DOUGLAS COUNTY SURVEY/GISGIS PARCEL MAPPING GUIDELINES FOR PARCEL DISCREPANCIESIt is the intent of the Douglas County GIS Parcel Mapping to accurately identify the areas of land parcels to be valued and taxed 1. Discrepancies in areas• The Auditor/Assessor (tax) acreage areas started with the original US General Land Office (GLO) township plat maps created from the Public Land Survey (PLS) that was done between 1858 and 1871. The recovery of the PLS corners and the accurate location of these corners with GPS obtained coordinates has allowed for accurate section subdivisions, which results in accurate areas for parcels based on legal descriptions, which may be significantly different than the original areas. (See Example 2)• Any parcel bordering a meandered lake and/or a water boundary will likely have a disparity of area between the Auditor/Assessor acreages and the GIS acreages because of the inaccuracy of the original GLO meander lines from which the original areas were determined. Water lines are not able to be drafted to the same accuracy as the normal parcel lines. The water lines are usually just sketched on a survey and their dimensions are not generally given on a land record. The water boundaries of our GIS parcels are located from aerial photography. This is a subjective determination based on the interpretation by the Survey/GIS technician of what is water. Some lakes fluctuate significantly and the areas of all parcels bordering water are subject to constant change. In these cases the ordinary high water line (OHW) is attempted to be identified. Use of 2-foot contours will be made, if available. (See Example 1)• Some land records do not accurately report the area described in the land description and the description area is ignored. (See Example 3)• The parcel mapping has made every attempt to map the parcels based on available survey information as surveyed and located on the ground. This may conflict with some record legal descriptions.Solutions• If an actual survey by a licensed Land Surveyor is available, it will be utilized for the tax acreage.• If the Auditor/Assessor finds a discrepancy between the tax and GIS areas, they will request a review by the County Survey/GIS department.• As a starting guideline, the County Survey/GIS department will identify all parcels that differ in tax area versus GIS parcel area of 10 % or more and a difference of at least 5 acres. (This could be expanded later after the initial review.)• Each of these identified parcels will be reviewed individually by the County Survey/GIS department to determine the reason for the discrepancy and a recommendation will be made by the County Survey/GIS department to the Auditor/Assessor if the change should be made or not.• If a change is to be made to the tax area, a letter will be sent to the taxpayer informing them that their area will be changed during the next tax cycle, which could affect their property valuation. This letter will originate from the Auditor/Assessor with explanation from the County Survey/GIS department. 2. Gaps and Overlaps• Land descriptions for adjoining parcels sometimes overlap or leave a gap between them.o In these instances the Survey/GIS technician has to make a decision where to place this boundary. A number of circumstances are reviewed to facilitate this decision as these dilemmas are usually decided on a case by case basis. All effort will be made to not leave a gap, but sometimes this is not possible and the gap will be shown with “unknown” ownership. (Note: The County does not have the authority to change boundaries!)o Some of the circumstances reviewed are: Which parcel had the initial legal description? Does the physical occupation of the parcel line as shown on the air photo more closely fit one of the described parcels? Interpretation of the intent of the legal description. Is the legal description surveyable?Note: These overlaps will be shown on the GIS map with a dashed “survey line” and accompanying text for the line not used for the parcel boundary. 3. Parcel lines that do not match location of buildings Structures on parcels do not always lie within the boundaries of the parcel. This may be a circumstance of building without the benefit of a survey or of misinterpreting these boundaries. The parcel lines should be shown accurately as surveyed and/or described regardless of the location of structures on the ground. NOTE: The GIS mapping is not a survey, but is an interpretation of parcel boundaries predicated upon resources available to the County Survey/GIS department.Gary Stevenson Page 1 7/21/2017Example 1Example 2A Example 2B Example 3

This polygon layer contains all of the property parcels in Cambridge. Each parcel has a unique Map and Lot ID number that links it to a record in the Assessing Department's Vision database system. Created for internal use by the Assessing Department to provide a visual reference associated with each parcel in the CAMA database. Created for interdepartmental and external use as part of the web viewers on the City's website. Also created for addressing/geocoding needs, although the Buildings and Master Address List are currently more accurate than Parcels. NOTE: The Parcels GIS data layer is NOT used by Assessing to calculate land area or taxes. Assessors refer to actual deeds or plans accepted by the Massachusetts Land Court. The figures stored in the GIS data layer are for general reference purposes only.

