The seamless, county-wide parcel layer was digitized from official Assessor Parcel (AP) Maps which were originally maintained on mylar sheets and/or maintained as individual Computer Aided Design (CAD) drawing files (e.g., DWG). The CRA office continues to maintain the official AP Maps in CAD drawings and Information Systems Department/Geographic Information Systems (ISD/GIS) staff apply updates from these maps to the seamless parcel base in the County’s Enterprise GIS. The seamless parcel layer is updated and published to the Internet on a monthly basis.The seamless parcel layer was developed from the source data using the general methodology outlined below. The mylar sheets were scanned and saved to standard image file format (e.g., TIFF). The individual scanned maps or CAD drawing files were imported into GIS software and geo-referenced to their corresponding real-world locations using high resolution orthophotography as control. The standard approach was to rescale and rotate the scanned drawing (or CAD file) to match the general location on the orthophotograph. Then, appropriate control points were selected to register and rectify features on the scanned map (or CAD drawing file) to the orthophotography. In the process, features in the scanned map (or CAD drawing file) were transformed to real-world coordinates, and line features were created using “heads-up digitizing” and stored in new GIS feature classes. Recommended industry best practices were followed to minimize root mean square (RMS) error in the transformation of the data, and to ensure the integrity of the overall pattern of each AP map relative to neighboring pages. Where available Coordinate Geometry (COGO) & survey data, tied to global positioning systems (GPS) coordinates, were also referenced and input to improve the fit and absolute location of each page. The vector lines were then assembled into a polygon features, with each polygon being assigned a unique identifier, the Assessor Parcel Number (APN). The APN field in the parcel table was joined to the corresponding APN field in the assessor property characteristics table extracted from the MPTS database to create the final parcel layer. The result is a seamless parcel land base, each parcel polygon coded with a unique APN, assembled from approximately 6,000 individual map page of varying scale and accuracy, but ensuring the correct topology of each feature within the whole (i.e., no gaps or overlaps). The accuracy and quality of the parcels varies depending on the source. See the fields RANK and DESCRIPTION fields below for information on the fit assessment for each source page. These data should be used only for general reference and planning purposes. It is important to note that while these data were generated from authoritative public records, and checked for quality assurance, they do not provide survey-quality spatial accuracy and should NOT be used to interpret the true location of individual property boundary lines. Please contact the Sonoma County CRA and/or a licensed land surveyor before making a business decision that involves official boundary descriptions.

The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. This dataset is also available as a layer package and a file geodatabase.The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) 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 lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels.The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary.The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

CDFW BIOS GIS Dataset, Contact: Allison Schichtel, Description: The Sonoma County fine scale vegetation and habitat map is an 83-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller.

Building outlines for permanent visible structures in Sonoma County. Original building outline capture for Sonoma County included only those areas in Sonoma County where the 2000 orthophotography resolution was one foot per pixel or less. In addition, building outlines were not captured for the cities of Sonoma and Healdsburg, but were added at a later date. For areas where the orthophotography resolution was greater than one foot per pixel, a point was captured representing the location of the structure. The point was subsequently converted into a 36 sf polygon. These polygons were then added to the Building Outline layer. As a result, some areas of the county may not have representative building outlines. As new orthophotography becomes available, new building outlines are added. This layer is a work in progress and the County of Sonoma does not guarantee 100% coverage for representative building outlines.

© County of Sonoma GIS Central This layer is sourced from gis-webpub.sonoma-county.org.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads shapefile includes all features within the MTS Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in the MTS that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Intended as a repository for all publicly owned land in Sonoma County, this would include but is not limited to county, state, local, and federal property which may or may not include parks, landfills, corporate yards, business and services, hospital property. This layer is not intended to make any determination regarding public access. This layer was compiled from two primary sources; the County seamless parcel base and the Merrick 2000 orthophotography. This layer was originally created to support the 1989 version of the Open Space Element of the General Plan.

