20 datasets found
  1. Permanent Residents – Monthly IRCC Updates

    • open.canada.ca
    • data.wu.ac.at
    csv, xlsx
    Updated Nov 18, 2025
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    Immigration, Refugees and Citizenship Canada (2025). Permanent Residents – Monthly IRCC Updates [Dataset]. https://open.canada.ca/data/en/dataset/f7e5498e-0ad8-4417-85c9-9b8aff9b9eda
    Explore at:
    xlsx, csvAvailable download formats
    Dataset updated
    Nov 18, 2025
    Dataset provided by
    Immigration, Refugees And Citizenship Canadahttp://www.cic.gc.ca/
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Time period covered
    Jan 1, 2015 - Sep 30, 2025
    Description

    People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

  2. Live Birth Profiles by County

    • data.chhs.ca.gov
    • data.ca.gov
    • +4more
    csv, zip
    Updated Nov 12, 2025
    + more versions
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    California Department of Public Health (2025). Live Birth Profiles by County [Dataset]. https://data.chhs.ca.gov/dataset/live-birth-profiles-by-county
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    csv(1911), csv(8256822), csv(9986780), zip, csv(562713)Available download formats
    Dataset updated
    Nov 12, 2025
    Dataset authored and provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    Description

    This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

    The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

  3. Pronghorn Migration Corridors - Likely Tables - 2014-2020 [ds2934]

    • data.ca.gov
    • data.cnra.ca.gov
    • +3more
    Updated Jul 24, 2025
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    California Department of Fish and Wildlife (2025). Pronghorn Migration Corridors - Likely Tables - 2014-2020 [ds2934] [Dataset]. https://data.ca.gov/dataset/pronghorn-migration-corridors-likely-tables-2014-2020-ds29341
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    zip, html, kml, geojson, csv, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Jul 24, 2025
    Dataset authored and provided by
    California Department of Fish and Wildlifehttps://wildlife.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The project lead for the collection of this data was Richard Shinn. Pronghorn (30 adult females and 1 adult male) were captured and equipped with GPS collars (Sirtrack, Havelock North, NZ) transmitting data from 2014-2020. The Likely Tables herd contains migrants, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, much of the herd displays a somewhat nomadic migratory tendency, slowly migrating north for the summer using various high use areas as they move. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. A high use area being used during winter by many of the collared animals is west of the Warner Mountains, east of Highway 395, and north of the Modoc County line. Additionally, a few individuals persist east of Highway 395, seemingly separated from the rest of the herd. Summer ranges are spread out, with some individuals moving into the Modoc National Forest and as far north as Goose Lake. A few outliers in the herd moved long distances south or east. GPS locations were fixed between 1-4 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst.

    The methodology used for this migration analysis allowed for the mapping of the herd’s home range and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 17 migrating pronghorn, including 29 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for pronghorn was 15.42 days and 38.02 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to varying fix rates in the data, separate models using Brownian bridge movement models (BMMM), with an adaptable variance rate, and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (72% of sequences selected with BBMM). In general, fixed motion variances were used when BBMM variances exceeded 8000. Home range analyses were based on data from 20 pronghorn and 25 year-round sequences using a combination of BBMMs and fixed motion variances of 1000 (84% of sequences selected with BBMM). Home range designations for this herd may expand with a larger sample, filling in some of the gaps between home range polygons in the map. Large water bodies were clipped from the final outputs.

    Corridors are visualized based on pronghorn use per cell, with greater than or equal to 1 pronghorn and greater than or equal to 3 pronghorn (20% of the sample) representing migration corridors and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Home range is visualized as the 50th percentile contour of the home range utilization distribution.