For more information and download links see: https://www.cambridgema.gov/GIS/gisdatadictionary/Assessing

This shapefile contains the zoning district boundaries for the Town of Newfane, Vt., and is to be used in conjunction with other GIS data for mapping. Most of these boundaries coincide with either existing parcel lines or with roads and streams. In this shapefile, the district boundaries were made to coincide with GIS representations of roads and streams where the proposed zoning district boundaries coincide with such features, but not parcel lines. No GIS parcel data for Newfane existed at the time of this shapefile was created, so scans of paper tax maps developed on an unrectified photo base were used. The zoning district boundaries that coincide with parcel boundaries are only approximate. The zoning district boundaries, where they coincide with roads or streams are accurate to the acccuracy of the GIS data representation of those features, but where district boundaries coincide with parcel boundaries, the district boundaries will likely be off by 200 to 500 feet, or more in some areas. As such, these data should be used only in map form to give a general overally impression of the zoning district boundaries for the Town of Newfane, but cannot be used to determine actual zoning district boundaries in any locations other than where these districts coincide with roads or streams. The shapefile Newfane\zoning_prop_taxmap.shp has been created to show the relationship with zoning district boundaries and parcel lines as represented on a series of scanned tax map images. For a more accurate depiction of the Village zoning districts (Newfane, South Newfane, and Williamsville) in relation to the scanned tax map images, see the shapefile Newfane\zoning_prop_village.shp.Data were created in September 2008 but are current to the most recent zoning bylaw amendment of February 19, 2015.

NEW MAP: https://data.cambridgema.gov/Assessing/Cambridge-Parcel-Data-Map/nz6r-3fde

This dataset is OBSOLETE as of 10/24/2024 and will be removed from the Open Data Portal on 10/24/2025

An updated version of this dataset is available at https://data.cambridgema.gov/Assessing/Cambridge-Parcel-Data/c392-hjk3/about_data

This polygon layer contains all of the property parcels in Cambridge. Each parcel has a unique Map and Lot ID number that links it to a record in the Assessing Department's Vision database system. Created for internal use by the Assessing Department to provide a visual reference associated with each parcel in the CAMA database. Created for interdepartmental and external use as part of the web viewers on the City's website. Also created for addressing/geocoding needs, although the Buildings and Master Address List are currently more accurate than Parcels. NOTE: The Parcels GIS data layer is NOT used by Assessing to calculate land area or taxes. Assessors refer to actual deeds or plans accepted by the Massachusetts Land Court. The figures stored in the GIS data layer are for general reference purposes only.

For more information and download links see: https://www.cambridgema.gov/GIS/gisdatadictionary/Assessing

This GIS database is a generalized land cover database designed for Regional Planning with a land use component used for forecasts and modeling at ARC. LandPro should not be taken out of its Regional context, though county-level or municipal-level analysis may be useful for transportation, environmental and land use planning.

