The Urban Area Boundary Files portray the urban area boundaries for which census data are disseminated. They are available for download in two types: cartographic and digital. Cartographic boundary files depict the geographic areas using only the shorelines of the major land mass of Canada and its coastal islands. Digital boundary files depict the full extent of the geographic areas, including the coastal water area. The files provide a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate consisting of four condensed maps that show urban populations of the people living in Canada. The two maps at the top of this plate show the night-time distribution of population, circa 1956 for Metropolitan Toronto and part of Montreal Island. These two maps actually show the distribution of persons in their permanent homes, without adjustments for such persons that may be absent from their homes at night. Persons in short term, transient residence, such as those in hotels and hospitals are not represented. Another map shows the distribution of urban population across Canada, circa 1951. The definition of urban includes all persons residing in cities, towns and villages of 1000 population or more, whether incorporated or unincorporated, as well as the population of all parts of the census metropolitan areas. A smaller scale map of Canada shows urban areas. These are areas in which urban communities of 1000 or more population are 15 or fewer miles apart.

Contained within the 5th Edition (1978 to 1995) of the National Atlas of Canada is a map that shows the two aspects of the Canadian urban system: the interdependence (linkages) between cities, and the hinterland surrounding each city. Data for 1976 to 1981 period. An inset map shows the urban system in the Windsor to Quebec corridor.

Greenness measures based on normalized difference vegetation index (NDVI), including percentage area classed as green and average NDVI, by population centre.

This table contains 144 series, with data for years 1851 - 1971 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Age groups (16 items: All ages; 10-14 years; 5-9 years; 0-4 years ...) Sex (3 items: Both sexes; Males; Females ...) Type of area (4 items: All areas; Rural non-farm; Farm; Urban ...).

Presents data on population projections by aggregate area, both sexes at the following levels: geography: Alberta, Alberta Health Services (AHS) continuum zones, subzones, aggregate areas, and local areas; age: single year of age, 5-year age groups and combined ages; sex: male, female, and both. Historical population estimates (actuals) are included on the file for comparison/reference.

LAR.i Laboratory - Université du Québec à Chicoutimi (UQAC) 2021-08-24 Name: Image dataset of various soil types in an urban city Published journal paper: Gensytskyy, O., Nandi, P., Otis, M.JD. et al. Soil friction coefficient estimation using CNN included in an assistive system for walking in urban areas. J Ambient Intell Human Comput 14, 14291–14307 (2023). https://doi.org/10.1007/s12652-023-04667-w This dataset contains images of various types of soils and was used for the project "An assistive system for walking in urban areas". The images were taken using a smartphone camera in a vertical orientation and are high-quality. The files are named with two characters, being the first letter and last letter of its class name, following by their number. Capture location : City of Saguenay, Quebec Canada. Class count : 8 Total number of images : 493 Classes and number of images per class: Asphalt (89) Concrete (80) Epoxy_coated_interior (34) Grass (90) Gravel (58) Scrattered_snow (40) Snow (68) Wood (34)

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 2.1118 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.5001 and 0.9257 (in million kms), corressponding to 23.6828% and 43.8347% respectively of the total road length in the dataset region. 0.686 million km or 32.4825% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0078 million km of information (corressponding to 1.1405% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

The dataset titled "Canadian Protected and Conserved Areas Database (CPCAD)" falls under the domain of Housing Potential. It is available in the format of 'application/vnd.shp' and has a size of 131 MB. The dataset, owned and published by the Government of Canada, was published on 31st December 2022 and covers the time span from 1st July 1867 to 31st December 2023. The geographical area covered by the dataset is Canada. The dataset is open for access and the contact point for access is the Government of Quebec. The dataset can be accessed at the provided location and the contact email for access is 'scf-geocarto-cws-geomapping@ec.gc.ca'. The dataset is identified by the metadata identifier '6c343726-1e92-451a-876a-76e17d398a1c' and is available in English and French languages. The dataset contains data about individuals but not identifiable individuals, and it does not contain data about Indigenous communities. The dataset has a yearly temporal resolution and a national geospatial resolution. It consists of 17 rows, 7 columns, and 120 data cells. The dataset is described as the authoritative source of data on protected and conserved areas in Canada. It is licensed under the Open Government Licence - Canada and the resources available in the dataset are 'Protected Area' and 'Canadian Protected and Conserved Areas Database (CPCAD)'. The metadata was created on 19th March 2025 and was last modified on 28th March 2025.

The Geographic Attribute File is a dataset that contains census geographic information at the Enumeration Area level for all of Canada for the 1996 census. Each record includes geographic coordinates, population and dwelling counts, land area, names, unique identifiers, and geographic codes for linkages with other census boundaries.

