The data contains the digital federal electoral districts under the Representation Order of 2013.

  • Canada is divided into 308 electoral districts. A representative or member of Parliament is elected for each electoral district. Following the release of population counts from each decennial census, the Chief Electoral Officer determines the number of seats in the House of Commons and publishes the information in the Canada Gazette. Electoral boundaries commissions then determine the adjustments to the constituency boundaries. The federal electoral boundaries commissions are independent bodies that make all decisions regarding the proposed and final federal electoral boundaries. Elections Canada provides support services to the boundaries commission in each province. Based on reports from these commissions, the Chief Electoral Officer prepares a representation order that describes the boundaries and specifies the name and the population of each FED. The representation order is in force on the first dissolution of Parliament that occurs at least one year after its proclamation. The 2003 Representation Order (proclaimed on August 25, 2003) was based on 2001 Census population counts, and increased the number of FEDs to 308, up from 301 from the previous 1996 Representation Order. Ontario received three additional seats, while Alberta and British Columbia each gained two seats. The names of FEDs may change at any time through an Act of Parliament.

  • This is a legacy product that is no longer supported. It may not meet current government standards. The Canadian Digital Surface Model (CDSM) is part of Natural Resources Canada's altimetry system designed to better meet the users' needs for elevation data and products. The 0.75-second (~20 m) CDSM consists of a derived product from the original 1-second (30 m) Shuttle Radar Topographic Mission (SRTM) digital surface model (DSM). In these data, the elevations are captured at the top of buildings, trees, structures, and other objects rather than at ground level. A CDSM mosaic can be obtained for a pre-defined or user-defined extent. The coverage and resolution of a mosaic varies according to the extent of the requested area. Derived products such as slope, shaded relief and colour shaded relief maps can also be generated on demand by using the Geospatial-Data Extraction tool. Data can then be saved in many formats. The pre-packaged GeoTiff datasets are based on the National Topographic System of Canada (NTS) at the 1:50 000 scale; the NTS index file is available in the Resources section in many formats.

  • In 2009 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology. The initial methodology was developed in partnership with AAFC Research Branch, and supported in part by the Canadian Space Agency. The long-term objective of this endeavour is to expand from the Prairies and produce an annual crop inventory of the entire agricultural extent of Canada.

  • In 2011, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) expanded the process of generating annual crop inventory digital maps using satellite imagery to include British Columbia, Ontario, Quebec, and the Maritime provinces, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-5, DMC) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.