Description This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission and is a generalized land cover database designed for regional planning with a land use component used for forecasts and modeling at ARC. LandPro2012 should not be taken out of its regional context, though county-level or municipal-level analysis may be useful for transportation, environmental and land use planning. LandPro2012 is ARC's land use/land cover GIS database for the 21-county Atlanta Region (Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Fulton, Gwinnett, Henry, Rockdale, the EPA non-attainment (8hr standard) counties of Carroll, Coweta, Barrow, Bartow, Forsyth, Hall, Newton, Paulding, Spalding and Walton and Dawson which will become a part of the 2010 Urbanized Area). LandPro2012 was created by on-screen photo-interpretation and digitizing of ortho-rectified aerial photography. The primary source for this GIS database were the local parcels and the 2009 true color imagery with 1.64-foot pixel resolution, provided by Aerials Express, Inc. 2010 is the first year we have used parcel data to help more accurately delineate the LandPro categories.For ArcGIS 10 users: See full metadata by enabling FGDC metadata in ArcCatalog Customize > ArcCatalog Options > Metadata (tab)Though the terms are often used interchangeably, land use and land cover are not synonymous. Land cover generally refers to the natural or cultivated vegetation, rock, or water covering the land, as well as the developed surface which can be identified on aerial photography. Land use generally refers to the way that humans use or will use the land, regardless of its apparent land cover. Collateral data for the land cover mapping effort included the Aero Surveys of Georgia street atlas, the Georgia Department of Community Affairs (DCA) Community Facilities database and the USGS Digital Raster Graphics (DRGs) of 1:24,000 scale topographic maps. The land use component of this database was added after the land cover interpretation was completed, and is based primarily on ownership information provided by the 21 counties and the City of Atlanta for larger tracts of undeveloped land that meet the land use definition of "Extensive Institutional" or "Park Lands" (refer to the Code Descriptions and Discussion section below). Although some of the boundaries of these tracts may align with visible features from the aerial photography, these areas are generally "non-photo-identifiable," thus require other sources for accurate identification. The land use/cover classification system is adapted from the USGS (Anderson) classification system, incorporating a mix of level I, II and III classes. There are a total of 25 categories in ARC's land use/cover system (described below), 2 of which are used only for land use designations: Park Lands (Code 175) and Extensive Institutional (Code 125). The other 23 categories can describe land use and/or land cover, and in most cases will be the same. The LU code will differ from the LC code only where the Park Lands (Code 175) and Extensive Institutional (Code 125) land holdings have been identified from collateral sources of land ownership.Although similar to previous eras of ARC land use/cover databases developed before 1999 (1995, 1990 etc.), "LandPro" differs in many significant ways. Originally, ARC's land use and land cover database was built from 1975 data compiled by USGS at scales of 1:100,000 and selectively, 1:24,000. The coverage was updated in 1990 using SPOT satellite imagery and low-altitude aerial photography and again in 1995 using 1:24,000 scale panchromatic aerial photography. Unlike these previous 5-year updates, the 1999, 2001, 2003, 2005 2007, 2008 and 2009 LandPro databases were compiled at a larger scale (1:14,000) and do not directly reflect pre-1999 delineations. In addition, all components of LandPro were produced using digital orthophotos for on-screen photo-interpretation and digitizing, thus eliminating the use of unrectified photography and the need for data transfer and board digitizing. As a result, the positional accuracy of LandPro is much higher than in previous eras. There have also been some changes to the classification system prior to 1999. Previously, three categories of Forest (41-deciduous, 42-coniferous, and 43-mixed forest) were used; this version does not distinguish between coniferous and deciduous forest, thus Code 40 is used to simply designate Forest. Likewise, two categories of Wetlands (61-forested wetland, and 62-non-forested wetland) were used before; this version does not distinguish between forested and non-forested wetlands, thus Code 60 is used to simply designate Wetlands. With regard to Wetlands, the boundaries themselves are now based on the National Wetlands Inventory (NWI) delineations along