20 datasets found
  1. s

    Assessor's Map Books: Santa Cruz County, California, 2015

    • searchworks.stanford.edu
    zip
    Updated Jul 28, 2018
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    (2018). Assessor's Map Books: Santa Cruz County, California, 2015 [Dataset]. https://searchworks.stanford.edu/view/qd150sw6768
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    zipAvailable download formats
    Dataset updated
    Jul 28, 2018
    Area covered
    Santa Cruz County, California
    Description

    The Geographic Information Systems (GIS) Unit falls under the purview of the County of Santa Cruz Information Services Department. The GIS Unit serves all County departments and external customers and provides data on land, features and people of Santa Cruz County. Santa Cruz County encompasses 4 cities and approximately 265,000 people. This coverage can be used for basic applications such as viewing, querying and map output production, or to provide a basemap to support graphical overlays and analyses of geospatial data.

  2. Vegetation - Santa Clara and Santa Cruz Counties [ds3116]

    • data.cnra.ca.gov
    • data.ca.gov
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    Updated Jun 27, 2025
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    California Department of Fish and Wildlife (2025). Vegetation - Santa Clara and Santa Cruz Counties [ds3116] [Dataset]. https://data.cnra.ca.gov/dataset/vegetation-santa-clara-and-santa-cruz-counties-ds3116
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    html, zip, xlsx, txt, gdb, gpkg, kml, csv, arcgis geoservices rest api, geojsonAvailable download formats
    Dataset updated
    Jun 27, 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

    Area covered
    Santa Clara
    Description

    Under contract to the Santa Cruz Mountains Stewardship Network with support from the Golden Gate National Parks Conservancy, and staffed by personnel from Tukman Geospatial, Aerial Information Systems (AIS), and Kass Green and Associates, Tukman Geospatial and Aerial Information Systems created a fine-scale vegetation map of portions of Santa Cruz and Santa Clara Counties. CDFW’s Vegetation Classification and Mapping Program (VegCAMP) provided in-kind service to allocate and score the AA.

    The mapping study area, consists of approximately 1,133,106.8 acres, of Santa Clara and Santa Cruz counties. Work was performed on the project between 2020 and 2023. The Santa Cruz and Santa Clara fine-scale vegetation map was designed for a broad audience for use at many floristic and spatial scales and is useful to managers interested in specific information about vegetation composition and forest health.

    CNPS under separate contract and in collaboration with CDFW VegCAMP developed the floristic vegetation classification used for the project. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS).

    The vegetation map was produced with countywide vegetation survey data and combined with surveys from CNPS. Trimble® Ecognition® followed by manual image interpretation that was used to map lifeforms. Fine-scale segmentation was conducted using Trimble Ecognition® and relies on summer 2020 4-band NAIP, the 2020 lidar-derived canopy height model, and a suite of spectral indices derived from the NAIP. They utilized a type of algorithmic data modeling known as machine learning to automate the classification of fine-scale segments into one of Santa Cruz and Santa Clara Counties 121 fine-scale map classes. The minimum mapping unit (MMU) is set by feature type. For agricultural classes, the MMU is 1/4 acre, for woody upland classes is 1/2 acre, woody riparian is 1/4 acre, upland herbaceous is 1/2 acre, wetland herbaceous is 1/4 acre. Bare land is 1/2 acre, impervious features is 1000 square feet, while developed is 1/5 acre and water is 400 square feet.

    Field reconnaissance and accuracy assessment enhanced map quality. There was a total of 121 mapping classes. The overall Fuzzy Accuracy Assessment rating for the final vegetation map, map at the Alliance and Group levels, is 92 percent. More information can be found in the project report, which is bundled with the vegetation map published for BIOS here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/3100_3199/ds3116.zip.