  4. Pronghorn Migration Corridors - Clear Lake - 2015-2020 [ds2932]

    • caprod.ogopendata.com
    • data.ca.gov
    • +4more
    Updated Jul 24, 2025
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    California Department of Fish and Wildlife (2025). Pronghorn Migration Corridors - Clear Lake - 2015-2020 [ds2932] [Dataset]. https://caprod.ogopendata.com/dataset/pronghorn-migration-corridors-clear-lake-2015-2020-ds29321
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    geojson, zip, kml, csv, html, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Jul 24, 2025
    Dataset authored and provided by
    California Department of Fish and Wildlifehttps://wildlife.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The project lead for the collection of this data was and Richard Shinn. Pronghorn (28 adult females) were captured and equipped with GPS collars (Sirtrack, Havelock North, NZ) transmitting data from 2015-2020. The Clear Lake herd contains migrants, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, much of the herd displays a somewhat nomadic migratory tendency, slowly migrating north, east, or south for the summer using various high use areas as they move. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. The areas adjacent to both east and west of Clear Lake Reservoir are highly used during winter by many of the collared animals. Additionally, a few individuals persist west of Highway 139 year-round, seemingly separated from the rest of the herd due to this highway barrier. However, other pronghorn cross this road near Cornell and join this subgroup. Summer ranges are spread out, with many individuals moving southeast through Modoc National Forest or as far north as Fremont National Forest in Oregon. A few outliers in the herd moved long distances south, crossing Rt 139 to Oak Ridge, or east into Likely Tables pronghorn herd areas. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst.

    The methodology used for this migration analysis allowed for the mapping of the herd’s home range and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 23 migrating pronghorn, including 72 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for pronghorn was 12.11 days and 34.18 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to varying fix rates in the data, separate models using Brownian bridge movement models (BMMM), with an adaptable variance rate, and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (68% of sequences selected with BBMM). In general, fixed motion variances were used when BBMM variances exceeded 8000. Home range analyses were based on data from 24 pronghorn and 47 year-round sequences using a fixed motion variance of 1000. Home range designations for this herd may expand with a larger sample, filling in some of the gaps between home range polygons in the map. Large water bodies were clipped from the final outputs.

    Corridors are visualized based on pronghorn use per cell, with greater than or equal to 1 pronghorn, greater than or equal to 3 pronghorn (10% of the sample), and greater than or equal to 5 pronghorn (20% of the sample) representing migration corridors, medium use corridors, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Home range is visualized as the 50th percentile contour of the home range utilization distribution.

  5. i16 Census Tract EconomicallyDistressedAreas 2023

    • data.ca.gov
    • data.cnra.ca.gov
    • +5more
    Updated Aug 15, 2025
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    California Department of Water Resources (2025). i16 Census Tract EconomicallyDistressedAreas 2023 [Dataset]. https://data.ca.gov/dataset/i16-census-tract-economicallydistressedareas-2023
    Explore at:
    zip, kml, arcgis geoservices rest api, html, csv, geojsonAvailable download formats
    Dataset updated
    Aug 15, 2025
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The IRWM web based EDA mapping tool uses this GIS layer. Created by joining ACS 2019-2023 5 year estimates to the 2020 Census Tract feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2020 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2020 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

  6. California Traffic Collision Data from SWITRS

    • kaggle.com
    zip
    Updated Jun 19, 2021
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    Alex Gude (2021). California Traffic Collision Data from SWITRS [Dataset]. https://www.kaggle.com/datasets/alexgude/california-traffic-collision-data-from-switrs/versions/2/discussion
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    zip(2694352068 bytes)Available download formats
    Dataset updated
    Jun 19, 2021
    Authors
    Alex Gude
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    California
    Description

    Context

    My father started riding a motorcycle when I was in high school and he only stopped recently. A few years ago I wondered how risky it was and started looking for some data to answer that question. What I found was a treasure trove of data: every traffic collision from 2001 to present in the state of California.