with the CIR imagery. Furthermore, Code 51 has been renamed "Rivers" from "Streams and Canals" and represents the Chattahoochee and Etowah Rivers which have been identified in the land use/cover database. In addition to these changes, Code 52 has been dropped from the system as there are no known instances of naturally occurring lakes in the Region. Finally, the land use code for Park Lands has been changed from 173 to 175 so as to minimize confusion with the Parks land cover code, 173. There has been a change in the agriculture classification for LandPro2005 and any LandPro datasets hereafter. Previously, four categories of agriculture (21- agriculture-cropland and pasture, 22 - agriculture - orchards, 23 - agriculture - confined feeding operations and 24 - agriculture - other) were used; this version does not distinguish between the different agricultural lands. Code 20 is now used to designate agriculture. Due to new technology and the enhancements to this database, direct comparison between LandPro99, LandPro2001, LandPro2003 and landPro2005 and all successive updates are now possible, with the 1999 database serving as ARC's new baseline. Please note that as a result of the 2003 mapping effort, LandPro2001 has been adjusted for better comparison to LandPro2003 and is named "LandPro01_adj." Likewise, LandPro99 was previously adjusted when LandPro2001 was completed, but was not further adjusted following the 2003 update. Although some adjustments were originally made to the 1995 land use/cover database for modeling applications, direct comparisons to previous versions of ARC land use/cover before 1999 should be avoided in most cases.The 2010 update has moved away from using the (1:14,000) scale, as will any future updates. Due to the use of local parcels, we have begun to snap LandPro boundaries to the parcel data, making a more accurate dataset. The major change in this update was to make residential areas reflect modern zoning codes more closely. Due to these changes you will no longer be able to compare this dataset to previous years. High density (113) has changed from lots below .25 to lots .25 and smaller. Medium density (112) has changed from .25 to 2 acre lots, to .26 to 1 acre lots. Low density has changed from 2 to 5 acre lots to 1.1 to 2 acre lots. It must be noted that in the 2010 update, you still have old acreage standards reflected in the low density. This will be corrected in the 2011 and 2012 updates. The main focus of the 2010 update was to make sure the LandPro' residential areas reflected the local parcels and change LandPro based on the parcel acreage. DeKalb is the only county not corrected at this time because no parcels were available. The future updates will consist of but are not limited to, reclassifying areas in 111 that do not meet the new acreage standards, delineating and reclassifying Cell Towers, substations and transmission lines/power cuts from TCU (14) to a subset of this (142), reclassifying airports as 141 form TCU, and reclassifying landfills form urban other (17) to 174. Other changes are delineating more roads other than just Limited Access Highways, making sure parks match the already existing Land use parks layer, and beginning to differentiate office from commercial and commercial/industrial.Classification System:111: Low Density Single Family Residential - Houses on 1.1 - 2 acre lots. Though 2010 still reflects the old standard of lots up to 5 acres.112: Medium Density Single Family Residential - These areas usually occur in urban or suburban zones and are generally characterized by houses on .26 to 1 acre lots. This category accounts for the majority of residential land use in the Region and includes a wide variety of neighborhood types.113: High Density Residential - Areas that have predominantly been developed for concentrated single family residential use. These areas occur almost exclusively in urban neighborhoods with streets on a grid network, and are characterized by houses on lots .25 acre or smaller but may also include mixed residential areas with duplexes and small apartment buildings.117: Multifamily Residential - Residential areas comprised predominantly of apartment, condominium and townhouse complexes where net density generally exceeds eight units per acre. Typical apartment buildings are relatively easy to identify, but some high rise structures may be interpreted as, or combined with, office buildings, though many of these dwellings were identified and delineated in downtown and midtown for the first time with the 2003 update. Likewise, some smaller apartments and townhouses may be interpreted as, or combined with, medium- or high-density single family residential. Housing on military bases, campuses, resorts, agricultural properties and construction work sites is