  3. c

    Santa Cruz 2024 Roll Year

    • gis.data.ca.gov
    • hub.arcgis.com
    Updated Jun 3, 2024
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    California Department of Tax and Fee Administration (2024). Santa Cruz 2024 Roll Year [Dataset]. https://gis.data.ca.gov/maps/d48034d4a2d5414993f353fcc74615c2
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    Dataset updated
    Jun 3, 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

    Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

  4. c

    Santa Cruz 2025 Roll Year

    • gis.data.ca.gov
    • hub.arcgis.com
    • +1more
    Updated Jun 9, 2025
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    California Department of Tax and Fee Administration (2025). Santa Cruz 2025 Roll Year [Dataset]. https://gis.data.ca.gov/maps/dd7a5372dead42bcb66f56c6122ed258
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    Dataset updated
    Jun 9, 2025
    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

    Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

  5. c

    Santa Cruz 2023 Roll Year

    • gis.data.ca.gov
    • hub.arcgis.com
    Updated May 22, 2023
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    California Department of Tax and Fee Administration (2023). Santa Cruz 2023 Roll Year [Dataset]. https://gis.data.ca.gov/maps/01f9a5a71d9647f89c1d6abdc2231ae1
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    Dataset updated
    May 22, 2023
    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

    Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

  6. c

    BOE TRA 2024 co44

    • gis.data.ca.gov
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +1more
    Updated Jun 3, 2024
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    California Department of Tax and Fee Administration (2024). BOE TRA 2024 co44 [Dataset]. https://gis.data.ca.gov/maps/CDTFA::boe-tra-2024-co44/about
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    Dataset updated
    Jun 3, 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 Santa Cruz 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

  7. d

    Santa Cruz Black Salamander Range - CWHR A020A [ds2845]

    • catalog.data.gov
    • data.ca.gov
    • +6more
    Updated Jul 24, 2025
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    California Department of Fish and Wildlife (2025). Santa Cruz Black Salamander Range - CWHR A020A [ds2845] [Dataset]. https://catalog.data.gov/dataset/santa-cruz-black-salamander-range-cwhr-a020a-ds2845-c8995
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    Dataset updated
    Jul 24, 2025
    Dataset provided by
    California Department of Fish and Wildlife
    Description

    Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

  8. d

    Vegetation - State Vehicle Recreation Areas - 2020 [ds3194]

    • catalog.data.gov
    • data.ca.gov
    • +5more
    Updated Jul 24, 2025
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    California Department of Fish and Wildlife (2025). Vegetation - State Vehicle Recreation Areas - 2020 [ds3194] [Dataset]. https://catalog.data.gov/dataset/vegetation-state-vehicle-recreation-areas-2020-ds3194
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    Dataset updated
    Jul 24, 2025
    Dataset provided by
    California Department of Fish and Wildlife
    Description