The original dataset is available from Statistics Canada as a text file (.txt). For viewing in Scholars GeoPortal, the dataset was converted from this original format into a Shapefile format using the point coordinates available for each record. Each point is the population centre of an Enumeration Area.

The original data, and other supporting files and documentation, are available as additional downloads from Scholars GeoPortal.

The Urban Service Area dataset is comprised of all the polygons that represent Urban Service Areas in Alberta. Urban Service Area is a municipality type defined under the authority of the Municipal Government Act. The formation of an Urban Service Area can occur if there is a large unincorporated community within a Specialized Municipality that is recognized as an equivalent to a City by the Government of Alberta. It has a population, public utility services, and other characteristics.

An outline map showing the coastline, boundaries and major lakes and rivers for Canada and nearby countries. Also included are the locations of Canada's capital cities.

The 1991 Place Name Master File (PNMF) is a digital product which provides population and dwelling counts for census subdivisions (CSDs) and unincorporated places (UPs) linked to enumeration areas (EAs). In addition, the current version of the PNMF also provides over 21,000 other place names (PNs) linked to standard geographic areas through the Standard Geographical Classification (SGC). Each point represents a record in the PNMF.

The original dataset is available from Statistics Canada as a text files (.txt). For view in Scholars Portal, the dataset was converted from this original format into a Shapefile using the point coordinates available for each record. Each point is the population centre of an Enumeration Area.

The original data, and other supporting files and documentation, are available as additional downloads from Scholars GeoPortal.

The infographic is called Demographic Estimates, Census Metropolitan Areas – Canada, 2015 and is designed to inform readers about the latest demographic growth and aging trends at the Census Metropolitan Area (CMA) level.

The Geographic Attribute File is a dataset that contains census geographic information at the Dissemination Block level for all of Canada for the 2006 census. Dissemination Blocks are the smallest geographic area for which population and dwelling count data are disseminated. Each record includes geographic coordinates, population and dwelling counts, land area, names, unique identifiers, and geographic codes for linkages with other census boundaries.

The original dataset is available from Statistics Canada as a text file (.txt) or an Excel spreadsheet (.xlsx). For viewing in Scholars GeoPortal, the dataset was converted from its original tabular format into a Shapefile format using the point coordinates available for each record. These coordinates are only for Dissemination Areas, not Dissemination Blocks, so in some cases, there are several points at the same location (where a Dissemination Area is composed of several Dissemination Blocks).

The original data tables, and other supporting files and documentation, are available as additional downloads from Scholars GeoPortal.

Contained within the 2nd Edition (1915) of the Atlas of Canada is a maps that shows the city of Toronto. The map indicates the location of city wards, street names electric railways, and select buildings.

The Green/White Area dataset is comprised of all the polygons that represent the Green and White areas in the province of Alberta for the Department of Alberta Environment and Parks administrative purpose.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows as many populated places in the Great Lakes area as the scale of the map will permit. It also indicates, within certain limits, the number of inhabitants at each place according to the 1951 Census of Canada. The map also distinguishes between those places which were incorporated in cities, towns, and villages, and those which were not. Therefore, it shows the urban municipalities in those parts of Ontario and Quebec covered by the map.

This table contains 23760 series, with data for years 2011 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Nova Scotia; Prince Edward Island; Newfoundland and Labrador ...), Labour force characteristics (10 items: Population; Labour force; Full-time employment; Employment ...), Urban and rural areas (8 items: Total urban and rural areas; Urban core; Urban fringe; Census metropolitan area and census agglomeration ...), Sex (3 items: Both sexes; Females; Males ...), Age group (9 items: 15 years and over;15 to 24 years;25 years and over;25 to 44 years ...).

The dataset titled "Units Under Construction: By Dwelling Type" falls under the domain of Housing and is tagged with keywords such as Housing Market and Housing Potential. It is available in CSV format and was published on April 6, 2023. The dataset spans the time period from January 1, 2022, to December 31, 2022, and covers the geographical area of Canada. The dataset is open for access and its location is provided. The owner, author, and contact email for the dataset are all associated with the Canada Mortgage and Housing Corporation (CMHC). The dataset was accessed on July 9, 2023, and is in English. The dataset does not contain data about individuals, identifiable individuals, or Indigenous communities. It is the 2022 version of the dataset and the temporal resolution is annual. The geospatial resolution is city-wise. The dataset is machine-readable, indicating good data quality. The dataset is owned by the Canada Mortgage and Housing Corporation and provides an annual count of housing units currently under construction in Canadian urban centers with a population of at least 10,000. The dataset is organized by type of dwelling to provide industry professionals with an overview of new construction across Canada. The license for the dataset is not specified. The resources available in the dataset include an Excel file named 'units-under-construction-dwelling-type-2022-en.xlsx'. The metadata for the dataset was created on July 9, 2023, and was last modified on April 8, 2025.