Comprehensive dataset of 165 Junk removal services in California, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 1,068 Laser hair removal services in California, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 123 Snow removal services in California, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This database was designed in response to the Director Memorandum - "Effective January 1, 2019 all structure greater than 120 square feet in the State Responsibility Area (SRA) damaged by wildfire will be inspected and documented in the DINS Collector App."

This dataset was updated April, 2024.This ownership dataset was generated primarily from CPAD data, which already tracks the majority of ownership information in California. CPAD is utilized without any snapping or clipping to FRA/SRA/LRA. CPAD has some important data gaps, so additional data sources are used to supplement the CPAD data. Currently this includes the most currently available data from BIA, DOD, and FWS. Additional sources may be added in subsequent versions. Decision rules were developed to identify priority layers in areas of overlap.Starting in 2022, the ownership dataset was compiled using a new methodology. Previous versions attempted to match federal ownership boundaries to the FRA footprint, and used a manual process for checking and tracking Federal ownership changes within the FRA, with CPAD ownership information only being used for SRA and LRA lands. The manual portion of that process was proving difficult to maintain, and the new method (described below) was developed in order to decrease the manual workload, and increase accountability by using an automated process by which any final ownership designation could be traced back to a specific dataset.The current process for compiling the data sources includes: Clipping input datasets to the California boundary Filtering the FWS data on the Primary Interest field to exclude lands that are managed by but not owned by FWS (ex: Leases, Easements, etc) Supplementing the BIA Pacific Region Surface Trust lands data with the Western Region portion of the LAR dataset which extends into California. Filtering the BIA data on the Trust Status field to exclude areas that represent mineral rights only. Filtering the CPAD data on the Ownership Level field to exclude areas that are Privately owned (ex: HOAs) In the case of overlap, sources were prioritized as follows: FWS > BIA > CPAD > DOD As an exception to the above, DOD lands on FRA which overlapped with CPAD lands that were incorrectly coded as non-Federal were treated as an override, such that the DOD designation could win out over CPAD.In addition to this ownership dataset, a supplemental _source dataset is available which designates the source that was used to determine the ownership in this dataset.Data Sources: GreenInfo Network's California Protected Areas Database (CPAD2023a). https://www.calands.org/cpad/; https://www.calands.org/wp-content/uploads/2023/06/CPAD-2023a-Database-Manual.pdf US Fish and Wildlife Service FWSInterest dataset (updated December, 2023). https://gis-fws.opendata.arcgis.com/datasets/9c49bd03b8dc4b9188a8c84062792cff_0/explore Department of Defense Military Bases dataset (updated September 2023) https://catalog.data.gov/dataset/military-bases Bureau of Indian Affairs, Pacific Region, Surface Trust and Pacific Region Office (PRO) land boundaries data (2023) via John Mosley John.Mosley@bia.gov Bureau of Indian Affairs, Land Area Representations (LAR) and BIA Regions datasets (updated Oct 2019) https://biamaps.doi.gov/bogs/datadownload.htmlData Gaps & Changes:Known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. Additionally, any feedback received about missing or inaccurate data can be taken back to the appropriate source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.24_1: Input datasets this year included numerous changes since the previous version, particularly the CPAD and DOD inputs. Of particular note was the re-addition of Camp Pendleton to the DOD input dataset, which is reflected in this version of the ownership dataset. We were unable to obtain an updated input for tribral data, so the previous inputs was used for this version.23_1: A few discrepancies were discovered between data changes that occurred in CPAD when compared with parcel data. These issues will be taken to CPAD for clarification for future updates, but for ownership23_1 it reflects the data as it was coded in CPAD at the time. In addition, there was a change in the DOD input data between last year and this year, with the removal of Camp Pendleton. An inquiry was sent for clarification on this change, but for ownership23_1 it reflects the data per the DOD input dataset.22_1 : represents an initial version of ownership with a new methodology which was developed under a short timeframe. A comparison with previous versions of ownership highlighted the some data gaps with the current version. Some of these known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. In addition, any topological errors (like overlaps or gaps) that exist in the input datasets may thus carry over to the ownership dataset. Ideally, any feedback received about missing or inaccurate data can be taken back to the relevant source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.

This dataset is intended to provide a statewide depiction of land ownership in California. It includes lands owned by each federal agency, state agency, local government entities, conservation organizations, and special districts. It does not include lands that are in private ownership.

