This game presents the daily portrait of the number of confirmed cases of COVID-19 in Quebec. Important note: As of April 12, 2023, the data source for COVID-19 deaths has changed. Data is updated on a weekly basis. Cases and deaths that occurred on the Sunday, Monday and Tuesday before the Wednesday went online are not available. Please refer to the methodology notes for more details.

Ce jeu présente le portrait quotidien du nombre de cas confirmés de COVID-19 au Québec. Note importante : Depuis le 12 avril 2023, la source de données des décès attribuables à la COVID-19 a été modifiée. Les données sont mises à jour hebdomadairement. Les cas et décès ayant eu lieu le dimanche, lundi et mardi précédent la mise en ligne du mercredi ne sont pas disponibles. Veuillez consulter les notes méthodologiques pour plus de détails.

This dataset provides Canadians and researchers with preliminary data on the confirmed cases of coronavirus (COVID-19) in Canada. Given the rapidly-evolving nature of this situation, these data are considered preliminary. The dataset was downloaded from Statistics Canada as a CSV file. Due to the size of the data file, it was imported into SPSS from which 5 CSV files were exported based on the region variable. The regions are Atlantic, Quebec, Ontario and Nunavut, Prairies and Northwest Territories, and British Columbia and Yukon. This dataset is a custom extraction of Preliminary dataset on confirmed cases of COVID-19, Public Health Agency of Canada

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

The emergency data schedule shows the number of patients on a stretcher, the number of patients on a stretcher more than 24 hours, and the number of patients on a stretcher more than 48 hours from the last hour. The results are presented for each installation in the whole of Quebec in the form of a database. The data comes from the Provincial Emergency Console (CPU), and is updated every hour.

Adult criminal courts, charges and cases by offence, age and sex of accused and type of decision, Canada, provinces, territories, ten jurisdictions and eight jurisdictions, five years of data.

Adult criminal courts, type of guilty finding by offence, age and sex of accused and most serious sentence, Canada, provinces, territories, ten jurisdictions and eight jurisdictions, five years of data.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Adult criminal courts, type of guilty finding by offence, age and sex of accused and length of custody, Canada, provinces, territories, ten jurisdictions and eight jurisdictions, five years of data.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. In this study, researchers used capture-recapture sampling and multiple data sources to gauge the impact of drug trafficking in Quebec, Canada on the United States drug market. The main analyses were based on arrest data that were obtained for Quebec. In addition, analysis of the chemical composition and price assessments of the Quebec synthetic drugs was done. The study includes one SPSS data file (Quebec Arrest Data (Synthetic Drugs Cases, September 2014; n=20261)-ICPSR.sav ; n=20,261 ; 13 variables) and one Excel data file (Chemical composition of seized synthetic drugs.xls ; n=365 ; 14 variables). Spatial analyses of border seizure data was performed by the researchers, but these data are not available at this time. The data used for these analyses concerned synthetic drug seizures at Canadian borders from 2007 to 2012. The dataset was provided by the Canadian Border Services Agency (CBSA). For each seizure, the specific border crossing where the seizure was made was provided, as well as the value of the seizure (except for precursors), the country of origin and the type of drug seized. The types of drugs were classified into five types: (1) Precursors, (2) MDMA, (3) Amphetamine, (4) Methamphetamine and (5) Others. Most of the seizures (86.6 percent) were classified in this last category. The country of origin of the seizure was also provided.

Note that topographic maps at a scale of 1/20,000 are no longer updated. For the latest update date, see the metadata. The reference cartographic data is now constituted according to a continuous information layer approach: * AQRéseau+ * Geobase of the Quebec Hydrographic Network (GRHQ) * Administrative divisions at the scale of 1/20,000 (SDA)] (https://www.donneesquebec.ca/recherche/fr/dataset/decoupages-administratifs) * Geobase of the Quebec Hydrographic Network (GRHQ) * Administrative divisions at the scale of 1/20,000 (SDA) * Administrative divisions at the scale of 1/20,000 (SDA) * Geobase of the Quebec Hydrographic Network (GRHQ) * Administrative divisions at the scale of 1/20,000 (SDA) Topographic maps at a scale of 1/20,000 constitute the official cartographic base of the Government of Quebec. They cover almost all of the territory south of the 52nd parallel. The data is extracted from aerial photographs at a scale of 1:40,000 taken at an altitude of 6,300 meters. They offer an accuracy of approximately four meters in planimetry. In hypsometry, it is about two meters for dimensional points and about five meters for level curves. Each file covers an area of approximately 250 km2. The main components are: * Hydrography (lakes, rivers, streams, streams, swamps, etc.). * Vegetation (forests, peatlands, nurseries, orchards, etc.). * Human constructions: * transport infrastructures (roads, bridges, airports, etc.); * buildings (roads, bridges, airports, etc.); * buildings (mobile homes, silos, greenhouses, etc.); * equipment (docks, electric power transmission lines, surface reservoirs, etc.); * equipment (docks, electrical power lines, surface reservoirs, etc.).); * designated areas (golf courses, loan banks, etc.). * The relief (the contour lines are generally ten meters equidistant and, in in some cases, they may vary between eight and twenty meters).*This third party metadata element was translated using an automated translation tool (Amazon Translate).

