Parcels and Land Ownership dataset current as of unknown. This data set consists of digital map files containing parcel-level cadastral information obtained from property descriptions. Cadastral features contained in the data set include real property boundary lines, rights-of-way boundaries, property dimensions.

This map shows the breakdown of parcel ownership in Weld County by surface owner. The parcels that are 40 acres and larger are labeled with the owner name, the parcels that are between 10 acres and 40 acres are labeled with a number that corresponds to the owner name index and everything smaller than 10 acres is too small to label.

Information about the ownership and control of land parcels of a given area including the cadastral system and patterns of land ownership and inheritance and tenure. Includes, for example, information about: the system by which land (as a commodity) is bounded, purchased, and sold. Patterns of ownership and control (for example: owner-occupied, absentee landlord, common property)

This map compares the residency of Jersey City property owners, i.e. Jersey City versus non-Jersey City.View this map here.

An area depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction. The same individual or organization may currently own many parcels that may or may not have been acquired through the same legal transaction. Therefore, they are captured as separate entities rather than merged together. This is in contrast to Basic Ownership, in which the surface ownership parcels having the same owner are merged together. Basic Ownership provides the general user with the Forest Service versus non-Forest Service view of land ownership within National Forest boundaries. Surface Ownership provides the land status user with a current snapshot of ownership within National Forest boundaries. Metadata

This ownership dataset utilizes a methodology that results in a federal ownership extent that matches the Federal Responsibility Areas (FRA) footprint from CAL FIRE's State Responsibility Areas for Fire Protection (SRA) data. FRA lands are snapped to county parcel data, thus federal ownership areas will also be snapped. Since SRA Fees were first implemented in 2011, CAL FIRE has devoted significant resources to improve the quality of SRA data. This includes comparing SRA data to data from other federal, state, and local agencies, an annual comparison to county assessor roll files, and a formal SRA review process that includes input from CAL FIRE Units. As a result, FRA lands provide a solid basis as the footprint for federal lands in California (except in the southeastern desert area). The methodology for federal lands involves:

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 provides title information (excluding ownership) where there is a relationship to one or more primary parcels.

A Record of Title is a record of a property's owners, legal description and the rights and responsibilities registered against the title.This dataset does not contain any ownership information so that it can be freely distributed. If ownership information is required, you need to apply for access.

There can be multiple parcels associated with a title, and a title may only have a part share in a parcel. This means the shape representing the title will be an aggregation of all parcels that the title is associated with. The ‘spatial extents shared’ attribute when equal to ‘false’ will indicate that title has exclusive interest over all of the shape (this will be case for the vast majority).

The originating data for parcel/title associations includes some non-official sources where the official data does not support a link. For more information see the LINZ website

APIs and web services This dataset is available via ArcGIS Online and ArcGIS REST services, as well as our standard APIs. LDS APIs and OGC web services ArcGIS Online map services

A Web Map displaying the property ownership boundaries within Natrona County, as well as the municipal boundaries, addresses, Improvement Service District boundaries, streets, roads, Township, Range, and Section Boundaries and zoning boundaries.

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, "HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous." Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. " HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas." These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality." Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

[Metadata] Description: Government Landownership in the State of Hawaii as of 2022: County, Federal, State, and State DHHL LandsSources: County of Kauai, April, 2022; City & County of Honolulu, April 27, 2022; County of Maui, April, 2022; County of Hawaii, April, 2022; State Department of Hawaiian Home Lands, October, 2022. This dataset was created using ownership information provided by the counties via tax map key parcel layers and ownership tables. Parcels were queried using the "Owner" field for state, county, and federal agency names. State GIS staff verified land ownership using the online service QPublic, the 2022 Department of Hawaiian Home Lands layer and other GIS layers and resources. Where ownership was still unclear, State GIS personnel reached out to appropriate agencies for clarification. Summary fields “majorowner” and “type” were created using additional filters, queries and analysis tools to summarize the data based upon government ownership sector and type. Also see detailed government ownership layer (gov_own_detailed) which is comprised of government land ownership by TMK parcel. The parcel boundaries are intended to provide a visual reference only and do not represent legal or survey level accuracy. Attributes are for assessment purposes only and are subject to change at any time. For additional information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/gov_own.pdf or contact the Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

[Metadata] Description: Detailed Government Landownership in the State of Hawaii as of 2022: County, Federal, State, and State DHHL Lands (by TMK parcel)

