9 datasets found
  1. Vegetation - San Mateo County [ds3021]

    • catalog.data.gov
    • data.cnra.ca.gov
    • +4more
    Updated Aug 23, 2025
    + more versions
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    California Department of Fish and Wildlife (2025). Vegetation - San Mateo County [ds3021] [Dataset]. https://catalog.data.gov/dataset/vegetation-san-mateo-county-ds3021-15edd
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    Dataset updated
    Aug 23, 2025
    Dataset provided by
    California Department of Fish and Wildlifehttps://wildlife.ca.gov/
    Area covered
    San Mateo County
    Description

    In 2018, the Golden Gate National Parks Conservancy (Parks Conservancy) (https://parksconservancy.org), non-profit support partner to the National Park Service (NPS) Golden Gate National Recreation Area (GGNRA), initiated a fine scale vegetation mapping project in Marin County. The GGNRA includes lands in San Francisco and San Mateo counties, and NPS expressed interest in pursuing fine scale vegetation mapping for those lands as well. The Parks Conservancy facilitated multiple meetings with potential project stakeholders and was able to build a consortium of funders to map all of San Mateo County (and NPS lands in San Francisco). The consortium included the San Francisco Public Utilities Commission (SFPUC), Midpeninsula Regional Open Space District (MROSD), Peninsula Open Space Trust (POST), San Mateo City/County Association of Governments, and various County of San Mateo departments including Parks, Agricultural Weights and Measures, Public Works/Flood Control District, Office of Sustainability, and Planning and Building. Over a 3-year period, the project, collectively referred to as the “San Mateo Fine Scale Veg Map”, has produced numerous environmental GIS products including 1-foot contours, orthophotography, and other land cover maps. A 106-class fine-scale vegetation map was completed in April 2022 that details vegetation communities and agricultural land cover types, including forests, grasslands, riparian vegetation, wetlands, and croplands. The environmental data products from the San Mateo Fine Scale Veg Map are foundational and can be used by organizations and government departments for a wide range of purposes, including planning, conservation, and to track changes over time to San Mateo County’s habitats and natural resources.Development of the San Mateo fine-scale vegetation map was managed by the Golden Gate National Parks Conservancy and staffed by personnel from Tukman Geospatial (https://tukmangeospatial.com/), Aerial Information Systems (AIS; http://www.aisgis.com/), and Kass Green and Associates. The fine-scale vegetation map effort included field surveys by a team of trained botanists including Neal Kramer, Brett Hall, Lucy Ferneyhough, Brittany Burnett, Patrick Furtado, and Rosie Frederick. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the California Native Plant Society (CNPS) Vegetation Program (https://www.cnps.org/vegetation), with support from the California Department of Fish and Wildlife Vegetation Classification and Mapping Program (VegCAMP; https://wildlife.ca.gov/Data/VegCAMP) and ecologists with NatureServe (https://www.natureserve.org/) to develop a San Mateo County-specific vegetation classification. For more information on the field sampling and vegetation classification work San Mateo County Fine Scale Vegetation Map Final Report refer to the final report (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212663) issued by CNPS and corresponding floristic descriptions (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212666 and https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212667).Existing lidar data, collected in 2017 by San Mateo County was used to support the project. The lidar point cloud, and many of its derivatives, were used extensively during the process of developing the fine-scale vegetation and habitat map. The lidar data was used in conjunction with optical data. Optical data used throughout the project included 6-inch resolution airborne 4-band imagery collected in the summer of 2018, as well as various dates of National Agriculture Imagery Program (NAIP) imagery. Key data sets used in the lifeform and the enhanced lifeform mapping process include high resolution aerial imagery from 2018, the lidar-derived Canopy Height Model (CHM), and several other lidar-derived raster and vector datasets. In addition, a number of forest structure lidar derivatives are used in the machine learning portion of the enhanced lifeform workflow.In 2020, an enhanced lifeform map was produced which serves as the foundation for the much more floristically detailed fine-scale vegetation and habitat map. The lifeform map was developed using expert systems rulesets in Trimble Ecognition®, followed by manual editing.In 2020, Tukman Geospatial staff and partners conducted countywide reconnaissance field work to support fine-scale mapping. Field-collected data were used to train automated machine learning algorithms, which produced a fully automated countywide fine-scale vegetation and habitat map. Throughout 2021, AIS manually edited the fine-scale maps, and Tukman Geospatial and AIS went to the field for validation trips to inform and improve the manual editing process. In early January of 2022, draft maps were distributed and reviewed by San Mateo County’s community of land managers and by the funders of the project. Input from these groups was used to further refine the map. The countywide fine-scale vegetation map and related data products were made public in April 2022. In total, 106 vegetation classes were mapped. During the classification development phase, minimum mapping units (MMUs) were established for the vegetation mapping project. An MMU is the smallest area to be mapped on the ground. For this project, the mapping team chose to map different features at different MMUs. The MMU is 1/4 acre for agricultural, woody riparian, and wetland herbaceous classes; 1/2 acre for woody upland, upland herbaceous, and bare land classes; 1/5 acre for developed feature types; and 400 square feet for water.Accuracy assessment plot data were collected in 2021 and 2022. Accuracy assessment results were compiled and analyzed in the April of 2022. Overall accuracy of the lifeform map is 98 percent. Overall accuracy of the fine-scale vegetation map is 83.5 percent, with an overall ‘fuzzy’ accuracy of 90.8 percent.