A high resolution LiDAR derived hillshade facilitates the visualization of the topography of a landscape at a variety of scales. This hillshade which was created from a LiDAR derived hydro-flattened bare earth digital elevation model shows the signal returns without any vegetation or human-made structures. In addition to that, bodies of water have been smoothed. This layer may be used on its own or in conjunction with other data.The Sonoma County Vegetation Mapping and LiDAR Program. and the University of Maryland (under grant NNX13AP69G from NASA’s Carbon Monitoring System, Dr. Ralph Dubayah, PI) contracted LiDAR and orthophoto data collection for all of Sonoma County in late 2013. Also included in the data collection were two areas in Mendocino County - the Soda Spring Creek-Dry Creek Watershed and Lake Mendocino. This fine scale data will help provide an accurate, up-to-date inventory of the county’s landscape features, ecological communities and habitats. Project funders include: NASA, the University of Maryland, the Sonoma County Agricultural Preservation and Open Space District, the Sonoma County Water Agency, the California Department of Fish and Wildlife, the United States Geological Survey, the Sonoma County Information Systems Department, the Sonoma County Transportation and Public Works Department, the Nature Conservancy, and the City of Petaluma.The hillshade is a greyscale image showing topography in the landscape. In this case it is created from a LiDAR derived hydro-flattened bare earth digital elevation model illuminated by hypothetical light source shining from the north west. A hydro flattened bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. In addition bodies of waters 2acres or larger have been smoothed.The DEM used to create this hillshade is described as a bare earth digital elevation model (DEM) representing the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from LiDAR data using triangulated irregular network (TIN) processing of the ground point returns. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average elevation for that area.

You are free to: Share - copy and redistribute the data in any medium or format. Adapt - You may make derivative works, transform, and build upon the data for any purpose, even commercial. The licensor cannot revoke these freedoms as long as you follow the license terms.License terms: Attribution - You must give appropriate credit (if supplied, you must provide the name of the creator and attribution parties, a copyright notice, a license notice, a disclaimer notice and a link to the material) and indicate if any changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you, your organization, or your use of the data. ShareAlike - if you modify, transform, or build on the data, you must distribute your contributions under the same license as the original.No additional Restrictions - You may not apply legal terms or technological measures that legally restrict others form doing anything the license permits.Notices: You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the data.EXCEPT TO THE EXTENT REQUIRED BY APPLICABLE LAW, IN NO EVENT WILL LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY FOR ANY SPECIAL, INCIDENTAL, CONSEQUENTIAL, PUNITIVE OR EXEMPLARY DAMAGES ARISING OUT OF THIS LICENSE OR THE USE OF THE DATA, EVEN IF LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.The above is an easily understandable summary of and not a substitute for the license and disclaimer for the Attribution-ShareAlike 3.0 United States (CC BY-SA 3.0 US) the full text is available at creativecommons.org.https://creativecommons.org/licenses/by-sa/3.0/us/legalcode

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, stairways, and winter trails.