NEW MAP: https://data.cambridgema.gov/Assessing/Cambridge-Parcel-Data-Map/nz6r-3fde

This dataset is OBSOLETE as of 10/24/2024 and will be removed from the Open Data Portal on 10/24/2025

An updated version of this dataset is available at https://data.cambridgema.gov/Assessing/Cambridge-Parcel-Data/c392-hjk3/about_data

This polygon layer contains all of the property parcels in Cambridge. Each parcel has a unique Map and Lot ID number that links it to a record in the Assessing Department's Vision database system. Created for internal use by the Assessing Department to provide a visual reference associated with each parcel in the CAMA database. Created for interdepartmental and external use as part of the web viewers on the City's website. Also created for addressing/geocoding needs, although the Buildings and Master Address List are currently more accurate than Parcels. NOTE: The Parcels GIS data layer is NOT used by Assessing to calculate land area or taxes. Assessors refer to actual deeds or plans accepted by the Massachusetts Land Court. The figures stored in the GIS data layer are for general reference purposes only.

For more information and download links see: https://www.cambridgema.gov/GIS/gisdatadictionary/Assessing

The Historical Generalized Land Use dataset encompasses the seven county Twin Cities (Minneapolis and St. Paul) Metropolitan Area in Minnesota. The dataset was developed by the Metropolitan Council, a regional governmental organization that deals, in part, with regional issues and long range planning for the Twin Cities area. The data were interpreted from 1984, 1990, 1997, 2000, 2005, 2010 and 2016 air photos and other source data, with additional assistance from county parcel data and assessor's information.

The Metropolitan Council has routinely developed generalized land use for the Twin Cities region since 1984 to support its statutory responsibilities and assist in long range planning for the Twin Cities area. The Council uses land use information to monitor growth and to evaluate changing trends in land consumption for various urban purposes. The Council uses the land use trend data in combination with its forecasts of households and jobs to plan for the future needs and financing of Metropolitan services (i.e. Transit, Wastewater Services, etc.). Also, in concert with individual local units of government, the land use and forecast data are used to evaluate expansions of the metropolitan urban service area (MUSA).

The Council does not specifically survey the rights-of-way of minor highways, local streets, parking lots, railroads, or other utility easements. The area occupied by these uses is included with the adjacent land uses, whose boundaries are extended to the centerline of the adjacent rights-of way or easements. The accuracy of Council land use survey data is suitable for regional planning purposes, but should not be used for detailed area planning, nor for engineering work.

Until 1997, the Metropolitan Council had manually interpreted aerial photos on mylar tracing paper into a 13-category land-use classification system to aggregate and depict changing land use data. In 1997, with technological advances in GIS and improved data, the Metropolitan Council was able to delineate land uses from digital aerial photography with counties' parcel and assessor data and captured information with straight 'heads-up' digitizing with GIS software. Also, understanding that land use data collected and maintained at the county and city level are collected at different resolutions using different classification schemes, the Metropolitan Council worked with local communities and organizations to develop a cooperative solution to integrate the Council's land use interpretation with a generally agreed upon regional classification system. By 2000, the Metropolitan Council had not only expanded their Generalized Land Use Classification system to include 22 categories, but had refined how they categorized land (removing all ownership categories) to reflect actual use. See the Entities and Atributes section of the metadata for a detailed description of each of the land use categories and available subcategories.

With the completion of the 2016 Generalized Land Use dataset, regional and local planners have the ability to map changes in urban growth and development in a geographic information system (GIS) database. By tracking land use changes, the Metropolitan Council and local planners can better visualize development trends and anticipate future growth needs.


NOTE ABOUT COMPARATIVE ANALYSIS:

It is important to understand the changes between land use inventory years and how to compare recent land use data to historical data.

In general, over the land use years, more detailed land use information has been captured. Understanding these changes can help interpret land use changes and trends in land consumption. For detailed category definitions, specific land use comparisons and how best to compare the land uses between 1984 and 2016, please refer to the Attribute Accuracy or the Data Quality section of the metadata.

It is also important to note that changes in data collection methodology also effects the ability to compare land use years:

- In 2000, the land use categories were modified to more accurately reflect the use of the land rather than ownership. Although this has minimal effect on associating categories between 1997 and 2000, is may have had an affect on some particular land use. For example, land owned by a community or county but had no apparent active use could have been classified as 'Public/ Semi-Public' prior to 2000. In 2000, land with no apparent use, regardless of who owns it, is classified as 'Undeveloped.'