    The California Department of Parks and Recreation contracted Geographical Information Center (GIC) to conduct vegetation sampling across multiple California State Vehicle Recreation Areas (SVRA). The purpose of this map is to characterize the vegetation in various SVRAs, which includes Alameda Tesla, Carnegie, Claypit, Heber Dunes, Hollister Hills, Hungry Valley, Oceano Dunes, Ocotillo Wells and Prairie City. The development of this vegetation map was prompted by the passage of Senate Bill 249, in which California Department of Parks and Recreation’s Off-Highway Motor Vehicle Recreation Division (OHMVRD) was charged with meeting new legislative mandates to ensure resources compliance within all SVRAs. These mandates require (among other things) that OHMVRD compile an inventory of native plant communities within each SVRA [PRC 5090.35 (c)(1)]. To meet this requirement, OHMVRD has consulted the California Department of Fish and Wildlife’s Vegetation Classification and Mapping Program (VegCAMP) to source finescale vegetation maps that cover the SVRA footprint, or, if not available, used the VegCAMP methods to develop new finescale vegetation maps. This finescale vegetation map and associated data is intended to provide an inventory of native plant communities, inform the park’s natural resource management planning including the Wildlife Habitat Protection Plan (WHPP), and establish a baseline for measuring future vegetation change. About the individual SVRAs: Alameda Tesla: The finescale vegetation map for the Alameda Tesla area was created in 2021-2022 using CDFW's VegCAMP standard methods. At the time of surveying, this parcel was part of Carnegie SVRA and was sampled and analyzed together with that project, as part of informing the Carnegie SVRA Wildlife Habitat Protection Plan. However, after the legal separation of these two units in 2021, the mapping projects have also been separated. Carnegie: The finescale vegetation map for Carnegie SVRA was created in 2021-2022 for the park's Wildlife Habitat Protection Plan, using CDFW's VegCAMP standard methods. Field surveys were conducted in 2021. This mapping effort was part of a larger project within the Off Highway Motor Vehicle Division of State Parks to create updated vegetation maps and an inventory of native plant communities for each SVRA. When the project began in 2021, Carnegie SVRA and the adjacent Alameda-Tesla area were sampled and analyzed together. However, because of the legal the separation of these two units in 2021, the mapping projects were separated Clay Pit: Clay Pit SVRA is a small, 220-acre park in unincorporated Butte County, three miles southwest of Oroville. It consists of a narrow terrace surrounding a large bowl-shaped depression that was excavated for clay substrate to use in the construction of the Oroville Dam. It was a popular unofficial off-highway vehicle (OHV) riding area, and became an SVRA in 1981. The entire park is designated as open riding, except for an exclusion zone where a drainage canal flows through the park and into the Feather River oxbow. The park frequently floods from rainfall in wet months, and dries out in the summer. Because of the clay substrate, the shallow depressions formed from OHV use create vernal pools in the spring, providing habitat for native vernal pool plant species and branchiopod species. However, due to the history of disturbance and lack of original topography, many species at the park are ruderal non-natives. Heber Dunes: Heber Dunes SVRA is a small, 364-acre park in unincorporated Imperial County, seven miles northeast of Calexico, and is surrounded by agricultural fields, irrigation canals, and an undeveloped parcel owned by California Department of Transportation (CalTrans). It consists of open sand dunes, planted athel tamarisk (Tamarix aphylla) trees, and native and exotic desert scrub vegetation. The entire park is designated as open riding for off-highway vehicles. Hollister Hills: Hollister Hills SVRA is a 6,750 acre park located in northwest San Benito County, eight miles south of the city of Hollister. It is situated within the Gabilan Range of the California Coast ranges, in an area surrounded by primarily by rangelands. Hungry Valley: Hungry Valley SVRA is a 19,800 acre park within the Transverse Mountain Ranges, just south of Tejon Pass and the town of Gorman. The park is surrounded by National Forest land and by Tejon Ranch. Before becoming a SVRA in 1980, the park had a history of homesteading, mining, and unofficial OHV use. Oceano Dunes: This finescale vegetation map for Oceano Dunes SVRA was created to inform the park's Wildlife Habitat Protection Plan, using CDFW's VegCAMP standard methods. Field surveys were conducted in May 2022 by Chico State Geographic Information Center. Linework was conducted by Chico State Geographic Information Center. State Park staff provided edits to the draft map before it was finalized in 2023. An existing finescale map of the park was completed in 2013 (field surveys done in 2012) by MIG, report available here: https://nrm.dfg.ca.gov/documents/ContextDocs.aspx?cat=VegCAMP. Since vegetation in this park shifts frequently, and since large restoration projects have been conducted since the previous mapping effort, it was determined that an update to the map was needed. Chico State's Geographic Information Center (GIC) sampled the park in 2022 and conducted the linework to create this updated finescale vegetation map, with input from State Park staff. Vegetation was classified using a draft classification for the Santa Cruz-Santa Clara counties project, and by consulting with CDFW staff. Since GIC was also sampling and mapping other central coast State Parks in the region at the same time, the data for Pismo Beach is included here. Ocotillo Wells: This vegetation map was created in 2022-2023 to meet the above requirements and inform the Ocotillo Wells Wildlife Habitat Protection Plan. It was created by combining the existing maps from the DRECP mapping project 2016-2017 additions (Reyes et al.2021), and the Anza Borrego (1998) mapping project (See the VegCAMP website). State park staff including Melissa Patten, Leah Gardner, and Casey Paredes, conducted 25 recon surveys and additional map checks in March 2022 to groundtruth some areas, with a focus on the footprint of the older Anza Borrego project. Linework to edit the Anza Borrego project footprint area was done in 2023 using information from field surveys, and heads-up digitizing of NAIP 2020 imagery. Surveys conducted by State Parks staff in March 2022 focused on the Anza Borrego project footprint within the park, and then linework was done to update the vegetation polygons based on field surveys and 2020 NAIP aerial imagery. Prairie City: Prairie City SVRA is a 1,344 acre park located 20 miles east of Sacramento, in an ecological transition zone between the Central Valley and the Sierra foothills. Parts of the park have a history of dredge mining, and mine tailings form mounds and undulating topography in places. Other portions of the current park were formerly owned by Aerojet and used for a rocket engine program, contaminating groundwater and resulting in modern remediation and groundwater treatment efforts in the park, including monitoring and extraction wells. The imagery interpreted was NAIP 2020No accuracy assessment was done because almost all polygons were visited in the field. Minimum Mapping Units: Alameda Tesla, Carnegie, Heber Dunes, Hollister Hills, Hungry Valley, Prairie City.: The minimum mapping unit was 1 acre for upland vegetation types and ¼ acre for wetland vegetation types. Polygons were divided based on a change in cover class according to Braun-Blanquet categories (<1%, 1-5%, >5-15%, >15-25%, >25-50%, >50-75%, >75%). Breaks for the dominant overstory vegetation cover class required a 3-acre minimum mapping unit, and breaks for understory vegetation cover class required a 5-acre minimum mapping unit. Claypit: The minimum mapping unit was 1 acre, and ¼ acre for wetland or special types, which at the park includes only two small riparian stands and one patch of perennial grassland. The herbaceous stands that compose most of the park were split according to cover, but there was no maximum mapping unit size. Ocotillo Wells, Oceano Dunes: No minimum mapping unit was reported. Imagery: NAIP 2020 imagery was used for all SVRAs.