ORDINANCE NO. 6364AN ORDINANCE OF THE BOARD OF SUPERVISORS OF THE COUNTY OF SONOMA, STATE OF CALIFORNIA, ADOPTING REVISED SUPERVISORIAL DISTRICT BOUNDARIES FOR ALL OF THE SUPERVISORIAL DISTRICTS OF THE COUNTY, REPEALING SONOMA COUNTY CODE SECTION 1-8, AND DIRECTING COUNTY STAFF TO MAINTAIN FOR AT LEAST TEN YEARS THE COUNTY'S REDISTRICTING WEBSITE TO CONTINUE TO INFORM THE PUBLIC ABOUT THE REDISTRICTING PROCESS AND THE REVISED BOUNDARIES.The Board of Supervisors of the County of Sonoma, State of California, ordains as follows:Section I. Public Participation. The Sonoma County Board of Supervisors has taken steps above and beyond the requirements of Elections Code Section 21508 to engage the community and invite public participation in the supervisorial boundary redistricting process. The Board has encouraged residents, including those in underrepresented communities and non-English speaking communities, to participate in the redistricting public review process. These steps have included all of the following:Provided information to media organizations that provide county news coverage, including media organizations that serve language minority communities.Provided information through good government, civil rights, civic engagement, and community groups or organizations that are active in the county, including those active in language minority communities, and those that have requested to be notified concerning county redistricting.Arranged for live translation in Spanish at redistricting public hearings and workshops.The County retained a public outreach and local engagement consultant who performed 34 Community Engagement Opportunities (including 13 focus group sessions; 16 group or radio presentations; 3 Town Halls; 2 map drawing parties).On February 23, 2021, the Board established the Sonoma County Advisory Redistricting Commission (ARC) to advise and assist the Board with redrawing supervisorial district boundaries. The ARC had 19 members, comprised of two appointees per district and nine at-large members.On June 28, 2021, the ARC held its first public meeting to learn about redistricting and listen to public comment.On July 26, 2021, the ARC held another public meeting to continue to discuss the redistricting process and listen to public input.On August 23, 2021, the ARC held a public hearing to discuss redistricting, receive public input about communities of interest, and learn about mapping tools.On September l, 2021, the ARC held a meeting to consider the redistricting process, receive map-drawing training and listen to public feedback.On September 13, 2021, the ARC held a meeting to discuss equity.On September 15, 2021, the County held a Town Hall meeting to review the redistricting process and how the public can provide input.On October 5, 2021, the Sonoma County Board of Supervisors held a public hearing to review the new census data and discuss the redistricting process.On October 18, 2021, the ARC held a duly noticed public meeting to consider draft supervisorial district maps.On October 18, 2021, the ARC held a duly noticed public meeting to consider draft supervisorial district maps.On October 22, 2021, the ARC held a duly noticed public meeting to discuss the draft maps and listen to public feedback.On October 25, 2021, the ARC held a duly noticed public meeting to discuss the draft maps, listen to public feedback and vote on a proposed supervisorial district map to present to the Board of Supervisors. The ARC recommended the Board continue to listen to public feedback and update the map to respond to continued community input and comply with federal and state laws.On November 2, 2021, the Board held a public hearing to consider the ARC's proposed map and recommendations.On November 16, 2021, the Board held a public hearing to consider proposed maps and continue to listen to public feedback.On November 22, 2021, County staff held a Town Hall meeting focused on the City of Rohnert Park's comments and to gather public input;On November 29, 2021, the Board held a public workshop to consider a proposed map and continue to listen to public input.On December 7, 2()21, the Board held a final public hearing to introduce, waive reading and consider adoption of an ordinance to adopt a new supervisorial district map.Section Il. Information Gathered. The Board has considered the 2020 federal census data, the ARC's recommendations, in addition to all of the other community input through the ARC process, as well as the Board's own public hearings, the public workshop and additional public comments. Additionally, the Board also retained a demographer, National Demographics Corporation, to analyze the population and demographic data. Since the release of the 2020 federal census data, the ARC and the Board have considered numerous variations of the supervisorial district boundaries to ensure the final version of the map satisfies the criteria of federal and state law. Based on that information and community input, the Board has developed the final revised County of Sonoma supervisorial district boundaries as specified and set forth in the map attached to this ordinance as Attachment A ("Revised Sonoma County Supervisorial District Boundaries").Section Ill. Findings. Based on the information gathered as set forth above, the Board makes the following findings:The Revised Sonoma County Supervisorial District Boundaries are based on the total population of residents of the county as determined by the 2020 federal decennial census;The Revised Sonoma County Supervisorial District Boundaries comply with the United States Constitution, the California Constitution, and the federal Voting Rights Act of 1965 (52 U.S.C. Section 10301 et seq.);The Revised Sonoma County Supervisorial District Boundaries comply with California Elections Code Section 21500 because those boundaries have been developed in accordance with these criteria as set forth in the following order of priority:To the extent practicable, the supervisorial districts are geographically contiguous;To the extent practicable, the geographic integrity of local neighborhoods and local communities of interest are respected in a manner that minimizes their division;To the extent practicable, the geographic integrity of a city or census designated place is respected in a manner that minimizes its division;The Revised Supervisorial District Boundaries are easily identifiable and understandable by residents and to the extent practicable are bounded by natural and artificial barriers, by streets, or by the boundaries of the county;To the extent practicable, and where it does not conflict with the preceding criteria above, the Revised Supervisorial District Boundaries are geographically compact; andThe Revised Supervisorial District Boundaries have not been developed for the purpose of favoring or discriminating against a political party.Communities of Interest. Based on public comment received during the Public Participation process set forth in Section I above, the Board has determined that the following are communities of interest as defined in Elections Code Section 21500(c)(2) because these are populations that share common social or economic interests that should be included within a single supervisorial district for purposes of effective and fair representation:Roseland has recently been annexed to the City of Santa Rosa and shares socioeconomic characteristics with Moorland; both areas represent a community of interest that should be included within a single supervisorial district that includes portions of the downtown area of Santa Rosa for purposes of effective and fair representation;Coastal communities share common interests and should remain within one supervisorial district for the purposes of effective and fair representation;Russian River communities share common social and economic interests and should remain within one supervisorial district for purposes of effective and fair representation;Coffey Park-Larkfield-Mark West-Wikiup community shares common interests and should remain within one supervisorial district for purposes of effective and fair representation;The Springs area (Eldridge, Fetters Hot Springs, Agua Caliente, Boyes Hot Springs) share common interests and should remain within one supervisorial district for purposes of effective and fair representation; andThe community within the Bennett Valley Area Plan, approved by the Sonoma County Board of Supervisors in Resolution No. 11-0461, on September 30, 2011, share common interests and should remain within one supervisorial district for purposes of effective and fair representation.Section IV. Adoption Procedures. California Elections Code Section 21500(e) allows the County to adopt supervisorial district boundaries by resolution or ordinance and clarifies that revised supervisorial district boundary adoption occurs on the date of passage of such ordinance or resolution. The Revised Sonoma County Supervisorial District Boundaries attached hereto as Attachment A have been posted on the County'sRedistricting website at https://sonomacounty.ca.gov/CAO/Policy-Grants-and-SpeciaIProjects/2021-Redistricting/for at least seven days prior to final adoption in compliance with Elections Code SectionSection V. Adoption of Revised Sonoma County Supervisorial District Boundaries. Based on the above findings and adoption procedures, the Board hereby determines that the Revised Sonoma County Supervisorial District Boundaries comply with all federal and state laws. Accordingly, the Board hereby adopts the Revised Sonoma County Supervisorial District Boundaries.Section VI. Posting on County's Redistricting Website. In compliance with Elections Code Section 21508(g), the Board directs County staff to maintain the County of Sonoma's Redistricting website at https://sonomacounty.ca.gov/CAO/Policy-Grants-and-Special-Projects/2021-Redistrictingfor at least 10 years after the adoption of new supervisorial district

