Vector polygon map data of property parcels from Shasta County, California containing 101,781 features.

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Property parcel GIS map data consists of detailed information about individual land parcels, including their boundaries, ownership details, and geographic coordinates.

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Property parcel data can be used to analyze and visualize land-related information for purposes such as real estate assessment, urban planning, or environmental management.

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Available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

Shasta County Snowload layer

Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

A parcel feature service for viewing land information in the City of Shasta Lake, CA. It is a representation of the taxable parcels maintained by the Shasta County Assessor Office.

The Unpublished Digital Geologic-GIS Map of the West Shasta Copper-Zinc District, California is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (wscz_geology.gdb), a 10.1 ArcMap (.mxd) map document (wscz_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (whis_geology_gis_readme.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (whis_geology_gis_readme.pdf). Please read the whis_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wscz_geology_metadata.txt or wscz_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 10N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Whiskeytown National Recreation Area.

Geospatial data about Shasta County, California Water Lines. Export to CAD, GIS, PDF, CSV and access via API.

Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

Geospatial data about Shasta County, California Roads. Export to CAD, GIS, PDF, CSV and access via API.

Communities are areas within the county that are generally associated with populated locations, unincorporated towns, or incorporated cities within the Sahsta County. They are generally aligned to parcel lines, but do not have any legal definition. The

This shapefile contains tax rate area (TRA) boundaries in Shasta 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 shapefile contains tax rate area (TRA) boundaries in Shasta 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

CDFW BIOS GIS Dataset, Contact: Erin Zulliger, Description: Migration corridor, stopover, and winter range locations for Rocky Mountain elk (Cervus canadensis nelsoni) from the East Shasta Valley herd, Siskiyou County, California, and Klamath County, Oregon. Corridors, stopovers, and winter ranges were developed in Migration Mapper with Brownian Bridge Movement Models using GPS locations from collared elk. Migration corridors represent movement routes used by elk between winter and summer range habitats.

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2005 Shasta County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). DPLA was later reorganized into the Division of Statewide Integrated Water Management and the Division of Integrated Regional Water Management. The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters and Northern Region, under the supervision of Tito Cervantes. The finalized countywide land use vector data is in a single, polygon, shapefile format. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Shasta County conducted by DWR, Northern District Office staff(ND), currently known as Northern Region Office, under the leadership of Tito Cervantes, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2005. ND staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary date was developed using: 1. Linework developed for DWR’s 1995 survey of Shasta County was used as the starting point for the digital field boundaries developed for this survey. Where needed, Northern Region staff made corrections to the field boundaries using the 1993 Digital Orthophoto Quarter Quadrangle (DOQQ) images. After field visits had been completed, 2005 National Agricultural Imagery Program (NAIP), one-meter resolution imagery from the U.S. Department of Agriculture’s Farm Services Agency was used to locate boundary changes that had occurred since the 1993 imagery was taken. Field boundaries for this survey follow the actual borders of fields, not road center lines. Line work for the Redding area was downloaded from the City of Redding website and modified to be compatible with DWR land use categories and linework. 2. For field data collection, digital images and land use boundaries were copied onto laptop computers. The staff took these laptops into the field and virtually all agricultural fields were visited to positively identify agricultural land uses. Site visits occurred from July through September 2005. Using a standardized process, land use codes were digitized directly into the laptop computers using ArcMap. For most areas of urban land use, attributes were based upon aerial photo interpretation rather than fieldwork. 3. The digital land use map was reviewed using the 2005 NAIP four-band imagery and 2005 Landsat 5 images to identify fields that may have been misidentified. The survey data was also reviewed by summarizing land use categories and checking the results for unusual attributes or acreages. 4. After quality control procedures were completed, the data was finalized by staff in both ND and Sacramento's DPLA. Important Points about Using this Data Set: 1. The land use boundaries were drawn on-screen using orthorectified imagery. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and whatever additional information the aerial photography might provide. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. Double cropping and mixed land use must be taken into account when calculating the acreage of each crop or other land use mapped in this survey. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. For double cropped fields, a “D” will be entered in the “MULTIUSE” field of the DBF file of the shapefile. To calculate the crop acreage for that field, 40 acres should be allocated to the grain category and then 40 acres should also be allocated to corn. For polygons mapped as “mixed land use”, an “M” will be entered in the “MULTIUSE” field. To calculate the appropriate acreages for each land use within this polygon, multiply the percent (as a decimal fraction) associated with each land use by the acres represented by the polygon. 4. All Land Use Codes are respresentative of the current 2016 Legend unless otherwise noted. Not all land use codes will be represented in the survey. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 9' x 9' color photos, is approximately 23 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

The 1995 Shasta County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Northern District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and Northern District Important Points about Using this Data Set: 1. The land use boundaries were hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Irrigation method information was not collected for this survey. 5. Not all land use codes will be represented in the survey.

Locations of building footprints in and around the City of Redding. Building footprints were collected by digitizing the roof line of structures using high resolution aerial imagery. Building footprints are typically updated on an as need basis. City of Redding has recently included building footprints in Shasta County which were created by county GIS staff. Spatial coordinate system is California State Plane, zone I Fipszone 0401, NAD83

Production parcel layer with Situs Addresses from Assessor tables. Published weekly as an addirional parcel resource. Source parcels, maintained by Shasta County Assessor, only have the Assessor Parcel Number, this dataset includs a physical, situs, addre

Shasta County Voting Precincts

Bridges feature class was created by converting an existing KML file of bridge locations maintained by Shasta County Public Works, then adding and populating fields maintained in Public Work's Bridge Inspection Spreadsheet. These additional fields were added to simplify joining the feature class to the spreadsheet, and symbolizing on common attributes.

Assessor Books are collections of Assessor Pages, that are used by Assessor as a reference for tax parcels and other contextual information. In Shasta County, typical tax parcels have a 15 digit parcel number. The first three digits reference the Asses

Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the Water Year 2018, covering over 9.4 million acres of Irrigable agriculture on a field scale and additional areas of urban extent. The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014 and 2016 land use mapping, which classified over 14 million acres of land into Irrigable agriculture and urban area. Unlike the 2014 and 2016 datasets, the Water Year 2018 dataset includes multi-cropping and incorporates ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing true Irrigable area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, young perennials and wetland. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.5% using the Land IQ legend and 98.3% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. The value ‘n’ in the ‘DWR_REVISE’ data field indicates a Regional Office added a boundary and attributes where none was included in the Land IQ data set. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated), - DWR determined that the crop was not irrigated. - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). DWR added Adjusted Day Of Year (ADOY) for peak NDVI date corresponding to CROPTYP category. The date received by Land IQ was delivered in a Julian date format (YYYYDDD) and was converted into the ADOY by DWR for statistical purposes. Land use boundaries delineated by Land IQ were not revised by DWR.