  9. s

    Land Use and Land Cover: Salinas Valley, Monterey County, California, 2005

    • searchworks.stanford.edu
    zip
    Updated Feb 8, 2018
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    (2018). Land Use and Land Cover: Salinas Valley, Monterey County, California, 2005 [Dataset]. https://searchworks.stanford.edu/view/by476jb7889
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    zipAvailable download formats
    Dataset updated
    Feb 8, 2018
    Area covered
    Salinas Valley, Salinas, Monterey County, California
    Description

    This polygon shapefile represents land use and land cover for the Pajaro River and San Benito River Watershed in San Benito, Santa Clara, and Santa Cruz counties of California for 2005. This shapefile was extracted from a generalized land use/land cover database of the Salinas-Pajaro region. Map unit categories were based on a modified Anderson Level II hierarchy. Mapping generally adhered to a 0.5 acre Minimum Mapping Unit (MMU) for riparian and agriculture types and 1 acre MMU for all upland, urban, or other land use types. Vegetation percent cover classes were assigned to the tree and shrub layers for each stand. Herbaceous vegetation was not assigned a cover class. All density values are measured in absolute cover, not relative cover. If tree cover is equal to or greater than 40% then the shrub cover is assigned a Not Assessed value of 9. The minimum mapping unit (MMU) resolution size of the land use/land cover polygons is twofold. In the intense agricultural region and for wetland and riparian areas the polygons have a 0.5 acre MMU. In the remainder of the study area, composed of non-agricultural areas, upland vegetation, and urban areas, the MMU is 1 acre. For thin linear-shaped polygons the MMU for width is one half the width of a full MMU square. Exceptions to the MMU guidance are noted in further criteria below. Because of the agricultural emphasis of the project, large urban developed areas, such as cities, towns, and villages, were not typically further subdivided other than for agricultural uses within their extents. The MMU size for these agricultural uses within urban areas is 0.5 acres. As noted above, the study area overlaps with the 2005 mapping of the Salinas River and San Benito river major riparian corridors that Aerial Information Systems, Inc. conducted for the Nature Conservancy. The MMU for the original projects was <0.5 acres. Where those units had not changed for 2005 and 2012 mapping, the map units were kept at the original polygon size. The 0.5 acre MMU is used for new mapping of riparian and wetland map units. Other Mapping Criteria includes photo interpretation of land cover is based on state-wide criteria for vegetation mapping.

  10. n

    Data from: Isostatic residual gravity map of The Santa Clara Valley and...

    • access.earthdata.nasa.gov
    • cmr.earthdata.nasa.gov
    pdf
    Updated Apr 24, 2017
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    (2017). Isostatic residual gravity map of The Santa Clara Valley and vicinity, California [Dataset]. https://access.earthdata.nasa.gov/collections/C2231553893-CEOS_EXTRA
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    pdfAvailable download formats
    Dataset updated
    Apr 24, 2017
    Time period covered
    Jan 1, 1970 - Present
    Area covered
    Description

    This map has 2 mGal gravity contours over a topographic base at a scale of 1:100,000. It covers the southern portion of San Francisco Bay, most of the Santa Clara Valley, and the surrounding mountains. It is a companion to U.S. Geological Survey Open-File Report 03-360, Shaded Relief Aeromagnetic Map of the Santa Clara Valley and Vicinity, California by Carter W. Roberts and Robert C. Jachens.