This is a vector tile service of the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. It is mean to be used in conjunction with the vector tile service that provides labels for each polygon. There is an additional vector tile service that provides solid colored polygons for the vegetation map if hollow outlines are not desired. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8). The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) 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 lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

Map of Sonoma County Public and Private Schools.

The impetus for this project was to finally separate out parks and parkland accessible by the public from the public lands layer which contained both parkland and non parkland data. It was determined that a pure public lands layer was not the ideal repository for parks and parkland due to park boundaries not always following parcel lines, and in some cases ownership of the parcel(s) not being held by the public. Each municipal agency was queried for a list of their parks and park land, which was then, cross referenced with the ownership of the named area by referencing the owner of the parcel or parcels in the designated area. In some cases the municipality in question is not the owner of the parcel in question; rather it has an easement over the property which provides for the municipality to manage the property as a park for the public. In other cases it was clear that the entire parcel(s) was not dedicated for use as a park either by design or by other land uses on the property, in those cases the park shape deviates from the parcel shape.

© County of Sonoma GIS

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

The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International. Automatic aerial triangulation (AAT) was performed. The triangulated frames were rectified to a LiDAR derived DEM. Mosaicking was performed using an automatic seaming algorithm and manually edited to eliminate seams through elevated features where possible. Flight Dates 02-20-2021 through 05-25-2021.

This .tif file represents the intensity values of the Green Lidar laser returns from the Sonoma Creek Topo bathymetric dataset. The horizontal datum for this dataset is NAD83 (2011), the vertical datum is NAVD88, Geoid 18, and the data is projected in California State Plane, Zone 2. Units are in US Survey Feet. NV5 Geospatial collected the Sonoma Creek Topo bathymetric Lidar data for Sonoma Water between 02/04/2021 and 02/06/2021.

© County of Sonoma GIS Central

URL from idinfo/citation in CSDGM metadata.

Last Publication Date: April 01, 2020

This dataset was originally created to fill in gaps or other address range difficulties for the County of Sonoma dispatching system and to identify specific or special addresses that may or may not be difficult to find on the ground in the field. By no means complete, it is now the intent of this feature class to record all known addresses in Sonoma County. Most points will be directly on top of a structure. In those instances where a point is not on top of a structure it may be for one or more of the following reasons; multiple structures make positive identification of a specific address unclear, tree cover makes it difficult to identify structures, there is no structure associated with an address, or at the request of CAD 911 dispatchers for various reasons.

The Permit Sonoma GIS Parcel dataset represents unincorporated lands & incorporated (city) lands within Sonoma County.