Statewide Property Inventory started in 1989 per legislation 11011.15, to begin a pro-active approach to managing the State’s Real Property assets in a computerized format. Having the information in an electronic format makes it available to top level decision-makers considering options for the best use of these assets. The Statewide Property Inventory is mandated to capture detailed information on the following: land owned and leased by the state, structures owned and leased by the state, property the state leases to the private sector. Statewide Property Inventory was established in 1988 by legislative mandate. Leases were added in 2004 by executive order. Data is updated annually by the agencies. Point of Contact: Any questions should be referred to the SPIWeb@dgs.ca.gov

This geospatial dataset depicts ownership patterns of forest land across Michigan, circa 2019. The data sources are listed below. The first seven sources of data supersede the final data source. The final data source is modeled from Forest Inventory and Analysis points from 2012-2017 and the most up-to-date publicly available boundaries of federal, state, and tribal lands.1.MI_State_Boundary_Census_Gov_2019.shp (State of MI boundary) clipped from cb_2019_us_state_500k from https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html2.NPS_Land_Resources_Division_MI.shp clipped from NPS_-_Land_Resources_Division_Boundary_and_Tract_Data_Service-shp taken from https://public-nps.opendata.arcgis.com/datasets/nps-land-resources-division-boundary-and-tract-data-service/data?layer=1Published December 12, 2019This service depicts National Park Service tract and boundary data that was created by the Land Resources Division. NPS Director's Order #25 states: "Land status maps will be prepared to identify the ownership of the lands within the authorized boundaries of the park unit. These maps, showing ownership and acreage, are the 'official record' of the acreage of Federal and non-federal lands within the park boundaries. While these maps are the official record of the lands and acreage within the unit's authorized boundaries, they are not of survey quality and not intended to be used for survey purposes." As such this data is intended for use as a tool for GIS analysis. It is in no way intended for engineering or legal purposes. The data accuracy is checked against best available sources which may be dated and vary by location. NPS assumes no liability for use of this data. The boundary polygons represent the current legislated boundary of a given NPS unit. NPS does not necessarily have full fee ownership or hold another interest (easement, right of way, etc...) in all parcels contained within this boundary. Equivalently NPS may own or have an interest in parcels outside the legislated boundary of a given unit. In order to obtain complete information about current NPS interests both inside and outside a unit’s legislated boundary tract level polygons are also created by NPS Land Resources Division and should be used in conjunction with this boundary data. To download this data directly from the NPS go to https://irma.nps.gov/App/Portal/Home Property ownership data is compiled from deeds, plats, surveys, and other source data. These are not engineering quality drawings and should be used for administrative purposes only. The National Park Service (NPS) shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data. Terms of UseProperty ownership data is compiled from deeds, plats, surveys, and other source data. These are not engineering quality drawings and should be used for administrative purposes only. The National Park Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data.3.Isle Royale.shp only Isle Royale clipped from MI_State_Boundary_Census_Gov_2019.shp4.FWSInterest_MI.shp (U.S. Fish and Wildlife Service) clipped from FWSInterest from FWSInterest_Apr2020.zipfrom https://www.fws.gov/gis/data/CadastralDB/index_cadastral.html (being moved on 6/26/2020)Use inttype1 = OThis data layer depicts lands and waters administered by the U.S. Fish and Wildlife Service (USFWS) in North America, U.S. Trust Territories and Possessions. It may also include inholdings that are not administered by USFWS. The primary source for this information is the USFWS Realty program.5.surfaceownership_MI.shp (U.S. National Forest Service) clipped from S_USA.SurfaceOwnership.gdb and downloaded fromhttps://data.fs.usda.gov/geodata/edw/datasets.phphttps://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=surfaceownershiprefreshed May 26, 2020Used NFSLandU_4 field and surfaceO_3 and surfaceO_3 to identify NFS parcelsAn area depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction. The same individual or organization may currently own many parcels that may or may not have been acquired through the same legal transaction. Therefore, they are captured as separate entities rather than merged together. This is in contrast to Basic Ownership, in which the surface ownership parcels having the same owner are merged together. Basic Ownership provides the general user with the Forest Service versus non-Forest Service view of land ownership within National Forest boundaries. Surface Ownership provides the land status user with a current snapshot of ownership within National Forest boundaries.6.MichiganDNR_02062020.shp (State of Michigan) from the State of MI delivered @ email on 5/14/2020Has State forests, State Wildlife areas, and State parks.7.The previous public ownership layers supersede this Sass et al. (2020) layer.In Sass et al. (2020), the nonforest areas are masked out.Identification_Information:Citation:Citation_Information:Originator: Sass, Emma M.Originator: Butler, Brett J.Originator: Markowski-Lindsay, Marla Publication_Date: 2020Title:Estimated distribution of forest ownership across the conterminous United States – geospatial datasetGeospatial_Data_Presentation_Form: raster digital dataPublication_Information:Publication_Place: Fort Collins, COPublisher: Forest Service Research Data ArchiveEight values of ownership type:1 = Family (Private): Owned by families, individuals, trusts, estates, family partnerships, and other unincorporated groups of individuals that own forest land. FIACode 45.2 = Corporate (Private): Owned by corporations. FIA Code 41.3 = TIMO/REIT (Private): Owned by Timber Investment Management Organizations or Real Estate Investment Trusts. Included in FIA Code 414 = Other Private (Private): Owned by conservation and natural resource organizations, unincorporated partnerships and associations. FIA Codes 42-43.5 = Federal (Public): Owned by the federal government. FIA Codes 11-13, 21-25.6 = State (Public): Owned by a state government. FIA Code 31.7 = Local (Public): Owned by a local government. FIA Code 32.8 = Tribal: Owned by Native American tribes. FIA Code 44.8.FIA inventory units developed by FIA, 2020