  2. w

    San Mateo County Aerial Imagery Service 2017

    • data.wu.ac.at
    csv, json, xml
    Updated Nov 28, 2017
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    GIS Team, County of San Mateo Information Services Department (2017). San Mateo County Aerial Imagery Service 2017 [Dataset]. https://data.wu.ac.at/schema/performance_smcgov_org/ZWNyai1ydWp0
    Explore at:
    xml, json, csvAvailable download formats
    Dataset updated
    Nov 28, 2017
    Dataset provided by
    GIS Team, County of San Mateo Information Services Department
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    San Mateo County
    Description

    Aerial imagery map service of San Mateo County. Acquired in 2017. For use as a basemap in online maps.

    Spatial Reference: 102643 (2227) Pixel Size X: 0.5 Pixel Size Y: 0.5

    More information about the service itself can be found here: http://gis.co.sanmateo.ca.us/arcgis/rest/services/COMMON/SanMateoCounty_Imagery2017/ImageServer

  3. O

    County Shaded Relief Service 2006

    • data.smcgov.org
    • data.wu.ac.at
    csv, xlsx, xml
    Updated Nov 28, 2017
    + more versions
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    GIS Team, County of San Mateo Information Services Department (2017). County Shaded Relief Service 2006 [Dataset]. https://data.smcgov.org/Environment/County-Shaded-Relief-Service-2006/juc6-mz99/about
    Explore at:
    csv, xml, xlsxAvailable download formats
    Dataset updated
    Nov 28, 2017
    Dataset authored and provided by
    GIS Team, County of San Mateo Information Services Department
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    Shaded relief map service of San Mateo County. Derived from aerial imagery acquired in 2006. For use as a basemap in online maps and to show elevation change using shading.

    Spatial Reference: 102643 (2227) Pixel Size X: 5.0 Pixel Size Y: 5.0 Band Count: 3

    More information about the service itself can be found here: http://gis.co.sanmateo.ca.us/arcgis/rest/services/COMMON/SanMateoCounty_ShadedRelief2006/ImageServer

  4. c

    BOE TRA 2022 co41

    • gis.data.ca.gov
    • hub.arcgis.com
    Updated May 19, 2022
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    California Department of Tax and Fee Administration (2022). BOE TRA 2022 co41 [Dataset]. https://gis.data.ca.gov/datasets/CDTFA::san-mateo-2022-roll-year?layer=1
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    Dataset updated
    May 19, 2022
    Dataset authored and provided by
    California Department of Tax and Fee Administration
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This shapefile contains tax rate area (TRA) boundaries in San Mateo County for the specified assessment roll year. Boundary alignment is based on the 2021 county parcel map. A tax rate area (TRA) is a geographic area within the jurisdiction of a unique combination of cities, schools, and revenue districts that utilize the regular city or county assessment roll, per Government Code 54900. Each TRA is assigned a six-digit numeric identifier, referred to as a TRA number. TRA = tax rate area number