The CASES program was carried out from September 2002 to August 2004. The objective of the CASES field expeditions was to perform an extensive sampling of the Southern Beaufort Sea and the Amundsen Gulf coastal shelves (from 67N to 76N and from 120W to 41W). Two different expeditions were held. The first expedition was conducted on board the CCGS Pierre Radisson between September 20th and October 14th, 2002 and was identified as leg 0. The second expedition was conducted on board the CCGS Amundsen between September 8th, 2003, and August 26th, 2004. This last expedition was divided into nine periods of six weeks (four weeks for leg 9) designated legs 1 to 9.The scientific program is focussing on a central hypothesis which states that the atmospheric, oceanic and hydrologic forcing of sea ice variability dictates the nature and magnitude of biogeochemical carbon fluxes on and at the edge of the Mackenzie Shelf. The Canadian-led projects studied: 1) Atmospheric and sea ice forcing of coastal circulation; 2) Ice-atmosphere interactions and biological linkages; 3) Light, nutrients, primary and export production in ice-free waters; 4) Microbial communities and heterotrophy; 5) Pelagic food web: structure, function and contaminants; 6) Organic and inorganic fluxes; 7) Benthic processes and carbon cycling; 8) Millennial-decadal variability in sea ice and carbon fluxes; 9) Coupled bio-physical models of the carbon flows on the Canadian Arctic Shelf (Simard et al., 2010).Reference: Simard, A., Rail, M.E., Gratton, Y. 2010. Distribution of temperature and salinity in the Beaufort Sea during the Canadian Arctic Shelf Exchange Study sampling expeditions 2002-2004. Report No R1187, INRS-ETE, Quebec (QC), 128p.

The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The Montréal cases were transcribed from the Register of Patients of the University Lying-in Hospital, a large leather-bound ledger now kept in the McGill University Archives, Montréal, Quebec. Because the number of patients was small, all case records were coded. The series runs from 1843-1900. Unfortunately, the information for the period 1843 to 1850 is too limited to support systematic analysis. In 1901 the hospital adopted a new form of taking case records although the data gathered remained consistent with previous practice. Unfortunately, this information was not collected as thoroughly as had been the practice before the turn of the century. The series ends abruptly and inexplicably in 1905. The initial data base included 8216 cases.