    [Summary provided by USGS.]

  11. c

    Santa Cruz Long-Toed Salamander Predicted Habitat - CWHR A003A [ds3233]

    • gis.data.ca.gov
    • data.cnra.ca.gov
    • +3more
    Updated Sep 15, 2025
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    California Department of Fish and Wildlife (2025). Santa Cruz Long-Toed Salamander Predicted Habitat - CWHR A003A [ds3233] [Dataset]. https://gis.data.ca.gov/maps/5e10100809da45f1bd737c304692c65c
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    Dataset updated
    Sep 15, 2025
    Dataset authored and provided by
    California Department of Fish and Wildlife
    License

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

    Area covered
    Description

    CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.

  12. Santa Clara 2021 Roll Year

    • gis.data.ca.gov
    • hub.arcgis.com
    • +1more
    Updated May 10, 2021
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    California Department of Tax and Fee Administration (2021). Santa Clara 2021 Roll Year [Dataset]. https://gis.data.ca.gov/maps/5d09233723204ee09075ab6a8fea3be1
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    Dataset updated
    May 10, 2021
    Dataset authored and provided by
    California Department of Tax and Fee Administrationhttp://cdtfa.ca.gov/
    License

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

    Area covered
    Description

    Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

  13. c

    BOE TRA 2025 co43

    • gis.data.ca.gov
    • gis-california.opendata.arcgis.com
    • +1more
    Updated Jun 9, 2025
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    California Department of Tax and Fee Administration (2025). BOE TRA 2025 co43 [Dataset]. https://gis.data.ca.gov/datasets/CDTFA::santa-clara-2025-roll-year?layer=1
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    Dataset updated
    Jun 9, 2025
    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 Santa Clara County for the specified assessment roll year. Boundary alignment is based on the 2012 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

  14. Vegetation - Santa Clara River Update - 2016 [ds2961]

    • data.ca.gov
    • data.cnra.ca.gov
    • +7more
    Updated Jul 24, 2025
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    California Department of Fish and Wildlife (2025). Vegetation - Santa Clara River Update - 2016 [ds2961] [Dataset]. https://data.ca.gov/dataset/vegetation-santa-clara-river-update-2016-ds29611
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    geojson, kml, html, csv, zip, 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

    Area covered
    Santa Clara River
    Description

    Western Foundation of Vertebrate Zoology contracted Stillwater Sciences in 2018 to create a fine-scale vegetation map of portions of the Santa Clara River. The mapping study area, consists of approximately 16,370 acres of Ventura County. Work was performed on the project during the summer and fall of 2018. The projects main goal was to address the need for detailed up-to-date vegetation information in support of identifying and modeling habitat for southwestern willow flycatcher, yellow-billed cuckoo, and least Bell's vireo. Funding for the project was provided by an Endangered Species Act Section 6 grant from the United States Fish and Wildlife Service to the California Department of Fish and Wildlife. This project builds off a prior mapping project that was conducted by Stillwater Sciences and URS, which was funded by the California State Coastal Conservancy and the Santa Clara River Trustee Council, in 2007. Species composition data collected in the field was compiled and reviewed in the office to assign the appropriate MCV alliance to each sampled location. In cases where the species present were best described by an MCV association (a sub-category of the broader MCV alliance), one was assigned. For field sampled locations with unique species composition not currently represented by an existing MCV alliance or association, a provisional alliance or association was created. In addition, some areas were classified into broader land cover types (e.g., agriculture, developed, riverwash). The vegetation map was produced applying digital aerial imagery (natural color, 2-foot resolution) from the National Agricultural Imagery Program (NAIP) (USDA-FSA 2016) flown in May, June, and July 2016. The minimum mapping unit (MMU) is 0.5 acres for most types and 0.1 for more unusual types that were discernable from areal photography and/or documented in the field. Once the map was made photointerpretation of the NAIP imagery took place in order to identify vegetation types. Field mapping took place after to refine the vegetation type definitions, CNPS vegetation reconnaissance field forms were used in the field. There was a total of 91 mapping classes. There was no accuracy assessment was done for this project. More information can be found in the project report, which is bundled with the vegetation map published for BIOs here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/2900_2999/ds2961.zip