  5. San Francisco Bay Region Spheres of Influence

    • opendata.mtc.ca.gov
    • hub.arcgis.com
    • +2more
    Updated Aug 23, 2019
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    MTC/ABAG (2019). San Francisco Bay Region Spheres of Influence [Dataset]. https://opendata.mtc.ca.gov/datasets/e9accd91e02f47bd83edea4781eeb187
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    Dataset updated
    Aug 23, 2019
    Dataset provided by
    Metropolitan Transportation Commission
    Authors
    MTC/ABAG
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    The California Association Local Agency Formation Commissions defines a sphere of influence (SOI) as "a planning boundary outside of an agency’s legal boundary (such as the city limit line) that designates the agency’s probable future boundary and service area." This feature set represents the SOIs of the incorporated jurisdictions for the San Francisco Bay Region. The Metropolitan Transportation Commission (MTC) updated the feature set in late 2019 as part of the jurisdiction review process for the BASIS data gathering project. Changes were made to the growth boundaries of the following jurisdictions based on BASIS feedback and associated work: Antioch, Brentwood, Campbell, Daly City, Dublin, Fremont, Hayward, Los Gatos, Monte Sereno, Newark, Oakland, Oakley, Pacifica, Petaluma, Pittsburg, Pleasanton, San Bruno, San Francisco (added to reflect other jurisdictions whose SOI is the same as their jurisdiction boundary), San Jose, San Leandro, Santa Clara, Saratoga, and Sunnyvale. Notes: With the exception of San Mateo and Solano Counties, counties included jurisdiction (city/town) areas as part of their SOI boundary data. San Mateo County and Solano County only provided polygons representing the SOI areas outside the jurisdiction areas. To create a consistent, regional feature set, the Metropolitan Transportation Commission (MTC) added the jurisdiction areas to the original, SOI-only features and dissolved the features by name.Because of differences in base data used by the counties and the MTC, edits were made to the San Mateo County and Solano County SOI features that should have been adjacent to their jurisdiction boundary so the dissolve function would create a minimum number of features. Original sphere of influence boundary acquisitions:Alameda County - CityLimits_SOI.shp received as e-mail attachment from Alameda County Community Development Agency on 30 August 2019 Contra Costa County - BND_LAFCO_Cities_SOI.zip downloaded from https://gis.cccounty.us/Downloads/Planning/ on 15 August 2019Marin County - 'Sphere of Influence - City' feature service data downloaded from Marin GeoHub on 15 August 2019Napa County - city_soi.zip downloaded from their GIS Data Catalog on 15 August 2019 City and County of San Francisco - does not have a sphere of influence San Mateo County - 'Sphere of Influence' feature service data downloaded from San Mateo County GIS open data on 15 August 2019 Santa Clara County - 'City Spheres of Influence' feature service data downloaded from Santa Clara County Planning Office GIS Data on 15 August 2019 Solano County - SphereOfInfluence feature service data downloaded from Solano GeoHub on 15 August 2019 Sonoma County - 'SoCo PRMD GIS Spheres Influence.zip' downloaded from County of Sonoma on 15 August 2019

  6. a

    BOE TRA 2024 co41

    • cdtfa.hub.arcgis.com
    • gis.data.ca.gov
    • +1more
    Updated Jun 1, 2024
    + more versions
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    California Department of Tax and Fee Administration (2024). BOE TRA 2024 co41 [Dataset]. https://cdtfa.hub.arcgis.com/maps/CDTFA::boe-tra-2024-co41
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    Dataset updated
    Jun 1, 2024
    Dataset authored and provided by
    California Department of Tax and Fee Administration
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This shapefile contains tax rate area (TRA) boundaries in San Mateo County for the specified assessment roll year. Boundary alignment is based on the 2021 county parcel map. A tax rate area (TRA) is a geographic area within the jurisdiction of a unique combination of cities, schools, and revenue districts that utilize the regular city or county assessment roll, per Government Code 54900. Each TRA is assigned a six-digit numeric identifier, referred to as a TRA number. TRA = tax rate area number

  7. a

    BOE TRA 2021 co41

    • hub.arcgis.com
    • gis.data.ca.gov
    • +3more
    Updated Nov 1, 2021
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    California Department of Tax and Fee Administration (2021). BOE TRA 2021 co41 [Dataset]. https://hub.arcgis.com/datasets/CDTFA::san-mateo-2021-roll-year/about?layer=0
    Explore at:
    Dataset updated
    Nov 1, 2021
    Dataset authored and provided by
    California Department of Tax and Fee Administration
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This shapefile contains tax rate area (TRA) boundaries in San Mateo County for the specified assessment roll year. Boundary alignment is based on the 2022 county parcel map. A tax rate area (TRA) is a geographic area within the jurisdiction of a unique combination of cities, schools, and revenue districts that utilize the regular city or county assessment roll, per Government Code 54900. Each TRA is assigned a six-digit numeric identifier, referred to as a TRA number. TRA = tax rate area number