The vector grid system provides a spatial and statistical infrastructure that allows the integration of environmental and socio-economic data. Its exploitation allows the crossing of different spatial data within the same grid units. Project results obtained using this grid system can be more easily linked. This grid system forms the geographic and statistical infrastructure of the Southern Quebec Land Accounts of the Institute of Statistics of Quebec (ISQ). It forms the geospatial and statistical context for the development of ecosystem accounting in Quebec. **In order to improve the vector grid system and the Land Accounts of Southern Quebec and to better anticipate the future needs of users, we would like to be informed of their use (field of application, objectives of use, territory, association with other products, etc.). You can write to us at maxime.keith@stat.gouv.qc.ca **. This grid system allows the spatial integration of various data relating, for example, to human populations, the economy or the characteristics of land. The ISQ wishes to encourage the use of this system in projects that require the integration of several data sources, the analysis of this data at different spatial scales and the monitoring of this data over time. The fixed geographic references of the grids simplify the compilation of statistics according to different territorial divisions and facilitate the monitoring of changes over time. In particular, the grid system promotes the consistency of data at the provincial level. The spatial intersection of the grid and the spatial data layer to be integrated makes it possible to transfer the information underlying the layer within each cell of the grid. In the case of the Southern Quebec Land Accounts, the spatial intersection of the grid and each of the three land cover layers (1990s, 2000s and 2010s) made it possible to report the dominant coverage within each grid cell. The set of matrix files of Southern Quebec Land Accounts is the result of this intersection. **Characteristics: ** The product includes two vector grids: one formed of cells of 1 km² (or 1,000 m on a side), which covers all of Quebec, and another of 2,500 m² cells (or 50 m on a side, or a quarter of a hectare), which fits perfectly into the first and covers Quebec territory located south of the 52nd parallel. Note that the nomenclature of this system, designed according to a Cartesian plan, was developed so that it was possible to integrate cells with finer resolutions (up to 5 meters on a side). In its 2024 update, the 50 m grid system is divided into 331 parts with a side of 50 km in order to limit the number of cells per part of the grid to millions and thus facilitate geospatial processing. This grid includes a total of approximately 350 million cells or 875,000 km2. It is backwards compatible with the 50m grid broadcast by the ISQ in 2018 (spatial structure and unique identifiers are identical, only the fragmentation is different). **Attribute information for 50 m cells: ** * ID_m50: unique code of the cell; * CO_MUN_2022: geographic code of the municipality of January 2022; * CERQ_NV2: code of the natural region of the ecological reference framework of Quebec; * CL_COUV_T50: unique code of the cell; * CL_COUV_T00, CL_COUV_T01: codes for coverage classes Terrestrial maps from the years 1990, 2000 and 2010. Note: the 2000s are covered by two land cover maps: CL_COUV_T01A and CL_COUV_T01b. The first inventories land cover prior to reassessment using the 2010s map, while the second shows land cover after this reassessment process. **Complementary entity classes: ** * Index_grille50m: index of the parts of the grid; * Decoupage_mun_01_2022: division of municipalities; * Decoupage_MRC_01_2022: division of geographical MRCs; * Decoupage_RA_01_2022: division of administrative regions. Source: System on administrative divisions [SDA] of the Ministry of Natural Resources and Forests [MRNF], January 2022, allows statistical compilations to be carried out according to administrative divisions hierarchically superior to municipalities. * Decoupage_CERQ_NV2_2018: division of level 2 of the CERQ, natural regions. Source: Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks [MELCCFP]. Geospatial processes delivered with the grid (only with the FGDB data set) : * ArcGIS ModelBuilder allowing the spatial intersection and the selection of the dominant value of the geographic layer to populate the grid; * ModelBuilder allowing the statistical compilation of results according to various divisions. Additional information on the grid in the report Southern Quebec Land Accounts published in October 2018 (p. 46). View the results of the Southern Quebec Land Accounts on the interactive map of the Institut de la Statistique du Québec.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Operational datasets on acquisition of Canadian citizenship, permanent and temporary residency status and visitor visas of foreign nationals.

General civil court cases by level of court, case unit and type of action, Canada and selected provinces and territories, five years of data.

The deprivation index was designed in the late 1990s in order to measure the deprivation of Quebecers on a small geographic scale. It is used for the purposes of researching and monitoring trends on social inequalities in health, developing policies and programs, allocating resources, and evaluating services. It is composed of a material dimension and a social dimension that can be analyzed separately or in combination. The index includes six indicators, all from the 2016 census and calculated on the basis of dissemination areas (DAs). The file includes the national (province of Quebec), regional (health regions (RSS)), and local (territorial service networks (RTS), local service networks (RLS), and local community service centers (CLSCs)) versions of the deprivation index. In cases where a broadcast area (AD) straddles two territories (in the RTS, RLS and CLSC versions of the file), it is the AD with the largest proportion of the population that determines which RTS, RLS or CLSC is selected in order to have a single deprivation index value per AD for mapping. All the results by AD are available in the equivalence table on the *Web site of the National Institute of Public Health of Quebec (INSPQ) *. For more information on the deprivation index, you can consult The Material and Social Deprivation Index: in brief.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The center point of the inhabited place is located, as the case may be, at the center of the economic and commercial activity of the inhabited place, in the oldest neighborhood, at a road intersection, near an important physical landmark such as a church or a town hall, or at the center of gravity of a concentration of homes. Place names are mostly names of inhabited places. Those written in straight characters correspond to the official names of municipalities and those in italics represent common names (former municipality, village, district, sector, hamlet, locality). In some cases, the common name is shown in brackets along with the official name. Names of uninhabited places are included as geographical reference points.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Dataset of species/gear type commercial fisheries from 2012 to 2021 in the Eastern Canada Regions. Only fish harvested from the NL, Maritimes, Gulf, Quebec and Eastern Arctic regions are included (Species Sought). The data was obtained from Statistical Services, Fisheries and Oceans Canada (DFO) and consists of commercial species/gear type landings data from 2012 to 2021 taken from Northwest Atlantic Fisheries Organization (NAFO) Subareas 0, 2, 3, 4 and 5 and fished in the NL, Maritimes, Gulf, Quebec and Eastern Arctic regions. The layer was created by overlaying a 2 minute hexagonal grid (approx. 10km2 cell) on species/gear type commercial fisheries point data and summing the total landings by weight reported for each cell over the ten year period. Therefore, the value of each grid cell is equal to the total species/gear type landings in kg from 2012 to 2021 for the area, and may represent many fishing events from several vessels over the ten year period. All landings are from Canadian vessels greater than 35-ft, and does not include information pertaining to international fishing vessels (i.e., St. Pierre). Individuals should exercise caution when interpreting this data. Data has not been altered and is mapped from the original logbook entry for each record prior to amalgamation. Data may contain errors such as inaccurate or nonviable coordinates, landed weights and/or species identification. For example, cases of fishing events reported in a NAFO Division with corresponding coordinates falling outside that particular NAFO Division or fishing events which appear to be located on a land mass due to rounding errors in the original entries. Such cases were excluded from the dataset. Only one location is given for each fishing event; therefore, a fishing activity that would normally cover a large area (i.e., trawling) is only shown in a single location. Some species may not include all records or locations where activity is taking place due to regional differences in permissions for mapping, or because the fishery is only partially georeferenced (e.g. Lobster). The locations/areas shown should only be used as an estimation of fishing intensity and a general guide of where particular species/gear type fishing occurs. This dataset has been privacy screened to comply with the Government of Canada's privacy policy. Privacy assessments were conducted to identify NAFO unit areas containing data with less than five vessel IDs, license IDs and fisher IDs. If this threshold was not met, catch weight locations have been withheld from these statistical areas to protect the identity or activity of individual vessels or companies. In some instances, permissions were obtained to map species or gears with a limited number of vessels, licenses, or fisher ids. The withheld areas are indicated by the unit area that has been removed and given a weight of -9999.