Ownership is derived from CAL FIRE's State Responsibility Area (SRA) dataset and GreenInfo Network's California Protected Areas Database (CPAD).

CAL FIRE tracks lands owned by federal agencies as part of our efforts to maintain fire protection responsibility boundaries, captured as part of our State Responsibility Areas (SRA) dataset. This effort draws on data provided by various federal agencies including USDA Forest Service, BLM, National Park Service, US Fish and Wildlife Service, and Bureau of Indian Affairs. Since SRA lands are matched to county parcel data where appropriate, often federal land boundaries are also adjusted to match parcels, and may not always exactly match the source federal data.

Federal lands from the SRA dataset are combined with ownership data for non-federal lands from CPAD, in order to capture lands owned by various state and local agencies, special districts, and conservation organizations. Data from CPAD are imported directly and not adjusted to match parcels or other features. However, CPAD features may be trimmed if they overlap federal lands from the SRA dataset.

This service represents the latest release of the dataset by FRAP, and is updated annually. As of November 2018, it represents ownership18_2.

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015 Data Limitations: Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal. Data Collection Methodology: The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database. Secondary/Related Resources:

Below is an explanation of the data along with some features that are available on this map (description is also provided in the "Getting Started" widget of the application).A variety of different colored circles appear throughout the map. They represent sites that are associated with the following programs:1) Department of Toxic Substances Control (DTSC) sites:a) Historical Inactive - Identifies sites from an older database that are non-active sites where, through a Preliminary Endangerment Assessment (PEA) or other evaluation, DTSC has determined that a removal or remedial action or further extensive investigation is required.b) School Cleanup - Identifies proposed and existing school sites that are being evaluated by DTSC for possible hazardous materials contamination. School sites are further defined as “Cleanup”, where remedial actions are or have occurred.c) School Evaluation - Identifies proposed and existing school sites that are being evaluated by DTSC for possible hazardous materials contamination. School sites are further defined as “Evaluation”, where further investigation is needed.d) Corrective Action - Investigation or cleanup activities at Resource Conservation and Recovery Act (RCRA) or state-only hazardous waste facilities (that were required to obtain a permit or have received a hazardous waste facility permit from DTSC or U.S. EPA).e) State Response - Identifies confirmed release sites where DTSC is involved in remediation, either in a lead or oversight capacity. These confirmed release sites are generally high-priority and high potential risk.f) Evaluation - Identifies suspected, but unconfirmed, contaminated sites that need or have gone through a limited investigation and assessment process.g) Tiered Permit - A corrective action cleanup project on a hazardous waste facility that either was eligible to treat or permitted to treat waste under the Tiered Permitting system.2) State Water Board or DTSC sites:a) Leaking Underground Storage Tank (LUST) Cleanup - Includes all Underground Storage Tank (UST) sites that have had an unauthorized release (i.e. leak or spill) of a hazardous substance, usually fuel hydrocarbons, and are being (or have been) cleaned up. These sites are regulated under the State Water Board's UST Cleanup Program and/or similar programs conducted by each of the nine Regional Water Boards or Local Oversight Programs.b) Cleanup Program - Includes all "non-federally owned" sites that are regulated under the State Water Board's Site Cleanup Program and/or similar programs conducted by each of the nine Regional Water Boards. Cleanup Program Sites are also commonly referred to as "Site Cleanup Program sites".c) Voluntary Cleanup - Identifies sites with either confirmed or unconfirmed releases, and the project proponents have requested that the State Water Board or DTSC oversee evaluation, investigation, and/or cleanup activities and have agreed to provide coverage for the lead agency’s costs.3) Othera) Permitted Tanks - The "Permitted Tanks" data set includes Facilities that are associated with permitted underground storage tanks from the California Environmental Reporting System (CERS) database. The CERS data consists of current and recently closed permitted underground storage tank (UST) facilities information provided to CERS by Certified Unified Program Agencies (CUPAs).*Note: Underground Storage Tank Cleanup and Cleanup Program project records are pulled from the State Water Board's GeoTracker database. The Permitted Tanks information was obtained from California EPA’s California Environmental Reporting System (CERS) database. All other project records were obtained from DTSC's EnviroStor database. Program descriptions come from DTSC’s EnviroStor Glossary of Terms and the State Water Board’s GeoTracker Site/Facility Type Definitions. The information associated with these records was last updated in the application on 4/24/2023.