Minnesota's original public land survey plat maps were created between 1848 and 1907 during the first government land survey of the state by the U.S. Surveyor General's Office. This collection of more than 3,600 maps includes later General Land Office (GLO) and Bureau of Land Management maps up through 2001. Scanned images of the maps are available in several digital formats and most have been georeferenced.

The survey plat maps, and the accompanying survey field notes, serve as the fundamental legal records for real estate in Minnesota; all property titles and descriptions stem from them. They also are an essential resource for surveyors and provide a record of the state's physical geography prior to European settlement. Finally, they testify to many years of hard work by the surveying community, often under very challenging conditions.

The deteriorating physical condition of the older maps (drawn on paper, linen, and other similar materials) and the need to provide wider public access to the maps, made handling the original records increasingly impractical. To meet this challenge, the Office of the Secretary of State (SOS), the State Archives of the Minnesota Historical Society (MHS), the Minnesota Department of Transportation (MnDOT), MnGeo and the Minnesota Association of County Surveyors collaborated in a digitization project which produced high quality (800 dpi), 24-bit color images of the maps in standard TIFF, JPEG and PDF formats - nearly 1.5 terabytes of data. Funding was provided by MnDOT.

In 2010-11, most of the JPEG plat map images were georeferenced. The intent was to locate the plat images to coincide with statewide geographic data without appreciably altering (warping) the image. This increases the value of the images in mapping software where they can be used as a background layer.

Parcels and Land Ownership dataset current as of 2010. Parcel map was generated based on DeKalb County street centerline, ROW from DeKalb tax map, building structure and DeKalb tax map..

Government land ownership in the State of Hawaii: Federal, State, State Department of Hawaiian Home Lands (DHHL), and County. Source: City and County (C&C) of Honolulu (July 2013), Kauai County (January 2012), Maui County (July 2013), Hawaii County (June 2013). This dataset was created using the Large Landowners layer that was derived from the Tax Map Key (TMK) Parcel shapefiles from the counties of Honolulu, Kauai, Maui and Hawaii. Lands were selected for Type = "Public".

The Florida TIITF Land Records Spatial Index is a document based GIS layer to be displayed as a map comprised of polygons and attributes representing parcels described in deeds, leases, easements and other instruments archived in the Title Archives of the Division of State Lands, Department of Environmental Protection for the Florida Board of Trustees of the Internal Improvement Trust Fund (TIITF). The polygons represent parcels described in the archived TIITF land record documents; this is not a tax map or a representation of current ownership. The data includes acquisitions, dispositions and encumbrances. Selecting a parcel on the map may return information about several different documents associated with that parcel through out the history of State land transactions involving that parcel.

This dataset represents parcels not mapped or sourced in Vector Property Map. Please refer to the common ownership lots layer in https://opendata.dc.gov for the most current data on ownership. Property Owner Points. This dataset contains points that represent the approximate location of real property lots within the District of Columbia. Each property point is generated based on a corresponding record maintained within the Office of Tax and Revenue (OTR) Real Property Tax Administration's (RPTA) real property database. Each point contains the full attribution of database fields derived from ITS public release extract. The initial data conversion effort was begun in 1997 as a means to provide RPTA with a digital mapping system which could be maintained to reflect ongoing changes to property lots and ownership. The initial step was to scan RPTA tax square maps from aperture cards at an effective paper resolution of 400 DPI. The resulting images were then georeferenced to DC's 0.2-meter resolution 1995 digital orthophotos. During the georeferencing process, the images were not warped; they were simply scaled and rotated to best fit the orthophotos. The DC tax assessor provided a database of active tax accounts which were placed interactively by an operator using the georeferenced square image and the orthophoto. Centroids were placed on the primary structure visible in the orthophoto within the raster property polygon. The placement was performed within ArcView 3.2 using a customized data production application. Accounts which could not be placed in the first pass were then reviewed by another operator to attempt to find their correct location. The placed points were QC'd through a spatial overlay with the square index to assure a match between the square field value within the property database and the actual square polygon into which the point was placed. Spot checking was then performed to confirm that the centroids fell within the correct raster lot. The centroids were delivered to OTR as a single citywide AutoCAD DWG file. Attribute features with square, suffix, and lot numbers (SSLs) were included as an AutoCAD block.

