This layer represents parcel polygon geometries from the Assessor's Office Parcel Fabric. This layer is updated whenever edits in the tax_parcels and non_taxables layers of the Parcel Fabric are edited. Please sort the Last_Modified field descending for the latest update.

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

This shapefile contains tax rate area (TRA) boundaries in Monterey County for the specified assessment roll year. Boundary alignment is based on the 2023 county parcel map. A tax rate area (TRA) is a geographic area within the jurisdiction of a unique combination of cities, schools, and revenue districts that utilize the regular city or county assessment roll, per Government Code 54900. Each TRA is assigned a six-digit numeric identifier, referred to as a TRA number. TRA = tax rate area number

This shapefile contains tax rate area (TRA) boundaries in Monterey County for the specified assessment roll year. Boundary alignment is based on the 2009 county parcel map. A tax rate area (TRA) is a geographic area within the jurisdiction of a unique combination of cities, schools, and revenue districts that utilize the regular city or county assessment roll, per Government Code 54900. Each TRA is assigned a six-digit numeric identifier, referred to as a TRA number. TRA = tax rate area number

This part of SIM 3254 presents data for the bathymetry and shaded-relief maps (see sheets 1, 2, SIM 3254) of the Offshore of Ventura map area, California. The raster data file for the bathymetry map is included in "Bathymetry_OffshoreVentura.zip." The raster data file for the shaded-relief map is included in "BathymetryHS_OffshoreVentura.zip." Both are accessible from http://pubs.usgs.gov/ds/781/OffshoreVentura/data_catalog_OffshoreVentura.html. The bathymetry and shaded-relief maps of the Offshore of Ventura map area, California, were generated from bathymetry data collected by California State University, Monterey Bay, Seafloor Mapping Lab (CSUMB) and by the U.S. Geological Survey (USGS). Most of the offshore area was mapped by CSUMB in the summers of 2006 and 2007, using a 244-kHz Reson 8101 multibeam echosounder. The seafloor west of Ventura Harbor was mapped by the USGS in 2006 and 2010, using 117-kHz (2006) and 234.5-kHz (2010) SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonars. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

Dataset Summary:

The Monterey, San Benito and San Luis Obispo County Impervious Surfaces map is a 5-class fine-scale polygon vector representation of artificial impervious surfaces in the region. There are 1,253,729 features in the dataset. Non-impervious areas are not mapped and are not covered by polygons. The impervious map represents the state of the landscape in summer, 2022. This technical mapping work for this product was conducted by the impervious mapping team at the University of Vermont Spatial Analysis Lab and EarthDefine. Table 1 lists download locations for the dataset.Table 1. Monterey, San Benito, and San Luis Obispo Counties counties impervious surfaces data

Description

Link

File Geodatabase Feature Class

https://vegmap.press/central_coast_impervious_fgdb

Vector Tile Service

https://vegmap.press/central_coast_impervious_vt

Detailed Dataset Description: The impervious map was created using a combination of AI techniques and “expert systems” rulesets developed in Trimble eCognition. Initial impervious polygons for populated areas were produced using AI techniques by EarthDefine. These were refined and classified into impervious types by the UVM Spatial Analysis Lab using eCognition. Impervious surfaces in less populated areas were produced entirely by the UVM Spatial Analysis Lab in eCognition. ECognition rulesets combine automated image segmentation with-object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for impervious mapping included: high resolution (0.6 meter) 4-band NAIP imagery (2022), the unified lidar point cloud, which is an amalgamation of the most recent/best available lidar data for the three-county area, and lidar derived rasters from the unified point cloud such as the canopy height model (CHM) and normalized digital surface model (nDSM).

After production in AI and eCognition, the preliminary impervious map product was manually edited by a team of UVM’s photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results.

The impervious map has 5 classes representing different types of impervious features, which are described below:

·
Building – Structures including homes, commercial buildings, outbuildings, and other human-made structures such as water tanks and silage silos. Structures fully occluded by vegetation will not be mapped.

·
Paved Road – Roads that are paved and wide enough for a vehicle.

· Dirt/Gravel Road – Dirt or gravel roads wide enough for a vehicle. Non-ephemeral fire roads, ranch roads and long driveways. Polygons representing narrow unpaved (single track) trails are not included in this data product.

· Other Dirt/Gravel Surface – Dirt or gravel surfaces that are highly compacted and used by humans and equipment, such as parking lots, road pull-offs, some dirt or gravel paths, and highly compacted areas around commercial activities. This class DOES NOT include natural turf playing fields, very lightly used dirt roads, livestock areas, naturally occurring bare soil or rock, or bare areas around ponds.

· Other Paved Surface – Includes parking lots, sidewalks, paved walking paths, swimming pools, tennis courts.

Miscellaneous quality control and processing notes:·
Zoom level used during manual quality control was no finer than 1 to 500.·
Vector data was created with no overlapping polygons.

Data Limitations: This is not a planimetric data product and was created using semi-automated techniques. It provides a reasonable and useful depiction of impervious surfaces for planners and managers but does not have the accuracy or precision to support engineering applications. No formal accuracy assessment was conducted for this dataset. Users should apply caution when using the data for applications requiring high positional or classification accuracy. Appropriate uses of the data product include:· As an input to storm water models· For planners to assess % imperviousness in a parcel/watershed· To help identify areas of human infrastructure for fuels and fire management· As an input to fuel models that are used in fire behavior and fire spread models· For cartography and mapping· Generally for use at scales 1:1,000 and smaller

Inappropriate uses of this product include:· Measuring exact square footage of structures or impervious features for building projects· Using the impervious polygons as geographically precise information for transportation and public works engineering projects· Determining ownership or maintenance responsibility of a particular feature, such as a paved or dirt road· Identifying publicly accessible areas for recreation or other uses· Confirming the suitability of a surface for any use including driving, hiking, bicycling, etc.

Common errors in this dataset are inter-class confusion and errors of commission to impervious. These are discussed in more detail below:

·
Inter-Class Confusion: The accuracy of the map for impervious versus pervious is very high (although no quantitative assessment of accuracy was funded for this product). However, the accuracy for individual impervious classes will be much lower. For example, confusion exists between the ‘Other Paved’ and ‘Other Dirt/Gravel Surface,’ classes, even though these are both mapped correctly as impervious surfaces.

· Errors of Commission: The most widespread error in this map are areas mapped as impervious that are actual pervious surfaces of dried out herbaceous land cover. Some dried-out herbaceous cover may be mistakenly classified as impervious due to spectral similarity. Manual editing minimized but did not completely eliminate these errors.