The county parcel layer was derived over many years from the cadastral parcel maps prepared by the Assessor's Office. The complete and contiguous parcel fabric was then registered and rectified to the best available Public Land Survey (PLS) available. This PLS layer has many inherent inaccuracies related to the original surveys completed in the late 19th and early 20th centuries. As more accurate ground controls become available, the parcel layer will be adjusted. The parcel layer is currently updated roughly twice per year, based on changes to the cadastral maps by the assessor’s drafting function. This can result in delays of several months before updates due to land divisions, mergers, and boundary line adjustment are reflected in the parcel data sets used by these map pages.

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 Tuolumne 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

Fire Protection District Boundaries within Tuolumne County, California, including City of Sonora,Twain Harte and Groveland Community Services Districts, and Lake Don Pedro Community Services District. Fire Protection Districts within Tuolumne County include Columbia, Jamestown, Strawberry, Tuolumne, and Sugar Pine Mi-Wuk. Note - Twain Harte and Groveland provides fire protection services. Lake Don Pedro CSD does not provide dedicated fire services, but this area is served by Tuolumne County and CalFire. Not included here is the Tuolumne Band of Me Wuk Indians, who provides fire protection services within their trust lands. This layer shows district boundaries, however many of these districts maintain mutual aid agreements with neighboring fire protection agencies. All areas not included here fall within the jurisdiction of Tuolumne County Fire, CalFire, or U.S. Forest Service. Contact the individual district or Tuolumne County Fire directly for more information.https://www.tuolumnecounty.ca.gov/717/Fire-Department

School locations in Tuolumne County, California

Snow Load building requirements for Tuolumne County. Determined by elevation ranges.

This project was initiated by the CDFW Central Region and was conducted on a portion of the Tuolumne herd that migrate to the Jawbone Ridge flats in the winter in Tuolumne County, Mariposa County, and Alpine County. Jawbone Ridge and the adjacent winter range habitat was further divided into the Clavey and Cherry sub-herd units. Additionally, a small sample of deer were captured from the Yosemite herd (south of the Tuolumne herd) to determine herd overlap. The raw dataset consisted of GPS way points collected from Advanced Telemetry Solutions (ATS) store on board GPS collars (G2110B/D model) and were placed on female mule deer only. Individuals were captured via darting or clover traps. This data was collected from 2009-2015 by Nathan Graveline and Ronald Anderson. GPS collars were set to take a location every 7 hours, and emit a signal Monday through Friday, 9am to 5pm. Some GPS collars were set to take a location fix every hour during periods of time when deer were thought to be migrating (May and November). The Clavey and Cherry sub-herd units support the highest concentration of wintering deer within the Tuolumne deer herd range. The majority of deer in these two sub-herds migrate east into the Emigrant and Yosemite Wilderness, with a few heading north to the Carson-Iceberg Wilderness. Low density populations of non-migratory deer are present in the winter range. Forest practices, wildfires, and recreation (hunting, camping, OHV) represent the most significant impacts to this herd. To improve the quality of the data set as per Bjørneraas et al. (2010), theGPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors in a single deer population. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 83 deer, including location, date, time, and average location error as inputs in Migration Mapper. 245 migration sequences were used in the modeling analysis. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to varying fix rates, separate models using Brownian bridge movement models (BMMM) and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (25% of sequences selected with BMMM). Migration corridors, stopovers, and winter range analyses were produced separately for the Yosemite Herd sample (n = 6) and merged with the Tuolumne dataset given the smaller capture effort and intention to prioritize moderate and high use corridors specifically in the Tuolumne herd. Winter range analyses were based on data from 85 individual deer in total. A separate BBMM was created for all deer locations designated as winter range using a fixed motion variance parameter of 1000. Winter range designations for this herd would likely expand with a larger sample south of Jawbone Ridge (Yosemite Herd) due to a small capture sample size from this area, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell in the BBMMs, with greater than or equal to 1 deer, greater than or equal to 9 deer (10% of the sample), and greater than or equal to 17 deer (20% of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.