Internal view of the parcel layer. This view contains all the attributes that can be seen by County employees.There are approximately 51,300 real property parcels in Napa County. Parcels delineate the approximate boundaries of property ownership as described in Napa County deeds, filed maps, and other source documents. GIS parcel boundaries are maintained by the Information Technology Services GIS team. Assessor Parcel Maps are created and maintained by the Assessor Division Mapping Section. Each parcel has an Assessor Parcel Number (APN) that is its unique identifier. The APN is the link to various Napa County databases containing information such as owner name, situs address, property value, land use, zoning, flood data, and other related information. Data for this map service is sourced from the Napa County Parcels dataset which is updated nightly with any recent changes made by the mapping team. There may at times be a delay between when a document is recorded and when the new parcel boundary configuration and corresponding information is available in the online GIS parcel viewer.From 1850 to early 1900s assessor staff wrote the name of the property owner and the property value on map pages. They began using larger maps, called “tank maps” because of the large steel cabinet they were kept in, organized by school district (before unification) on which names and values were written. In the 1920s, the assessor kept large books of maps by road district on which names were written. In the 1950s, most county assessors contracted with the State Board of Equalization for board staff to draw standardized 11x17 inch maps following the provisions of Assessor Handbook 215. Maps were originally drawn on linen. By the 1980’s Assessor maps were being drawn on mylar rather than linen. In the early 1990s Napa County transitioned from drawing on mylar to creating maps in AutoCAD. When GIS arrived in Napa County in the mid-1990s, the AutoCAD images were copied over into the GIS parcel layer. Sidwell, an independent consultant, was then contracted by the Assessor’s Office to convert these APN files into the current seamless ArcGIS parcel fabric for the entire County. Beginning with the 2024-2025 assessment roll, the maps are being drawn directly in the parcel fabric layer.Parcels in the GIS parcel fabric are drawn according to the legal description using coordinate geometry (COGO) drawing tools and various reference data such as Public Lands Survey section boundaries and road centerlines. The legal descriptions are not defined by the GIS parcel fabric. Any changes made in the GIS parcel fabric via official records, filed maps, and other source documents are uploaded overnight. There is always at least a 6-month delay between when a document is recorded and when the new parcel configuration and corresponding information is available in the online parcel viewer for search or download.Parcel boundary accuracy can vary significantly, with errors ranging from a few feet to several hundred feet. These distortions are caused by several factors such as: the map projection - the error derived when a spherical coordinate system model is projected into a planar coordinate system using the local projected coordinate system; and the ground to grid conversion - the distortion between ground survey measurements and the virtual grid measurements. The aim of the parcel fabric is to construct a visual interpretation that is adequate for basic geographic understanding. This digital data is intended for illustration and demonstration purposes only and is not considered a legal resource, nor legally authoritative.SFAP & CFAP DISCLAIMER: Per the California Code, RTC 606. some legal parcels may have been combined for assessment purposes (CFAP) or separated for assessment purposes (SFAP) into multiple parcels for a variety of tax assessment reasons. SFAP and CFAP parcels are assigned their own APN number and primarily result from a parcel being split by a tax rate area boundary, due to a recorded land use lease, or by request of the property owner. Assessor parcel (APN) maps reflect when parcels have been separated or combined for assessment purposes, and are one legal entity. The goal of the GIS parcel fabric data is to distinguish the SFAP and CFAP parcel configurations from the legal configurations, to convey the legal parcel configurations. This workflow is in progress. Please be advised that while we endeavor to restore SFAP and CFAP parcels back to their legal configurations in the primary parcel fabric layer, SFAP and CFAP parcels may be distributed throughout the dataset. Parcels that have been restored to their legal configurations, do not reflect the SFAP or CFAP parcel configurations that correspond to the current property tax delineations. We intend for parcel reports and parcel data to capture when a parcel has been separated or combined for assessment purposes, however in some cases, information may not be available in GIS for the SFAP/CFAP status of a parcel configuration shown. For help or questions regarding a parcel’s SFAP/CFAP status, or property survey data, please visit Napa County’s Surveying Services or Property Mapping Information. For more information you can visit our website: When a Parcel is Not a Parcel | Napa County, CA