- With better resolution of air photos beginning in 2000, the incorporation of property information from county assessors and the use of more accurate political boundaries (particularly on the exterior boundaries of the region), positive impacts were made on the accuracy of new land use delineations between pre-2000 land use data and data collected in 2000, 2005, 2010, 2016. With the improved data, beginning in 2000, a greater effort to align land use designations, both new and old, to correspond with property boundaries (county parcels) where appropriate. In addition, individual properties were reviewed to assess the extent of development. In most cases, if properties under 5 acres were assessed to be at least 75% developed, then the entire property was classified as a developed land use (not 'Undeveloped'). As a result of these realignments and development assessments, changes in land use between early land use years (1984-1997) and more recent years (2000-2016) will exist in the data that do NOT necessarily represent actual land use change. These occurrences can be found throughout the region.

There are also numerous known deficiencies in the datasets. Some known deficiencies are specific to a particular year while others may span the entire time series. For more details, please refer to Attribute Accuracy of the Data Quality section of the metadata.

More Metadata

Data updated daily.

Street centerline was originally developed from the road features on the County's 1:2400 base map. Both address range and a link to street names are included as data attributes. The current data is collected from recorded plats in DXF format or is digitized from plats. The data covers the entire County as well as the incorporated Towns. Purpose: The street centerline coverage was originally developed to support addressing. Fire & Rescue Services and the Sheriff's Office use the address range information for Emergency 911 dispatching; it is also used to update U.S. Census Bureau Tiger data in support of the decennial census. Supplemental Information: Centerline data are maintained from plats and site plans prepared by engineering and surveying firms. Although street centerline is the most current of the County's street data, deviation in construction after plat submittal and recordation may cause centerline to differ from similar County layer, road casing. Centerline contains the most current data; road casing is spatially more accurate because it is updated photogrammetrically. These data are not intended to be used for local surveys or at a scale larger than 1:2400. Data are stored in the corporate ArcSDE Geodatabase as a feature class. The coordinate system is Virginia State Plane (North), Zone 4501, datum NAD83 HARN. Maintenance and Update Frequency: Data is updated on a daily basis. Completeness Report: Features may have been eliminated or generalized due to scale and intended use. To assist Loudoun County, Virginia in the maintenance of the data, please provide any information concerning discovered errors, omissions, or other discrepancies found in the data. Data Owner: Office of Mapping and Geographic Information

UPDATE: AN ERROR WAS FOUND IN THE TRAINING DATA PREPARATION. AN UPDATED VERSION HAS BEEN CREATED. THIS VERSION SIGNIFICANTLY UNDERESTIMATES VALUES, BUT REPRESENTS TRENDS DECENTLY, PLEASE SEE THE UPDATED VERSION OF THIS REPOSITORY FOR BETTER PREDICTIONS.

Repository includes maps describing the depth (cm) to the top of any water or root soil restrictive layer (resdept) as defined by United States soil survey program.

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for the training sample (file ending _CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: resdept_r_cm_2D_QRF.tif

Indicates depth to top of restriction (resdept; in cm) using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN A NEW VERSION OF THE REPOSITORY.

Repository includes maps of organic matter content (% wt) as defined by United States soil survey program.