This data publication contains a set of 30m resolution raster files representing 2020 Canadian wall-to-wall maps of broad land cover type, forest canopy height, degree of crown closure and aboveground tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours and random forest imputation method. A full description of all methods and validation analyses can be found in Guindon et al. (2024). As the Arctic ecozones are outside NFI’s covered areas, the vegetation attributes in these regions were predicted using a single random forest model. The vegetation attributes in these arctic areas could not be rigorously validated. The raster file « SCANFI_aux_arcticExtrapolationArea.tif » identifies these zones. SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a plethora of studies in a number of areas, such as forest economics, fire science and ecology. # Limitations 1- The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St-Lawrence River. These forests are misrepresented in our training data, there is therefore an imprecision in our estimates. 2- Attributes of open stand classes, namely shrub, herbs, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates. 3- As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies. Also, the broadleaf proportion are slightly underestimated in this product version. 4- Our validation indicates that the areas in Yukon exhibit a notably lower R2 value. Consequently, estimates within these regions are less dependable. 5- Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map. Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions. Updates of this dataset will eventually be available on this metadata page. # Details on the product development and validation can be found in the following publication: Guindon, L., Manka, F., Correia, D.L.P., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J., and Boulanger, Y. 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Can. J. For. Res. https://doi.org/10.1139/cjfr-2023-0118 # Please cite this dataset as: Guindon L., Villemaire P., Correia D.L.P., Manka F., Lacarte S., Smiley B. 2023. SCANFI: Spatialized CAnadian National Forest Inventory data product. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc # The following raster layers are available: • NFI land cover class values: Land cover classes include Water, Rock, Bryoid, Herbs, Shrub, Treed broadleaf, Treed mixed and Treed conifer • Aboveground tree biomass (tonnes/ha): biomass was derived from total merchantable volume estimates produced by provincial agencies • Height (meters): vegetation height • Crown closure (%): percentage of pixel covered by the tree canopy • Tree species cover (%): estimated as the proportion of the canopy covered by each tree species: o Balsam fir tree cover in percentage (Abies balsamea) o Black spruce tree cover in percentage (Picea mariana) o Douglas fir tree cover in percentage (Pseudotsuga menziesii) o Jack pine tree cover in percentage (Pinus banksiana) o Lodgepole pine tree cover in percentage (Pinus contorta) o Ponderosa pine tree cover in percentage (Pinus ponderosa) o Tamarack tree cover in percentage (Larix laricina) o White and red pine tree cover in percentage (Pinus strobus and Pinus resinosa) o Broadleaf tree cover in percentage (PrcB) o Other coniferous tree cover in percentage (PrcC)