This dataset contains a list of inventory entries that have been removed from Ontario’s data catalogue. The following details are included: * public title * date removed * ministry responsible at removal * access level at time of removal * removal rationale * new or related URL for access, if applicable No open data has been removed. Dataset entries are only removed from Ontario’s data catalogue when they represent a duplication, consolidation, or have been added in error. It is the responsibility of each ministry to keep their list of datasets up to date to ensure that the catalogue is a consistent and reliable source of government data.

This 2018 Klamath River data set was a multi-agency collaboration to collect pre-dam removal topography and imagery across the rivers 260 mile corridor. Airborne topo-bathymetric LiDAR, boat based multi-beam sonar, and aerial imagery was collected from the mouth at estuary to the head waters near Klamath Lake in Oregon. This comprehensive baseline data set will help inform the scientific and restoration community to more thoroughly understand the effects of dam removal, quantitatively measure sediment transport evolution, and to help monitor the biological and physical response of a new free flowing Klamath River. For direct download of ancillary raster products for the Klamath River click here For direct download of the orthomosaic for the entire Klamath River click here

Comprehensive dataset of 3 Auto dent removal services in Baja California Sur, Mexico as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset contains cleaned GBIF (www.gbif.org) occurrence records and associated climate and environmental data for all arthropod prey of listed species in California drylands as identified in Lortie et al. (2023): https://besjournals.onlinelibrary.wiley.com/doi/full/10.1002/2688-8319.12251. All arthropod records were downloaded from GBIF (https://doi.org/10.15468/dl.ngym3r) on 14 November 2022. Records were imported into R using the rgbif package and cleaned with the coordinateCleaner package to remove occurrence data with likely errors. Environmental data include bioclimatic variables from WorldClim (www.worldclim.org), landcover and NDVI data from MODIS and the LPDAAC (https://lpdaac.usgs.gov/), elevation data from the USGS (https://www.sciencebase.gov/catalog/item/542aebf9e4b057766eed286a), and distance to the nearest road from the census bureau's TIGER/Line road shapefile (https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html). All environmental data were combined into a stacked raster and we extracted the environmental variables for each occurrence record from this raster to make the final dataset.

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015 Data Limitations: Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal. Data Collection Methodology: The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database. Secondary/Related Resources: State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx United Nations Standard Products and Services Code, http://www.unspsc.org/

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, coastal buffers are removed, leaving the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal Buffers (this dataset)Place AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

The California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical wildland fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, California State Parks, National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data. This data should be used carefully for statistical analysis and reporting due to missing perimeters (see Use Limitation in metadata). Some fires are missing because historical records were lost or damaged, were too small for the minimum cutoffs, had inadequate documentation or have not yet been incorporated into the database. Other errors with the fire perimeter database include duplicate fires and over-generalization. Additionally, over-generalization, particularly with large old fires, may show unburned "islands" within the final perimeter as burned. Users of the fire perimeter database must exercise caution in application of the data. Careful use of the fire perimeter database will prevent users from drawing inaccurate or erroneous conclusions from the data. This data is updated annually in the spring with fire perimeters from the previous fire season. This dataset may differ in California compared to that available from the National Interagency Fire Center (NIFC) due to different requirements between the two datasets. The data covers fires back to 1878. As of May 2025, it represents fire24_1. Please help improve this dataset by filling out this survey with feedback:Historic Fire Perimeter Dataset Feedback (arcgis.com) Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 impacted residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres. Version update:Firep24_1 was released in April 2025. Five hundred forty-eight fires from the 2024 fire season were added to the database (2 from BIA, 56 from BLM, 197 from CAL FIRE, 193 from Contract Counties, 27 from LRA, 8 from NPS, 55 from USFS and 8 from USFW). Six perimeters were added from the 2025 fire season (as a special case due to an unusual January fire siege). Five duplicate fires were removed, and the 2023 Sage was replaced with a more accurate perimeter. There were 900 perimeters that received updated attribution (705 removed “FIRE” from the end of Fire Name field and 148 replaced Complex IRWIN ID with Complex local incident number for COMPLEX_ID field). The following fires were identified as meeting our collection criteria but are not included in this version and will hopefully be added in a future update: Addie (2024-CACND-002119), Alpaugh (2024-CACND-001715), South (2024-CATIA-001375). One perimeter is missing containment date that will be updated in the next release.Cross checking CALFIRS reporting for new CAL FIRE submissions to ensure accuracy with cause class was added to the compilation process. The cause class domain description for “Powerline” was updated to “Electrical Power” to be more inclusive of cause reports. Includes separate layers filtered by criteria as follows:California Fire Perimeters (All): Unfiltered. The entire collection of wildfire perimeters in the database. It is scale dependent and starts displaying at the country level scale. Recent Large Fire Perimeters (≥5000 acres): Filtered for wildfires greater or equal to 5,000 acres for the last 5 years of fires (2020-January 2025), symbolized with color by year and is scale dependent and starts displaying at the country level scale. Year-only labels for recent large fires.California Fire Perimeters (1950+): Filtered for wildfires that started in 1950-January 2025. Symbolized by decade, and display starting at country level scale. Detailed metadata is included in the following documents:Wildland Fire Perimeters (Firep24_1) MetadataSee more information on our Living Atlas data release here: CAL FIRE Historical Fire Perimeters Available in ArcGIS Living AtlasFor any questions, please contact the data steward:Kim Wallin, GIS SpecialistCAL FIRE, Fire & Resource Assessment Program (FRAP)kimberly.wallin@fire.ca.gov