  15. a

    Parcel Map Index

    • gis-cupertino.opendata.arcgis.com
    • hub.arcgis.com
    Updated Oct 16, 2015
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    City of Cupertino (2015). Parcel Map Index [Dataset]. https://gis-cupertino.opendata.arcgis.com/datasets/Cupertino::parcel-map-index/about
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    Dataset updated
    Oct 16, 2015
    Dataset authored and provided by
    City of Cupertino
    License

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

    Area covered
    Description

    Parcel Map Index is a Polygon FeatureClass showing approximate boundaries of Parcel Map recorded at Santa Clara County Clerk Recorders Office. Records are indexed by City assigned Parcel Map number. It is primarily used as a reference layer. The layer is updated as needed by the GIS Division. Parcel Map Index has the following fields:

    OBJECTID: Unique identifier automatically generated by Esri type: OID, length: 4, domain: none

    Parcel: The Assessor's Parcel Number type: String, length: 7, domain: none

    created_date: The date the database row was initially created type: Date, length: 8, domain: none

    last_edited_date: The date the database row was last updated type: Date, length: 8, domain: none

    Shape: Field that stores geographic coordinates associated with feature type: Geometry, length: 4, domain: none

    BookPage:

    type: String, length: 50, domain: none

    Shape.STArea():

    The area of the shape - in square feet type: Double, length: 0, domain: none

    Shape.STLength():

    The length of the shape - in feet type: Double, length: 0, domain: none

  16. a

    California Statewide Parcel Boundaries

    • hub.arcgis.com
    Updated Jul 9, 2020
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    County of Los Angeles (2020). California Statewide Parcel Boundaries [Dataset]. https://hub.arcgis.com/documents/baaf8251bfb94d3984fb58cb5fd93258
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    Dataset updated
    Jul 9, 2020
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    California
    Description

    This dataset includes one file for each of the 51 counties that were collected, as well as a CA_Merged file with the parcels merged into a single file.Note – this data does not include attributes beyond the parcel ID number (PARNO) – that will be provided when available, most likely by the state of California.DownloadA 1.6 GB zipped file geodatabase is available for download - click here.DescriptionA geodatabase with parcel boundaries for 51 (out of 58) counties in the State of California. The original target was to collect data for the close of the 2013 fiscal year. As the collection progressed, it became clear that holding to that time standard was not practical. Out of expediency, the date requirement was relaxed, and the currently available dataset was collected for a majority of the counties. Most of these were distributed with minimal metadata.The table “ParcelInfo” includes the data that the data came into our possession, and our best estimate of the last time the parcel dataset was updated by the original source. Data sets listed as “Downloaded from” were downloaded from a publicly accessible web or FTP site from the county. Other data sets were provided directly to us by the county, though many of them may also be available for direct download. Â These data have been reprojected to California Albers NAD84, but have not been checked for topology, or aligned to county boundaries in any way. Tulare County’s dataset arrived with an undefined projection and was identified as being California State Plane NAD83 (US Feet) and was assigned by ICE as that projection prior to reprojection. Kings County’s dataset was delivered as individual shapefiles for each of the 50 assessor’s books maintained at the county. These were merged to a single feature class prior to importing to the database.The attribute tables were standardized and truncated to include only a PARNO (APN). The format of these fields has been left identical to the original dataset. The Data Interoperablity Extension ETL tool used in this process is included in the zip file. Where provided by the original data sources, metadata for the original data has been maintained. Please note that the attribute table structure changes were made at ICE, UC Davis, not at the original data sources.Parcel Source InformationCountyDateCollecDateCurrenNotesAlameda4/8/20142/13/2014Download from Alamenda CountyAlpine4/22/20141/26/2012Alpine County PlanningAmador5/21/20145/14/2014Amador County Transportation CommissionButte2/24/20141/6/2014Butte County Association of GovernmentsCalaveras5/13/2014Download from Calaveras County, exact date unknown, labelled 2013Contra Costa4/4/20144/4/2014Contra Costa Assessor’s OfficeDel Norte5/13/20145/8/2014Download from Del Norte CountyEl Dorado4/4/20144/3/2014El Dorado County AssessorFresno4/4/20144/4/2014Fresno County AssessorGlenn4/4/201410/13/2013Glenn County Public WorksHumboldt6/3/20144/25/2014Humbodt County AssessorImperial8/4/20147/18/2014Imperial County AssessorKern3/26/20143/16/2014Kern County AssessorKings4/21/20144/14/2014Kings CountyLake7/15/20147/19/2013Lake CountyLassen7/24/20147/24/2014Lassen CountyLos Angeles10/22/201410/9/2014Los Angeles CountyMadera7/28/2014Madera County, Date Current unclear likely 7/2014Marin5/13/20145/1/2014Marin County AssessorMendocino4/21/20143/27/2014Mendocino CountyMerced7/15/20141/16/2014Merced CountyMono4/7/20144/7/2014Mono CountyMonterey5/13/201410/31/2013Download from Monterey CountyNapa4/22/20144/22/2014Napa CountyNevada10/29/201410/26/2014Download from Nevada CountyOrange3/18/20143/18/2014Download from Orange CountyPlacer7/2/20147/2/2014Placer CountyRiverside3/17/20141/6/2014Download from Riverside CountySacramento4/2/20143/12/2014Sacramento CountySan Benito5/12/20144/30/2014San Benito CountySan Bernardino2/12/20142/12/2014Download from San Bernardino CountySan Diego4/18/20144/18/2014San Diego CountySan Francisco5/23/20145/23/2014Download from San Francisco CountySan Joaquin10/13/20147/1/2013San Joaquin County Fiscal year close dataSan Mateo2/12/20142/12/2014San Mateo CountySanta Barbara4/22/20149/17/2013Santa Barbara CountySanta Clara9/5/20143/24/2014Santa Clara County, Required a PRA requestSanta Cruz2/13/201411/13/2014Download from Santa Cruz CountyShasta4/23/20141/6/2014Download from Shasta CountySierra7/15/20141/20/2014Sierra CountySolano4/24/2014Download from Solano Couty, Boundaries appear to be from 2013Sonoma5/19/20144/3/2014Download from Sonoma CountyStanislaus4/23/20141/22/2014Download from Stanislaus CountySutter11/5/201410/14/2014Download from Sutter CountyTehama1/16/201512/9/2014Tehama CountyTrinity12/8/20141/20/2010Download from Trinity County, Note age of data 2010Tulare7/1/20146/24/2014Tulare CountyTuolumne5/13/201410/9/2013Download from Tuolumne CountyVentura11/4/20146/18/2014Download from Ventura CountyYolo11/4/20149/10/2014Download from Yolo CountyYuba11/12/201412/17/2013Download from Yuba County