  8. S

    Park

    • data.sanjoseca.gov
    • gisdata-csj.opendata.arcgis.com
    Updated Jul 3, 2025
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    Enterprise GIS (2025). Park [Dataset]. https://data.sanjoseca.gov/dataset/park
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    arcgis geoservices rest api, csv, kml, geojson, zip, htmlAvailable download formats
    Dataset updated
    Jul 3, 2025
    Dataset provided by
    City of San José
    Authors
    Enterprise GIS
    License

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

    Description

    This dataset includes open, closed, future, seasonal, and under construction parks and trails in San Jose, Santa Clara County, and parts of San Mateo County. It covers trail land and parkland owned by and/or maintained by the San Jose Department of Parks, Recreation and Neighborhood Services, other San Jose city departments, City of San Jose, City of Santa Clara, Santa Clara County, State of California, Federal government, various school districts, Caltrans, Santa Clara Water District, Valley Transit Authority, Midpen Open Space Authority, and other agencies. Privately owned parks and open space are included as well. Parkland open space types in this dataset include community centers, community gardens, civic grounds, dog parks, libraries, neighborhood parks, open space, school grounds, special districts, trails and trail lands, privately owned public open spaces (POPOS), private recreation facilities, proposed trails, regional parks, reuse centers, sports facilities, and trails. Attribute information in this dataset includes, among other information, park name, address (often street and cross street), park type, park class, park subclass, current status, date opened, Park District, acreage, developed acreage, undeveloped acreage, open space acreage, park supervisor, park manager, park owner, PBCE Planning Area, Specific Plan or Urban Village area, and notes.

    Data is published on Mondays on a weekly basis.

  9. d

    Input Data Boundary Outlines for DEMs of the North-Central California Coast...

    • search.dataone.org
    • data.usgs.gov
    • +3more
    Updated Jun 1, 2017
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    U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC), Santa Cruz, CA. (2017). Input Data Boundary Outlines for DEMs of the North-Central California Coast (DEM_source_data.shp) [Dataset]. https://search.dataone.org/view/07f8fa92-b060-40db-bc38-7b927e6829aa
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    Dataset updated
    Jun 1, 2017
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC), Santa Cruz, CA.
    Time period covered
    Jan 1, 1984 - Jan 1, 2011
    Area covered
    Description

    A GIS polygon shapefile outlining the boundaries of the native input datasets used to construct a seamless, 2-meter resolution digital elevation model (DEM) was constructed for the open-coast region of the San Francisco Bay Area (outside of the Golden Gate Bridge), extending from Half Moon Bay to Bodega Head along the North-central California coastline. The goal was to integrate the most recent high-resolution bathymetric and topographic datasets available (for example, Light Detection and Ranging (lidar) topography, multibeam and single-beam sonar bathymetry) into a seamless surface model extending offshore at least 3 nautical miles and inland beyond the +20 m elevation contour.

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California Department of Fish and Wildlife (2025). Vegetation - San Mateo County [ds3021] [Dataset]. https://catalog.data.gov/dataset/vegetation-san-mateo-county-ds3021-15edd
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Vegetation - San Mateo County [ds3021]

Explore at:
Dataset updated
Aug 23, 2025
Dataset provided by
California Department of Fish and Wildlifehttps://wildlife.ca.gov/
Area covered
San Mateo County
Description

