This is the last boundary change until the next redistricting following the 2030 Census. All of the districts now reflect the 2021 Citizens Redistricting Commission(CRC) plan. The only thing that will change is the members' names and parties as elections are held, appointments are made, or party affiliations change.Senate Districts feature layer is updated as-needed and we expect to update it more regularly in the future.Schema:F2020_POP: The 2020 population of the district as reported by the census.F2020_HU: Number of housing units in the district in 2020 as reported by the census.CRC_POP: Citizen's Redistricting Commission population.District: The District is the district number.Party: The Party is the party represented.last_name: The last name is the last name of the representative.first_name: The first name is the first name of the representative.district_website: The district website is the link to the district website.For more information about the F2020_Pop and the F2020_HU visit: https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.html

This feature service is derived from the Esri "United States Zip Code Boundaries" layer, queried to only CA data.For the original data see: https://esri.maps.arcgis.com/home/item.html?id=5f31109b46d541da86119bd4cf213848Published by the California Department of Technology Geographic Information Services Team.The GIS Team can be reached at ODSdataservices@state.ca.gov.U.S. ZIP Code Boundaries represents five-digit ZIP Code areas used by the U.S. Postal Service to deliver mail more effectively. The first digit of a five-digit ZIP Code divides the United States into 10 large groups of states (or equivalent areas) numbered from 0 in the Northeast to 9 in the far West. Within these areas, each state is divided into an average of 10 smaller geographical areas, identified by the second and third digits. These digits, in conjunction with the first digit, represent a Sectional Center Facility (SCF) or a mail processing facility area. The fourth and fifth digits identify a post office, station, branch or local delivery area.As of the time this layer was published, in January 2025, Esri's boundaries are sourced from TomTom (June 2024) and the 2023 population estimates are from Esri Demographics. Esri updates its layer annually and those changes will immediately be reflected in this layer. Note that, because this layer passes through Esri's data, if you want to know the true date of the underlying data, click through to Esri's original source data and look at their metadata for more information on updates.Cautions about using Zip Code boundary dataZip code boundaries have three characteristics you should be aware of before using them:Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.As of early 2025, USPS appears to be in the process of releasing boundaries, which will at least provide an authoritative source, but because of the other factors above, we do not recommend these boundaries for many use cases. If you are using these for anything other than mailing purposes, we recommend reconsideration. We provide the boundaries as a convenience, knowing people are looking for them, in order to ensure that up-to-date boundaries are available.

Data is from the California Department of Public Health (CDPH) Respiratory Virus Weekly Report. The report is updated each Friday. Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week. Laboratory surveillance for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (parainfluenza types 1-4, human metapneumovirus, non-SARS-CoV-2 coronaviruses, adenovirus, enterovirus/rhinovirus) involves the use of data from clinical sentinel laboratories (hospital, academic or private) located throughout California. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for influenza, respiratory syncytial virus, and other respiratory viruses in California. These laboratories report the number of laboratory-confirmed influenza, respiratory syncytial virus, and other respiratory virus detections and isolations, and the total number of specimens tested by virus type on a weekly basis. Test positivity for a given week is calculated by dividing the number of positive COVID-19, influenza, RSV, or other respiratory virus results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday. Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19 and influenza-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset provided by the State of California Department of Finance (https://dof.ca.gov/forecasting/demographics/projections/). Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html). CDPH collaborates with Northern California Kaiser Permanente (NCKP) to monitor trends in RSV admissions. The percentage of RSV admissions is calculated by dividing the number of RSV-related admissions by the total number of admissions during the same period. Admissions for pregnancy, labor and delivery, birth, and outpatient procedures are not included in total number of admissions. These admissions serve as a proxy for RSV activity and do not necessarily represent laboratory confirmed hospitalizations for RSV infections; NCKP members are not representative of all Californians. Weekly hospitalization data are defined as Sunday through Saturday. Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify influenza, respiratory syncytial virus, and COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all influenza, respiratory syncytial virus, and COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday. Wastewater data: This dataset represents statewide weekly SARS-CoV-2 wastewater summary values. SARS-CoV-2 wastewater concentrations from all sites in California are combined into a single, statewide, unit-less summary value for each week, using a method for data transformation and aggregation developed by the CDC National Wastewater Surveillance System (NWSS). Please see the CDC NWSS data methods page for a description of how these summary values are calculated. Weekly wastewater data are defined as Sunday through Saturday.