  17. a

    Sun Cloud Municipal Planning Areas

    • arizona-sun-cloud-agic.hub.arcgis.com
    Updated Jul 8, 2021
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    AZGeo ArcGIS Online (AGO) (2021). Sun Cloud Municipal Planning Areas [Dataset]. https://arizona-sun-cloud-agic.hub.arcgis.com/datasets/azgeo::sun-cloud-municipal-planning-areas
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    Dataset updated
    Jul 8, 2021
    Dataset authored and provided by
    AZGeo ArcGIS Online (AGO)
    License

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

    Area covered
    Description

    MPA Boundary Creation:Created from a variety of source reference input. Small topological errors resolved to larger adjacent poly where necessary. Polylines may not be exact compared to source datasets necessary to resolve topological errors.Pima County:Oro ValleyDigitized from 2016 General Plan, referencing GIS linework from previous General Plan & Environmentally Sensitive Lands Project (previous TSSW project work for OV).https://www.orovalleyaz.gov/files/assets/public/documents/town-manager/general-plan/yourvoiceourfuturegeneralplan.pdfhttps://www.orovalleyaz.gov/files/assets/public/documents/town-manager/general-plan/general-plan-land-use-map.pdfAreas where OV General Plan conflicted with Marana General Plan – area had since been annexed into Marana and has included in the Marana GP polys. MaranaDigitized from Make Marana – 2040 General PlanMap atlas available at: https://static1.squarespace.com/static/54cc191ce4b0f886f4762582/t/5e50496454d84d590324e663/1582320010384/Marana+Map+Atlas+2.27.19+w_editsa.pdfSpecifically referenced Figure 1: Planning Area map dated 2/2019SahuaritaDigitized from Aspire 2035 – Sahuarity’s General Plan https://sahuaritaaz.gov/DocumentCenter/View/1169/Aspire-2035-Sahuaritas-General-Plan-Amended-2020?bidId=City of Tucson Digitized from COT Future Growth Scenario Plan .pdf https://www.tucsonaz.gov/files/integrated-planning/LT-7_Future_Growth_Scenario_Map_7-8-13.pdfPart of Plan Tucsonhttps://www.tucsonaz.gov/pdsd/plan-tucsonSanta Cruz County: **Note: The following had instances of overlapping polygons in the reference data and were left as a topological exceptions of overlapping polygons. Nogales & Patagonia General Plans, as both had the area covered in their GPs Tres Alamos General Plan & Benson GPSt David GP and Denson GPCCNogalesVerified linework from Nogales General Plan 2010http://www.azplanningcenter.com/Nogales/Files/2010_08_20-000-ExecutiveSummary_IntroductionAndOverview.pdfPatagoniaVerified linework from 2009 General Planhttps://issuu.com/seagoedd/docs/patagonia_general_plan?layout=http%253A%252F%252Fskin.issuu.com%252Fv%252Flight%252Flayout.xml&showFlipBtn=trueCochise County Created from various input GIS datasets:Comprehensive Plan Growth Areas feature classComprehensive Plan Design feature class