    Content

    This data comes from the California Highway Patrol and covers collisions from January 1st, 2001 until mid-October, 2020. I have requested full database dumps from the CHP four times, once in 2016, 2017, 2018, and 2020. I have combined these datasets into the one presented here. For additional details, see my post: Introducing the SWITRS SQLite Hosted Dataset

    There are three main tables:

    • collisions: Contains information about the collision, where it happened, what vehicles were involved.
    • parties: Contains information about the groups people involved in the collision including age, sex, and sobriety.
    • victims: Contains information about the injuries of specific people involved in the collision.

    There is also a table called case_ids which I used to build the other tables. It tells you which of the four original datasets each row came from.

    There is a data dictionary here: https://tims.berkeley.edu/help/SWITRS.php I have in some cases remapped values so that they are human readable (making a left turn instead of D for example); you can find those mappings here: https://github.com/agude/SWITRS-to-SQLite/blob/master/switrs_to_sqlite/value_maps.py

    Acknowledgements

    This data would not exist without the California Highway Patrol compiling it, thanks!

    Inspiration

    There is SO much to explore and discover in this data!

    Here are some of the things I have looked into:

    And here are questions I'd like to answer:

    • Task: When do different makes and models of motorcycles crash?
    • On what days are pedestrians involved in collisions?
    • Are DUIs more likely on certain days?
    • How has COVID19 changed collisions? Are different types of vehicles involved? At different times?
    • What type/color of car/motorcycle is involved in the most crashes?
    • Can we predict injuries based on the other variables in a collision?
    • Are their more accidents around sunrise/sunset?

    Bugs

    Found a problem with the data? Please post a bug report (or a PR to fix the problem) on the ETL script's Github page: SWITRS-to-SQLite. Thanks!

  7. i16 Census Tract DisadvantagedCommunities 2023

    • data.ca.gov
    • data.cnra.ca.gov
    • +5more
    Updated Aug 18, 2025
    + more versions
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    California Department of Water Resources (2025). i16 Census Tract DisadvantagedCommunities 2023 [Dataset]. https://data.ca.gov/dataset/i16-census-tract-disadvantagedcommunities-2023
    Explore at:
    html, zip, kml, geojson, csv, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Aug 18, 2025
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The IRWM web based DAC mapping tool uses this GIS layer. Created by joining ACS 2019-2023 5 year estimates to the 2020 Census Tracts feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2020 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2020 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

  8. Temporary Residents: Study Permit Holders – Monthly IRCC Updates

    • open.canada.ca
    • data.wu.ac.at
    csv, xls, xlsx
    Updated Nov 18, 2025
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    Immigration, Refugees and Citizenship Canada (2025). Temporary Residents: Study Permit Holders – Monthly IRCC Updates [Dataset]. https://open.canada.ca/data/en/dataset/90115b00-f9b8-49e8-afa3-b4cff8facaee
    Explore at:
    xls, xlsx, csvAvailable download formats
    Dataset updated
    Nov 18, 2025
    Dataset provided by
    Immigration, Refugees And Citizenship Canadahttp://www.cic.gc.ca/
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Time period covered
    Jan 1, 2015 - Sep 30, 2025
    Description

    Temporary residents who are in Canada on a study permit in the observed calendar year. Datasets include study permit holders by year in which permit(s) became effective or with a valid permit in a calendar year or on December 31st. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

  9. i16 Census Place DisadvantagedCommunities 2023

    • data.cnra.ca.gov
    • data.ca.gov
    • +5more
    Updated Aug 18, 2025
    + more versions
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    California Department of Water Resources (2025). i16 Census Place DisadvantagedCommunities 2023 [Dataset]. https://data.cnra.ca.gov/dataset/i16-census-place-disadvantagedcommunities-2023
    Explore at:
    html, zip, geojson, csv, arcgis geoservices rest api, kmlAvailable download formats
    Dataset updated
    Aug 18, 2025
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The IRWM web based DAC mapping tool uses this GIS layer. Created by joining ACS 2019-2023 5 year estimates to the 2020 Census Place feature class. A Census Place is a location that is incorporated (city or town), unincorporated areas are CDP (Census Designated Place). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of all incorporated places are as of April 1, 2020 as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