The dataset ds181_Chinook_ln is a product of the CalFish Adult Salmonid Abundance Database. Data in this shapefile are collected from stream sections or reaches where Chinook population monitoring occurs and that are best represented by linear features. Some escapement monitoring locations are logically represented by point features, such as dams and hatcheries. See the companion point feature shapefile ds180_Chinook_pnts for information collected from point locations.The CalFish Abundance Database contains a comprehensive collection of anadromous fisheries abundance information. Beginning in 1998, the Pacific States Marine Fisheries Commission, the California Department of Fish and Game, and the National Marine Fisheries Service, began a cooperative project aimed at collecting, archiving, and entering into standardized electronic formats, the wealth of information generated by fisheries resource management agencies and tribes throughout California.The data format provides for sufficient detail to convey the relative accuracy of each population trend index record yet is simple and straight forward enough to be suited for public use. For those interested in more detail the database offers hyperlinks to digital copies of the original documents used to compile the information. In this way the database serves as an information hub directing the user to additional supporting information. This offers utility to field biologists and others interested in obtaining information for more in-depth analysis. Hyperlinks, built into the spatial data attribute tables used in the BIOS and CalFish I-map viewers, open the detailed index data archived in the on-line CalFish database application. The information can also be queried directly from the database via the CalFish Tabular Data Query. Once the detailed annual trend data are in view, another hyperlink opens a digital copy of the document used to compile each record.During 2010, as a part of the Central Valley Chinook Comprehensive Monitoring Plan, the CalFish Salmonid Abundance Database was reorganized and updated. CalFish provides a central location for sharing Central Valley Chinook salmon escapement estimates and annual monitoring reports to all stakeholders, including the public. Annual Chinook salmon in-river escapement indices that were, in many cases, eight to ten years behind are now current though 2009. In some cases, multiple datasets were consolidated into a single, more comprehensive, dataset to more closely reflect how data are reported in the California Department of Fish and Game standard index, Grandtab.Extensive data are currently available in the CalFish Abundance Database for California Chinook, coho, and steelhead. Major data categories include adult abundance population estimates, actual fish and/or carcass counts, counts of fish collected at dams, weirs, or traps, and redd counts. Harvest data has also been compiled for many streams.This CalFish Abundance Database shapefile was generated from fully routed 1:100,000 hydrography. In a few cases streams had to be added to the hydrography dataset in order to provide a means to create shapefiles to represent abundance data associated with them. Streams added were digitized at no more than 1:24,000 scale based on stream line images portrayed in 1:24,000 Digital Raster Graphics (DRG).The features in this layer represent the location for which abundance data records apply. In many cases there are multiple datasets associated with the same location, and so, features may overlap. Please view the associated datasets for detail regarding specific features. In CalFish these are accessed through the "link" field that is visible when performing an identify or query operation. A URL string is provided with each feature in the downloadable data which can also be used to access the underlying datasets.The Chinook data that is available from the CalFish website is actually mirrored from the StreamNet website where the CalFish Abundance Databases tabular data is currently stored. Additional information about StreamNet may be downloaded at http://www.streamnet.org. Complete documentation for the StreamNet database may be accessed at http://http://www.streamnet.org/def.html

