Linear layer illustrating the lot divisions present in the graphic matrix in the case of properties containing several lots.**Collection context** Maintenance process in collaboration with the evaluation department.**Collection method** Computer-aided mapping.**Attributes*** `ID_MAT_DIV` (`integer`): Identifier* `DATE_CREATION` (`smalldatetime`): Created on* `DATE_MODIFICATION` (`smalldatetime`): Modified on* `USER_MODIFICATION` (`varchar`): Modified by* `MAT10` (`varchar`): Number* `ID_MAT_DIV_OLD` (`varchar`): Old idFor 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).**

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.In 2023 the process was applied to the years 2013 through to 2022 and were made available using the same processes that were applied to the original 2019 datasets.

This layer represents the relative summer density of belugas in the St. Lawrence Estuary based on 35 aerial surveys carried out from 1990 to 2009. The boundaries of the areas were determined by combining the highest densities until the desired proportion of the population was obtained using kernel density estimation in order to obtain a smooth and continuous density distribution.Within Fisheries and Oceans Canada (DFO), the ecosystem approach is considered a tool for operational planning, project implementation and preparation of advisory reports. In response to this strategic direction, the DFO science division is committed to implement the ecosystem approach in its activities as Ecosystem Research Initiatives (ERI) in each of the six administrative regions of DFO. In the Quebec region, two pilot projects were implemented, of which one aimed to define and characterize the habitat of the St. Lawrence beluga (Delphinapterus leucas).Data sources and references:DFO. 2016. Ecosystem Research Initiative (ERI): Integrated Advice on the Summer Habitat of the St. Lawrence Estuary Beluga (Delphinapterus leucas). DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2016/030. Mosnier, A., R. Larocque, M. Lebeuf, J.-F. Gosselin, S. Dubé, V. Lapointe, V. Lesage, V., H. Bourdages, D. Lefaivre, S. Senneville and C. Chion. 2016. Définition et caractérisation de l'habitat du béluga (Delphinapterus leucas) de l'estuaire du Saint-Laurent selon une approche écosystémique. Secr. can. de consult. sci. du MPO. Doc. de rech. 2016/052. vi + 93 p.

All the evaluation units of the graphic matrix of the City of Rouyn-Noranda.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Average grid cell density is a polygon feature class containing the average density value for each grid cell per species/groups and season.

This data shows spatial density of peas cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which peas are more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have peas based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of Canola cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Canola is more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have Canola based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of Corn cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Corn is more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have Corn based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of Soybean cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Soybeans are more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have Soybeans based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of potatos cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which potatoes are more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have potatoes based on analysis of the 2009 to 2021 AAFC annual crop inventory data.