  10. Elk Migration Lines - West Goose Lake - 1999-2002, 2018-2020 [ds2900]

    • data.ca.gov
    • data.cnra.ca.gov
    • +3more
    Updated Jul 18, 2025
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    California Department of Fish and Wildlife (2025). Elk Migration Lines - West Goose Lake - 1999-2002, 2018-2020 [ds2900] [Dataset]. https://data.ca.gov/dataset/elk-migration-lines-west-goose-lake-1999-2002-2018-2020-ds2900
    Explore at:
    arcgis geoservices rest api, geojson, zip, html, csv, kmlAvailable download formats
    Dataset updated
    Jul 18, 2025
    Dataset authored and provided by
    California Department of Fish and Wildlifehttps://wildlife.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The project leads for the collection of this data were Erin Zulliger and Richard Shinn. The winter range of the West Goose Lake Rocky Mountain elk (Cervus canadensis nelsoni) sub-herd is located north of Alturas and west of Highway 395 within the Devil’s Garden Ranger District of the Modoc National Forest. This area is characterized by juniper (Juniperus occidentalis) woodlands, and sagebrush flats with some stands of lodgepole (Pinus contorta) and ponderosa pine (Pinus ponderosa) throughout flat, rocky terrain. From this area, a portion of the herd migrates approximately 50 miles north into Oregon’s Fremont National Forest, habitat that primarily consists of lodgepole and ponderosa pine forests. Minimal barriers exist along this migration route since the corridor primarily occurs on land managed by the US Forest Service. Additionally, although the core migration route does cross Highway 140, little to no impacts are known to exist from this crossing. Elk (12 adult females, 1 adult male, and 3 juvenile males) were captured from 2018 to February 2020 and equipped with Lotek and Vectronic satellite GPS collars. Additional GPS data was collected from elk (2 females and 1 male) in 1999-2002 and included in the analysis to supplement the small sample size of the 2018-2020 dataset. GPS locations were fixed at 4-hour intervals in the 2018-2020 dataset and 6 to 8-hour intervals in the 1999-2002 dataset.

    Migration lines as symbolized connect GPS data points per elk per seasonal migration. GPS points were extracted only during migrations using net-squared displacement graphs. Five migration sequences from 3 elk, with an average migration time of 6.8 days and an average migration distance of 16.14 km, were used from the 1999-2002 dataset. All three of these elk were used to supplement the eastern members of this herd, which travel shorter distances between summer and winter range than western individuals in the sample. Twenty migration sequences from 9 elk, with an average migration time of 11.2 days and an average migration distance of 57.75 km, were used from the 2018-2020 dataset.

  11. Leading causes of death, total population, by age group

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +1more
    Updated Feb 19, 2025
    + more versions
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    Government of Canada, Statistics Canada (2025). Leading causes of death, total population, by age group [Dataset]. http://doi.org/10.25318/1310039401-eng
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    Dataset updated
    Feb 19, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    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.

  12. Estimates of population as of July 1st, by marital status or legal marital...

    • www150.statcan.gc.ca
    • datasets.ai
    • +2more
    Updated Nov 9, 2022
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    Government of Canada, Statistics Canada (2022). Estimates of population as of July 1st, by marital status or legal marital status, age and sex [Dataset]. http://doi.org/10.25318/1710006001-eng
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    Dataset updated
    Nov 9, 2022
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Annual population estimates by marital status or legal marital status, age and sex, Canada, provinces and territories.

  13. Immigrants to Canada, by country of last permanent residence

    • www150.statcan.gc.ca
    • open.canada.ca
    • +1more
    Updated Sep 26, 2013
    + more versions
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    Government of Canada, Statistics Canada (2013). Immigrants to Canada, by country of last permanent residence [Dataset]. http://doi.org/10.25318/1710001001-eng
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    Dataset updated
    Sep 26, 2013
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Government of Canadahttp://www.gg.ca/
    Area covered
    Canada
    Description

    This table contains 25 series, with data for years 1955 - 2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Last permanent residence (25 items: Total immigrants; France; Great Britain; Total Europe ...).