This data set shows the following indicators: population breakdown by ethnicity, household income, education level, employment, age and sex. Data is broken down by the different Toronto neighbourhoods. CITY OF TORONTO NATIONAL HOUSEHOLD SURVEY METHODOLOGY NOTATION There were changes in the way information was collected for portions of the 2011 Census. This will impact the extent to which comparisons can be made with other Census periods on some Census variables. In general, 2011 Census data on population, dwelling counts, age, sex, families, household living arrangements, marital status, structural types of dwellings types and language can be compared to the data from other Censuses, with due regard for changing definitions of individual variables. Information on Aboriginal peoples, immigration, ethnocultural diversity, education, labour, income and housing was collected differently in 2011 as part of a voluntary National Household Survey (NHS) by Statistics Canada. In general, the 2011 NHS data is less comparable to that of the other Censuses due to non-response bias inherent in voluntary surveys. The risk of a voluntary survey is that the results may only reflect the kinds of individuals who are inclined to participate in a survey in the first place. As the National Household Survey User Guide notes, "because non-respondents tend to have different characteristics from respondents. As a result, there is a risk that the results will not be representative of the actual population." Comparisons between the 2011 NHS and other Censuses should not be considered fully reliable.

This cartographic digital product is derived from the Atlas of Canada's wall map "The World" (MCR 0046) and "Le monde" (MCR 0046F) published in 2021. The World is a general reference political map focused on the names and international boundaries of sovereign and non-sovereign countries. The information is portrayed using the Winkel II projection at a scale of 1:29 000 000. The dataset includes international boundaries, populated places, and labelled major hydrographic and physical features. In the geodatabase the representation of political boundaries do not necessarily reflect the position of the Government of Canada on all international issues of recognition, sovereignty or jurisdiction; some of the populated places have seasonal populations, while others are research or military bases with no permanent populations; and, there are no attribute information in the geodatabase for the labelled hydrographic and physical features.

A joint venture involving the National Atlas programs in Canada (Natural Resources Canada), Mexico (Instituto Nacional de Estadística Geografía e Informática), and the United States (U.S. Geological Survey), as well as the North American Commission for Environmental Co-operation, has led to the release (June 2004) of several new products: an updated paper map of North America, and its associated geospatial data sets and their metadata. These data sets are available online from each of the partner countries both for visualization and download. The North American Atlas data are standardized geospatial data sets at 1:10,000,000 scale. A variety of basic data layers (e.g. roads, railroads, populated places, political boundaries, hydrography, bathymetry, sea ice and glaciers) have been integrated so that their relative positions are correct. This collection of data sets forms a base with which other North American thematic data may be integrated. Any data outside of Canada, Mexico, and the United States of America included in the North American Atlas data sets is strictly to complete the context of the data. The North American Atlas - Populated Places data set shows a selection of named populated places suitable for use at a scale of 1:10,000,000. Places, which refer to individual municipalities, are always shown using point symbols. These symbols have been fitted to the North American Atlas roads, railroads, and hydrography layers, so that the points represent the approximate locations of places relative to data in these other layers. The selection of populated places was based on local importance (as shown by population size), importance as a cross-border point, and, occasionally, on other factors. All capital cities (national, provincial, territorial or State) are shown for Canada, Mexico, and the United States of America. Attributes were added to the data to reflect population class, name, and capital. Cartographic considerations were taken into account so that names do not overlap in crowded areas, nor are there too many names shown for sparsely-populated areas.

This issue describes in detail the health region limits as of October 2013 and their correspondence with the 2011 and 2006 Census geography. Health regions are defined by the provinces and represent administrative areas or regions of interest to health authorities. This product contains correspondence files (linking health regions to census geographic codes) and digital boundary files. User documentation provides an overview of health regions, sources, methods, limitations and product description (file format and layout). The 2013 Health Regions: Boundaries and Correspondence with Census Geography reflects the boundaries as of October 2013 and provides the geographic linkage to 2011 and 2006 Censuses. For current Health Regions data, refer to Statistics Canada.

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.html

Data 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