Railway network on the territory of the city. **Collection context** Historical data from the Government of Quebec. Additional data by photointerpretation. **Collection method** Computer-aided mapping. **Attributes** * `ID_VFR` (`integer`): Identifier * `SOURCE` (`varchar`): Source * `DATE_CREATION` (`smalldatetime`): Created on * `DATE_MODIFICATION` (`smalldatetime`): Modified on * `USER_MODIFICATION` (`varchar`): Modified by For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Electoral division of the 2017 election. **Collection context** Creation of districts in collaboration with the legal services and the electoral data of the Director General of Elections (DGE). Balancing of districts according to anthropogenic constraints and number of voters. **Collection method** Computer-aided mapping. **Attributes** * `ID_SEC_DIS` (`long`): Identifier * `NAME_DISTRI` (`varchar`): District name * `NO` (`long`): District number * `AREA` (`varchar`): Area * `ADVISORY_NAME` (`varchar`): Name of the advisor * `SOURCE` (`varchar`): Source * `DATE_CREAT` (`date`): Creation date * `DATE_MODIF` (`date`): Date of modification * `USER_MODIF` (`varchar`): Modified by For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Major assignment layer. **Collection context** Taken from the zoning by-law. **Collection method** Computer-aided mapping. **Attributes** * `ID_ASSIGNATION` (`integer`): Identifier * `ASSIGNMENT` (`varchar`): Assignment * `CODE_ASSIGNATION` (`varchar`): Assignment code * `ZON_AREA` (`numeric`): Area * `SECTOR` (`varchar`): Sector * `NOTE` (`varchar`): Note * `DATE_CREATION` (`smalldatetime`): Created on * `DATE_MODIFICATION` (`smalldatetime`): Modified on * `USER_MODIFICATION` (`varchar`): Modified by * `SOURCE` (`varchar`): Source For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Polygonal layer of electoral districts for the 2025 election. New balancing of voters increases the number of districts from 11 to 10 for the 2025 election. **Collection context** Review committee to balance the districts according to the data of the Chief Electoral Officer. **Collection method** Analysis of voters by address using cartographic analysis software. Update by computer-aided mapping. **Attributes** * `ID_SEC_DISTRICT_ELEC` (`integer`): Identifier * `DISTRICT_NAME` (`varchar`): District name * `NO` (`integer`): Number * `AREA` (`varchar`): Area * `ADVISOR_NAME` (`varchar`): Recommended * `SOURCE` (`varchar`): Source * `DATE_CREATION` (`smalldatetime`): Created on * `DATE_MODIFICATION` (`smalldatetime`): Modified on * `USER_MODIFICATION` (`varchar`): Modified by For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Buffer zone of 300 meters around the railways present on the territory of the city. Used in public safety analyses. **Collection context** Derived from the layer of railways in the territory of the city of Saint-Hyacinthe. **Collection method** Buffer zone of 300 meters. Spatial analysis. **Attributes** * `Id` (`long`): Identifier For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In 2019, 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, Sentinel-2) 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 in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, and Guelph.

A novel towfish incorporating sidescan and video hardware was used to ground truth echosounder data for the nearshore of Halifax Harbour. The resulting sampling grid extended from the shoreline to a depth of 10 m, including Bedford Basin through the Inner Harbour to the Outer Harbour. Each of these three zones could be distinguished from the others based upon combinations of substrate type, benthic invertebrates, and macrophyte canopy. Bedford Basin had a relative lack of macrophytes and evidence of intense herbivory. The Inner Harbour was characterized by shoreline hardening due to anthropogenic activities. The Outer Harbour was the most “natural” nearshore area with a mix of bottom types and a relatively abundant and diverse macrophyte canopy. All survey data were placed into a GIS, which could be used to answer management questions such as the placement and character of habitat compensation projects in the harbour. Future surveys utilizing similar techniques could be used to determine long term changes in the nearshore of the harbour. Cite this data as: Vandermeulen H. Data of: A Video, Sidescan and Echosounder Survey of Nearshore Halifax Harbour. Published: September 2021. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/9122c3e2-3cfc-45d0-ac36-aecb306130f6

The data in this layer represents habitat suitability of soft-shelled clams (Mya arenaria) in the DFO Maritimes region, and was developed using an interdepartmental approach. Substrate classification data as well as bathymetric data for the Region were used to identify potential habitat for soft-shelled clams. Substrates identified as suitable included: sand, mud, sand and mud (Greenlaw, 2022). Contours (0m and 70m) from GEBCO bathymetric data were used to isolate depths between which soft-shelled clams are present. At this stage, a polygon reflecting soft substrates from 0-70m was created as "Suitable". A "Not Suitable" layer was similarly created using the substrates: boulders, continuous bedrock, discontinuous bedrock, gravel, mixed sediment, sand and gravel. To digitally validate the model, the Regional shoreline was divided into subsectors (developed by Environment and Climate Change Canada for the Canadian Shellfish Sanitation Program). Data from DFO (clam harvesting intensity) as well as Conservation and Protection (clam harvesting infraction locations) were used to established species presence within each sub-sector. If there had been any harvesting activity, legal or illegal, in an individual subsector, it was considered "Suitable and Validated". Merged into one final product, the model includes areas that are "Not Suitable", "Suitable", as well as "Suitable and Validated" for soft-shelled clam habitat. Cite this data as: Harvey, C., Vincent, M., Greyson, P., Hamer, A. (2024) Data of: A Soft-Shelled Clam (Mya arenaria) Habitat Suitability Model For The DFO Maritimes Region. Published: January 2024. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/c76f7813-d802-4b31-8ebe-476f8a7cacf2

Canada’s landmass is very diversified and comprises 7 distinctive areas called physiographic regions, each of which has its own unique topography and geology. Physiographic regions are large areas that share similar relief and landforms shaped by common geomorphic processes and geological history. Physiographic regions are often used to describe Canada’s geography to show regional differences in climate, vegetation, population and the economy. This dataset collection contains three interrelated datasets mapping the location of Canada’s 7 different physiographic regions, their 21 subregions and many divisions (landforms).

The OPS has adopted these boundaries for administrative and functional purposes which will facilitate enterprise initiatives and enhance customer service. We are no longer updating this data. It is best suited for historical research and analysis.