Canada GIS data

A city GIS-project in tandem with Bluewater Trails to determine cycling comfortability scores throughout the city. Based on weighted criteria related to road infrastructure characteristics as well as traffic context. The city does not guarantee the complete accuracy of this data.

Linear representation of transit priority measures such as transit priority lanes, in lane stops, and separated right-of-way.Transit Priority Corridors are streets that have been strategically chosen to include measures to improve the efficiency of buses along the entire corridor. Metadata

uav imagery by Bard College, acquired 2025-12-09 to 2025-12-09, 6.6 cm per pixel, published on OpenAerialMap.

French version / Version française: Zone d’entente sur la gestion des feux | CarrefourGéo Ontario Fire management agreements divide land into 4 areas:Crown Protection Area (CPA): The Crown is responsible for responding to all fires in the CPA Municipal Protection Area (MPA): The municipality is responsible for responding in the MPA Federal Protection Area (FPA): The federal government is responsible for responding in the FPA Northern Fire Protection Area (NFPA): The local fire department (mostly in unorganized areas) is responsible for responding to all incidents and the suppression of fires in the NFPA Additional Documentation Fire Management Agreement Area - User Guide (PDF) Fire Management Agreement Area - Data Description (PDF) Fire Management Agreement Area - Documentation (Word) StatusOn going: data is being continually updated  Maintenance and Update FrequencyNot stated  Contact Aviation, Forest Fire and Emergency ServicesAFFES.GIS@ontario.ca

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of February across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows disability status by sex and age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   This layer is symbolized to show the count and percentage of elderly (65+) with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B18101Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

uav imagery by Ben Scholer, acquired 2025-11-08 to 2025-11-08, 8.8 cm per pixel, published on OpenAerialMap.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows fertility in past 12 months by age of mother. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. The calculated percentages are slightly different from traditional age-specific fertility rates in that the total number of live births (due to twins or higher-order multiple births) is not available in this table.  This layer is symbolized to show the count and percent of women age 15 to 50 who had a birth in the past 12 months.  To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B13016Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico   Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

uav imagery by WebODM, acquired 2025-07-27 to 2025-07-27, 2.5 cm per pixel, published on OpenAerialMap.

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents the month of August within a dataset of 12 raster files (one for each month) representing total precipitation values across North America for the 1981–2010 climatological reference period, in millimeters. The dataset is derived from a kilometer-scale climate product generated using the CHELSA downscaling model and represents long-term climatological means. The North American monthly precipitation layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric precipitation fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly precipitation across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

satellite imagery by webodm, acquired 2026-06-09 to 2026-06-09, 2.0 cm per pixel, published on OpenAerialMap.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows full-time, year-round vs. part-time employment by age. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   This layer is symbolized to show the percent and count of population age 65+ who worked in the past 12 months. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B23027Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

satellite imagery by drone, acquired 2026-09-16 to 2026-09-16, 3.5 cm per pixel, published on OpenAerialMap.

uav imagery by Gary Stebbins, acquired 2026-06-04 to 2026-06-04, 1.5 cm per pixel, published on OpenAerialMap.

Polygon representation of areas that define winter maintenance service areas specific to street assets.This dataset was created to support the winter maintenance service delivery for streets in the Halifax Regional Municipality.Metadata

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows children by nativity of parents by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   This layer is symbolized to show the percentage of children who are in immigrant families (children who are foreign born or live with at least one parent who is foreign born). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B05009Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).    The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

uav imagery by Malloy Aero, acquired 2026-01-25 to 2026-01-25, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Robert Howe, acquired 2026-04-10 to 2026-04-10, 2.4 cm per pixel, published on OpenAerialMap.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows computer ownership and type of internet subscription. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   This layer is symbolized to show the count and percent of households with no internet connection. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

Miscellaneous events are often the result of the cumulative impact of a combination of abiotic, insect and disease agents or events.  For example, Aspen decline where repeated infestations of Forest Tent Caterpillar, are combined with several seasons of prolonged drought. The Government of Ontario tracks forest damage events to help proactively manage the detrimental effects to our forests.   We monitor the threat and spread of invasive forest pest insect species in Ontario. The data is also important to the Forest Management Planning process in calculating timber volume loss within affected areas. Additional Documentation Forest Misc Damage Event - Documentation (Word) Forest Misc. Damage Event - Data Description (PDF) Status On going: data is being continually updated Maintenance and Update Frequency As needed: data is updated as deemed necessary Contact Calvin Julie, calvin.julie@ontario.ca Recommendations The boundaries for these events were coarsely mapped. This must be taken into account when comparing information from this data class to other NRVIS layers that have a higher spatial accuracy (e.g.:Forest Cover). Otherwise, misleading results will be derived whenever this information is subjected to analysis. This NRVIS data class accommodates history for events spanning several years which includes overlaps. Users should request and/or sort out individual events through the 'Year of Event' ,'Event Name' and 'Forest Damage Ranking' attributes in order to prevent clutter.

uav imagery by WebODM, acquired 2025-06-27 to 2025-06-27, 6.8 cm per pixel, published on OpenAerialMap.

satellite imagery by Robert Howe, acquired 2025-10-03 to 2025-10-04, 8.5 cm per pixel, published on OpenAerialMap.

uav imagery by Robert Howe, acquired 2025-10-03 to 2025-10-04, 3.3 cm per pixel, published on OpenAerialMap.