In 2018, the Golden Gate National Parks Conservancy (Parks Conservancy) (https://parksconservancy.org), non-profit support partner to the National Park Service (NPS) Golden Gate National Recreation Area (GGNRA), initiated a fine scale vegetation mapping project in Marin County. The GGNRA includes lands in San Francisco and San Mateo counties, and NPS expressed interest in pursuing fine scale vegetation mapping for those lands as well. The Parks Conservancy facilitated multiple meetings with potential project stakeholders and was able to build a consortium of funders to map all of San Mateo County (and NPS lands in San Francisco). The consortium included the San Francisco Public Utilities Commission (SFPUC), Midpeninsula Regional Open Space District (MROSD), Peninsula Open Space Trust (POST), San Mateo City/County Association of Governments, and various County of San Mateo departments including Parks, Agricultural Weights and Measures, Public Works/Flood Control District, Office of Sustainability, and Planning and Building. Over a 3-year period, the project, collectively referred to as the “San Mateo Fine Scale Veg Map”, has produced numerous environmental GIS products including 1-foot contours, orthophotography, and other land cover maps. A 106-class fine-scale vegetation map was completed in April 2022 that details vegetation communities and agricultural land cover types, including forests, grasslands, riparian vegetation, wetlands, and croplands. The environmental data products from the San Mateo Fine Scale Veg Map are foundational and can be used by organizations and government departments for a wide range of purposes, including planning, conservation, and to track changes over time to San Mateo County’s habitats and natural resources.Development of the San Mateo fine-scale vegetation map was managed by the Golden Gate National Parks Conservancy and staffed by personnel from Tukman Geospatial (https://tukmangeospatial.com/), Aerial Information Systems (AIS; http://www.aisgis.com/), and Kass Green and Associates. The fine-scale vegetation map effort included field surveys by a team of trained botanists including Neal Kramer, Brett Hall, Lucy Ferneyhough, Brittany Burnett, Patrick Furtado, and Rosie Frederick. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the California Native Plant Society (CNPS) Vegetation Program (https://www.cnps.org/vegetation), with support from the California Department of Fish and Wildlife Vegetation Classification and Mapping Program (VegCAMP; https://wildlife.ca.gov/Data/VegCAMP) and ecologists with NatureServe (https://www.natureserve.org/) to develop a San Mateo County-specific vegetation classification. For more information on the field sampling and vegetation classification work San Mateo County Fine Scale Vegetation Map Final Report refer to the final report (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212663) issued by CNPS and corresponding floristic descriptions (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212666 and https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212667).Existing lidar data, collected in 2017 by San Mateo County was used to support the project. The lidar point cloud, and many of its derivatives, were used extensively during the process of developing the fine-scale vegetation and habitat map. The lidar data was used in conjunction with optical data. Optical data used throughout the project included 6-inch resolution airborne 4-band imagery collected in the summer of 2018, as well as various dates of National Agriculture Imagery Program (NAIP) imagery. Key data sets used in the lifeform and the enhanced lifeform mapping process include high resolution aerial imagery from 2018, the lidar-derived Canopy Height Model (CHM), and several other lidar-derived raster and vector datasets. In addition, a number of forest structure lidar derivatives are used in the machine learning portion of the enhanced lifeform workflow.In 2020, an enhanced lifeform map was produced which serves as the foundation for the much more floristically detailed fine-scale vegetation and habitat map. The lifeform map was developed using expert systems rulesets in Trimble Ecognition®, followed by manual editing.In 2020, Tukman Geospatial staff and partners conducted countywide reconnaissance field work to support fine-scale mapping. Field-collected data were used to train automated machine learning algorithms, which produced a fully automated countywide fine-scale vegetation and habitat map. Throughout 2021, AIS manually edited the fine-scale maps, and Tukman Geospatial and AIS went to the field for validation trips to inform and improve the manual editing process. In early January of 2022, draft maps were distributed and reviewed by San Mateo County’s community of land managers and by the funders of the project. Input from these groups was used to further refine the map. The countywide fine-scale vegetation map and related data products were made public in April 2022. In total, 106 vegetation classes were mapped. During the classification development phase, minimum mapping units (MMUs) were established for the vegetation mapping project. An MMU is the smallest area to be mapped on the ground. For this project, the mapping team chose to map different features at different MMUs. The MMU is 1/4 acre for agricultural, woody riparian, and wetland herbaceous classes; 1/2 acre for woody upland, upland herbaceous, and bare land classes; 1/5 acre for developed feature types; and 400 square feet for water.Accuracy assessment plot data were collected in 2021 and 2022. Accuracy assessment results were compiled and analyzed in the April of 2022. Overall accuracy of the lifeform map is 98 percent. Overall accuracy of the fine-scale vegetation map is 83.5 percent, with an overall ‘fuzzy’ accuracy of 90.8 percent.

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