  14. Estimates of the number of non-permanent residents by type, quarterly

    • www150.statcan.gc.ca
    • open.canada.ca
    Updated Sep 24, 2025
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    Government of Canada, Statistics Canada (2025). Estimates of the number of non-permanent residents by type, quarterly [Dataset]. http://doi.org/10.25318/1710012101-eng
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    Dataset updated
    Sep 24, 2025
    Dataset provided by
    Government of Canadahttp://www.gg.ca/
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    This table provides quarterly estimates of the number of non-permanent residents by type for Canada, provinces and territories.

  15. i16 Census Place EconomicallyDistressedAreas 2023

    • data.ca.gov
    • data.cnra.ca.gov
    • +5more
    Updated Aug 18, 2025
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    California Department of Water Resources (2025). i16 Census Place EconomicallyDistressedAreas 2023 [Dataset]. https://data.ca.gov/dataset/i16-census-place-economicallydistressedareas-2023
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    csv, arcgis geoservices rest api, geojson, zip, kml, htmlAvailable download formats
    Dataset updated
    Aug 18, 2025
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The IRWM web based EDA mapping tool uses this GIS layer. Created by joining ACS 2019-2023 5 year estimates to the 2020 Census Counties feature class, and the 2023 Unemployment Rate. A Census Place is a location that is incorporated (city or town), unincorporated areas are CDP (Census Designated Place). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of all incorporated places are as of April 1, 2020 as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

  16. Estimates of the components of international migration, quarterly

    • www150.statcan.gc.ca
    • open.canada.ca
    • +1more
    Updated Sep 24, 2025
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    Government of Canada, Statistics Canada (2025). Estimates of the components of international migration, quarterly [Dataset]. http://doi.org/10.25318/1710004001-eng
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    Dataset updated
    Sep 24, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Components of international migratory increase, quarterly: immigrants, emigrants, returning emigrants, net temporary emigrants, net non-permanent residents.

  17. Experimental estimates for business openings and closures for Canada,...

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    Updated Nov 25, 2025
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    Government of Canada, Statistics Canada (2025). Experimental estimates for business openings and closures for Canada, provinces and territories, census metropolitan areas, seasonally adjusted [Dataset]. http://doi.org/10.25318/3310027001-eng
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    Dataset updated
    Nov 25, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Government of Canadahttp://www.gg.ca/
    Area covered
    Canada
    Description

    This table presents experimental counts of businesses that open, close, or continue their operations each month for various levels of geographic and industry detail across Canada going back to January 2015. The data are available as series that are adjusted for seasonality. The level of geographic detail includes national, provincial and territorial, as well as census metropolitan areas (CMA). The data are also broken down by two-digit North American Industry Classification System (NAICS) with some common aggregations, including one for the total business sector for national, provincial and territorial levels of geography.