Canadians have long served their country by participating in numerous wars and conflicts. Over the years, individual citizens, veterans organizations and service clubs have raised thousands of memorials across the country to honour those sailors, soldiers, airmen and women who made great sacrifices towards the restoration of world peace and served their country so well. In keeping with our proud military heritage, Canadians erected cenotaphs in municipal centres, raised monuments in parks, cemeteries and public areas. Church members have mounted plaques and stained glass windows as memorials, and cairns and fountains have been constructed as important landmarks marking various military involvement. All of this is done to mark the service of family, friends and comrades, and to ensure that these important and not so well known battles and events form part of our communal memory. Visit Memorials - Remembrance - Veterans Affairs Canada for memorials and war cemeteries in Canada and around the world that honour Canadians who have served. Source: National Inventory of Canadian Military Memorials Update Frequency: As needed Contact Information:For inquiries regarding the data please contact Veterans Affairs Canada (memorial-memoriaux@veterans.gc.ca) For inquiries regarding the service, please leave a comment below French version can be found here

General vehicle-specific data from the prior 10 years. Data compiled in this format for the Traffic Safety Data and Analysis website (www.iowadot.gov/tsda). Metadata available here.

This point layer displays the locations of known photovoltaic (PV) and solar hot water installations. The location coordinates may not represent the exact placement of the solar equipment on each parcel, particularly for large parcels or those with solar equipment in multiple locations. This layer only includes PV and solar hot water systems that have been installed. Systems that were previously permitted and installed that have since been retired and removed are not included. This layer will be updated quarterly. Explore all our data on the Cambridge GIS Data Dictionary. Attributes NameType DetailsDescription SystemID type: String width: 15 precision: 0 Internal randomly-generated identifier of the system SystemStatus type: String width: 15 precision: 0 Status of system installation. Systems that have been installed are considered ‘Active’ systems. Only ‘Active’ systems are included in this layer. SystemType type: String width: 30 precision: 0 PV (photovoltaic) or Solar Hot Water FullStreetAddress type: String width: 100 precision: 0 Street address BuildingType type: String width: 30 precision: 0 Building type for this installation SiteCategory type: String width: 30 precision: 0 Site category for this installation KW type: Double width: 8 precision: 38 If the record is a photovoltaic (PV) system, the rated capacity of the system in kW. The rated capacity represents the power output potential of the system. SolarHotWaterSystemArea type: Double width: 8 precision: 38 If the record is a solar hot water system, the area of the collector in square feet. PermitIssueDate type: Date width: 8 precision: 0 Date the solar permit was issued for this installation. created_user type: String width: 255 precision: 0 created_date type: Date width: 8 precision: 0 last_edited_user type: String width: 255 precision: 0 last_edited_date type: Date width: 8 precision: 0

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of June across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Site Regions and Site Districts of Ontario represent an early ELC system originally developed by Angus Hills (1959 and later revisions). This dataset was revised by the ELC Working Group in 2000 to better reflect new information and new technology, while maintaining Hills's original concepts. For further information on the rationale for the revisions, refer to Ecoregions of Ontario Modifications to Angus Hills Site Regions and Districts, Revisions and Rationale (Crins and Uhlig 2000), or the Ecological Land Classification Primer (Crins 2007). Additional Documentation Site Region - Data Description (PDF) Site Region - Documentation (Word) Status Completed: production of the data has been completed Maintenance and Update Frequency Not planned: there are no plans to update the data Contact Peter Uhlig, Peter.Uhlig@ontario.ca

GFS model output is processed into APCP rasters that capture accumulated precipitation over a 7‑day forecast period. These rasters are then combined with watershed boundary data, and zonal statistics are applied to compute average precipitation for each sub‑basin. The final polygon features provide a clear depiction of global rainfall and snowfall patterns, offering critical information for disaster risk management and international water resource planning.

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents the month of November within a dataset of 12 raster files (one for each month) representing total precipitation values across North America for the 1981–2010 climatological reference period, in millimeters. The dataset is derived from a kilometer-scale climate product generated using the CHELSA downscaling model and represents long-term climatological means. The North American monthly precipitation layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric precipitation fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly precipitation across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

uav imagery by Hawkinson Aerial Photography, LLC, acquired 2025-06-02 to 2025-06-02, 2.7 cm per pixel, published on OpenAerialMap.

uav imagery by Sean Frey, acquired 2025-07-14 to 2025-07-14, 3.6 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-07-23 to 2025-07-23, 4.1 cm per pixel, published on OpenAerialMap.

uav imagery by Gary Stebbins, acquired 2025-08-14 to 2025-08-14, 1.0 cm per pixel, published on OpenAerialMap.

Overview This feature service provides a spatial inventory of cemeteries within the County of Brant. It supports location-based reference and planning for cemetery sites, burial grounds, and memorial parks. Contents Cemeteries contains point or area features that identify cemetery locations and related site records. The layer includes basic administrative attributes such as the cemetery name, ownership, and status to support searching, display, and operational reference. Licence This service is shared under CC0.

Overview This feature service provides yard waste collection zone boundaries for the County of Brant. It supports municipal service planning and public information by identifying the areas used to organise yard waste collection coverage. Contents Brant Yard Waste Collection: A polygon layer representing the yard waste collection zones used to define service areas across the County of Brant. Licence This service is shared under CC0.