  18. AVCA Boundary 2019

    • avca-open-data-avca.hub.arcgis.com
    Updated Nov 21, 2018
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    ADMIN_AVCA (2018). AVCA Boundary 2019 [Dataset]. https://avca-open-data-avca.hub.arcgis.com/datasets/65d1c58205064196841da533169cb287
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    Dataset updated
    Nov 21, 2018
    Dataset provided by
    American Volleyball Coaches Associationhttps://www.avca.org/
    Authors
    ADMIN_AVCA
    Area covered
    Description

    This is the 2017 shapefile version of the AVCA administrative boundary. It generally follows the previous Resource 2000 boundary with the following modifications. Priority of drawing went first to the Pima County Parcel map, then to the NHD watershed boundary, and finally to Santa Cruz Parcels.This file is a derivative of the AVCA_Boundary_2017 feature service located here: https://services6.arcgis.com/r8sri7egU6P44VfN/arcgis/rest/services/AVCA_Boundary_2017/FeatureServer2/9/2019 added computed (GIS) acres based on various common coordinate systems: NAD83 UTM zone 12N = 577,866StatePlane = 578,224Pima County Coordinate System: 578,451

  19. a

    Santa Clara County Airport Influence Areas

    • gisdata-sccplanning.hub.arcgis.com
    Updated Aug 16, 2024
    + more versions
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    Santa Clara County Planning and Development Department (2024). Santa Clara County Airport Influence Areas [Dataset]. https://gisdata-sccplanning.hub.arcgis.com/maps/a2a542fca00f4a01a8f1dd4c2b6eaaab
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    Dataset updated
    Aug 16, 2024
    Dataset authored and provided by
    Santa Clara County Planning and Development Department
    Area covered
    Description

    Several layers with information regarding Land Use around Airports within Santa Clara County, established by the Airport Land Use Commission (ALUC). The Airport Influence Area (AIA) layer has the AIA boundaries around each airport. Safety Zones is a layer of boundaries for the various safety zones for each airport.CNEL is a layer of sound levels around each airport, measured in decibels.The Part 77 layer is building height restrictions around each airport.Flightpaths are the approach and departure paths for each airport.

  20. a

    BOE TRA 2022 co43

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • gis.data.ca.gov
    • +3more
    Updated May 23, 2022
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    California Department of Tax and Fee Administration (2022). BOE TRA 2022 co43 [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/CDTFA::santa-clara-2022-roll-year?layer=0
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    Dataset updated
    May 23, 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 Santa Clara County for the specified assessment roll year. Boundary alignment is based on the 2012 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

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

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(2018). Assessor's Map Books: Santa Cruz County, California, 2015 [Dataset]. https://searchworks.stanford.edu/view/qd150sw6768

Assessor's Map Books: Santa Cruz County, California, 2015

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zipAvailable download formats
Dataset updated
Jul 28, 2018
Area covered
Santa Cruz County, California
Description

The Geographic Information Systems (GIS) Unit falls under the purview of the County of Santa Cruz Information Services Department. The GIS Unit serves all County departments and external customers and provides data on land, features and people of Santa Cruz County. Santa Cruz County encompasses 4 cities and approximately 265,000 people. This coverage can be used for basic applications such as viewing, querying and map output production, or to provide a basemap to support graphical overlays and analyses of geospatial data.

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