  18. Elk Migration Corridors - West Goose Lake - 1999-2002, 2018-2020 [ds2901]

    • data.ca.gov
    • data.cnra.ca.gov
    • +5more
    Updated Jul 18, 2025
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    California Department of Fish and Wildlife (2025). Elk Migration Corridors - West Goose Lake - 1999-2002, 2018-2020 [ds2901] [Dataset]. https://data.ca.gov/dataset/elk-migration-corridors-west-goose-lake-1999-2002-2018-2020-ds2901
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    zip, kml, geojson, csv, html, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Jul 18, 2025
    Dataset authored and provided by
    California Department of Fish and Wildlifehttps://wildlife.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The project leads for the collection of this data were Erin Zulliger and Richard Shinn. The winter range of the West Goose Lake Rocky Mountain elk (Cervus canadensis nelsoni) sub-herd is located north of Alturas and west of Highway 395 within the Devil’s Garden Ranger District of the Modoc National Forest. This area is characterized by juniper (Juniperus occidentalis) woodlands, and sagebrush flats with some stands of lodgepole (Pinus contorta) and ponderosa pine (Pinus ponderosa) throughout flat, rocky terrain. From this area, a portion of the herd migrates approximately 50 miles north into Oregon’s Fremont National Forest, habitat that primarily consists of lodgepole and ponderosa pine forests. Minimal barriers exist along this migration route since the corridor primarily occurs on land managed by the US Forest Service. Additionally, although the core migration route does cross Highway 140, little to no impacts are known to exist from this crossing. Elk (12 adult females, 1 adult male, and 3 juvenile [less than 1 year of age] males) were captured from 2018 to February 2020 and equipped with Lotek and Vectronic satellite GPS collars. Additional GPS data was collected from elk (2 females and 1 male) in 1999-2002 and included in the analysis to supplement the small sample size of the 2018-2020 dataset. GPS locations were fixed at 4-hour intervals in the 2018-2020 dataset and 6 to 8-hour intervals in the 1999-2002 dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual elk is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst.

    The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 12 migrating elk, including 25 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. Five migration sequences from 3 elk, with an average migration time of 6.8 days and an average migration distance of 16.14 km, were used from the 1999-2002 dataset. All three of these elk were used to supplement the eastern members of this herd, which travel shorter distances between summer and winter range than western individuals in the sample. Twenty migration sequences from 9 elk, with an average migration time of 11.2 days and an average migration distance of 57.75 km, were used from the 2018-2020 dataset. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1400. Winter range analyses were based on data from 11 individual elk and 18 wintering sequences using a fixed motion variance of 1400. Winter range designations for this herd would likely expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.

    Corridors are visualized based on elk use per cell, with greater than or equal to 1 elk and greater than or equal to 3 elk (20% of the sample) representing migration corridors and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.

  19. Labour force characteristics by immigrant status, annual, inactive

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +2more
    Updated Jan 10, 2025
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    Government of Canada, Statistics Canada (2025). Labour force characteristics by immigrant status, annual, inactive [Dataset]. http://doi.org/10.25318/1410008301-eng
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    Dataset updated
    Jan 10, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Government of Canadahttp://www.gg.ca/
    Area covered
    Canada
    Description

    Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by immigrant status and age group, last 5 years.

  20. i16 Census BlockGroup EconomicallyDistressedAreas 2023

    • data.ca.gov
    • data.cnra.ca.gov
    • +5more
    Updated Aug 18, 2025
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    California Department of Water Resources (2025). i16 Census BlockGroup EconomicallyDistressedAreas 2023 [Dataset]. https://data.ca.gov/dataset/i16-census-blockgroup-economicallydistressedareas-2023
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    csv, kml, html, geojson, zip, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Aug 18, 2025
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This is a copy of the statewide Census Block Group GIS Tiger file. The IRWM web based EDA mapping tool uses this GIS layer. Created by joining ACS 2019-2023 5 year estimates to the 2020 Census Tract feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2020 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2020 tract 1210.02 are also within BG 3 within that census tract. Census 2020 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2020, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas.

  21. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Immigration, Refugees and Citizenship Canada (2025). Permanent Residents – Monthly IRCC Updates [Dataset]. https://open.canada.ca/data/en/dataset/f7e5498e-0ad8-4417-85c9-9b8aff9b9eda
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Permanent Residents – Monthly IRCC Updates

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92 scholarly articles cite this dataset (View in Google Scholar)
xlsx, csvAvailable download formats
Dataset updated
Nov 18, 2025
Dataset provided by
Immigration, Refugees And Citizenship Canadahttp://www.cic.gc.ca/
License

Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically

Time period covered
Jan 1, 2015 - Sep 30, 2025
Description

People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

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