Fish benthic trawls were completed by the National Marine Fisheries Service Groundfish Survey Program (NMFS GSP). Data from 477 fishery independent trawls ranging from 55-1200 m in depth were collected June-November in 1977, 1989, 1992, 1995, and 1997- 2002. Fish that were not present in at least 5% of the trawls were removed from this analysis, resulting in a dataset containing information on 59 fish species. Site groups were calculated using the Bray-Curtis dissimilarity coefficient with average means clustering (Romesburg, 1991). Gear included a 7.6-m head-rope semi balloon otter trawl with 1.25 cm codend mesh trawled for 5-10 minutes at each station.

Note: Blueprint has been retired as of June 15, 2021. This dataset will be kept up for historical purposes, but will no longer be updated. California has a new blueprint for reducing COVID-19 in the state with revised criteria for loosening and tightening restrictions on activities. Every county in California is assigned to a tier based on its test positivity and adjusted case rate for tier assignment. Additionally, a new health equity metric took effect on October 6, 2020. In order to advance to the next less restrictive tier, each county will need to meet an equity metric or demonstrate targeted investments to eliminate disparities in levels of COVID-19 transmission, depending on its size. The California Health Equity Metric is designed to help guide counties in their continuing efforts to reduce COVID-19 cases in all communities and requires more intensive efforts to prevent and mitigate the spread of COVID-19 among Californians who have been disproportionately impacted by this pandemic. Please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID19CountyMonitoringOverview.aspx for more information. Also, in lieu of a Data Dictionary, please refer to the detailed explanation of the data columns in Appendix 1 of the above webpage. Because this data is in machine-readable format, the merged headers at the top of the source spreadsheet have not been included: The first 8 columns are under the header "County Status as of Tier Assignment" The next 3 columns are under the header "Current Data Week Tier and Metric Tiers for Data Week" The next 4 columns are under the header "Case Rate Adjustment Factors" The next column is under the header "Small County Considerations" The last 5 columns are under the header "Health Equity Framework Parameters"

This database was designed in response to the Director Memorandum - "Effective January 1, 2019 all structure greater than 120 square feet in the State Responsibility Area (SRA) damaged by wildfire will be inspected and documented in the DINS Collector App."

On October 30, 2015 Governor Brown issued an emergency declaration requiring public agencies to identify areas of tree mortality that hold the greatest potential to result in wildfire and/or falling trees and threaten people and property in these areas. Once identified, these areas will be prioritized for removal of dead and dying trees that present a threat to public safety. Tier One High Hazard Zones are areas where assets to be protected and tree mortality directly coincide. These are the areas designated by state and local governments as being in greatest need of dead tree removal, pursuant to the California Governor's Emergency proclamation on October 30, 2015. These areas are considered as having the highest potential of being a safety issue to people, buildings and infrastructure. Dead trees heighten wildfire risk and can be hazardous if they fall.This service represents the latest official release of HHZ. It will be updated annually when a new version is released. As of June 2019, it represents HighHazardZones19_1.