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: om_r_0_cm_2D_QRF_bt.tif

Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission to show generalized land cover for regional planning with a land use component used for forecasts and modeling at ARC.LandPro2012 should not be taken out of its regional context, though county-level or municipal-level analysis may be useful for transportation, environmental and land use planning. LandPro2012 is ARC's land use/land cover GIS database for the 21-county Atlanta Region (Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Fulton, Gwinnett, Henry, Rockdale, the EPA non-attainment (8hr standard) counties of Carroll, Coweta, Barrow, Bartow, Forsyth, Hall, Newton, Paulding, Spalding and Walton and Dawson which will become a part of the 2010 Urbanized Area). LandPro2012 was created by on-screen photo-interpretation and digitizing of ortho-rectified aerial photography. The primary source for this GIS database were the local parcels and the 2009 true color imagery with 1.64-foot pixel resolution, provided by Aerials Express, Inc. 2010 is the first year we have used parcel data to help more accurately delineate the LandPro categories.For ArcGIS 10 users: See full metadata by enabling FGDC metadata in ArcCatalog Customize > ArcCatalog Options > Metadata (tab)Though the terms are often used interchangeably, land use and land cover are not synonymous. Land cover generally refers to the natural or cultivated vegetation, rock, or water covering the land, as well as the developed surface which can be identified on aerial photography. Land use generally refers to the way that humans use or will use the land, regardless of its apparent land cover. Collateral data for the land cover mapping effort included the Aero Surveys of Georgia street atlas, the Georgia Department of Community Affairs (DCA) Community Facilities database and the USGS Digital Raster Graphics (DRGs) of 1:24,000 scale topographic maps. The land use component of this database was added after the land cover interpretation was completed, and is based primarily on ownership information provided by the 21 counties and the City of Atlanta for larger tracts of undeveloped land that meet the land use definition of "Extensive Institutional" or "Park Lands" (refer to the Code Descriptions and Discussion section below). Although some of the boundaries of these tracts may align with visible features from the aerial photography, these areas are generally "non-photo-identifiable," thus require other sources for accurate identification. The land use/cover classification system is adapted from the USGS (Anderson) classification system, incorporating a mix of level I, II and III classes. There are a total of 25 categories in ARC's land use/cover system (described below), 2 of which are used only for land use designations: Park Lands (Code 175) and Extensive Institutional (Code 125). The other 23 categories can describe land use and/or land cover, and in most cases will be the same. The LU code will differ from the LC code only where the Park Lands (Code 175) and Extensive Institutional (Code 125) land holdings have been identified from collateral sources of land ownership.Although similar to previous eras of ARC land use/cover databases developed before 1999 (1995, 1990 etc.), "LandPro" differs in many significant ways. Originally, ARC's land use and land cover database was built from 1975 data compiled by USGS at scales of 1:100,000 and selectively, 1:24,000. The coverage was updated in 1990 using SPOT satellite imagery and low-altitude aerial photography and again in 1995 using 1:24,000 scale panchromatic aerial photography. Unlike these previous 5-year updates, the 1999, 2001, 2003, 2005 2007, 2008 and 2009 LandPro databases were compiled at a larger scale (1:14,000) and do not directly reflect pre-1999 delineations. In addition, all components of LandPro were produced using digital orthophotos for on-screen photo-interpretation and digitizing, thus eliminating the use of unrectified photography and the need for data transfer and board digitizing. As a result, the positional accuracy of LandPro is much higher than in previous eras. There have also been some changes to the classification system prior to 1999. Previously, three categories of Forest (41-deciduous, 42-coniferous, and 43-mixed forest) were used; this version does not distinguish between coniferous and deciduous forest, thus Code 40 is used to simply designate Forest. Likewise, two categories of Wetlands (61-forested wetland, and 62-non-forested wetland) were used before; this version does not distinguish between forested and non-forested wetlands, thus Code 60 is used to simply designate Wetlands. With regard to Wetlands, the boundaries themselves are now based on the National Wetlands Inventory (NWI) delineations along with the CIR imagery. Furthermore, Code 51 has been renamed "Rivers" from "Streams and Canals" and represents the Chattahoochee and Etowah Rivers which have been identified in the land use/cover database. In addition to these changes, Code 52 has been dropped from the system as there are no known instances of naturally occurring lakes in the Region. Finally, the land use code for Park Lands has been changed from 173 to 175 so as to minimize confusion with the Parks land cover code, 173. There has been a change in the agriculture classification for LandPro2005 and any LandPro datasets hereafter. Previously, four categories of agriculture (21- agriculture-cropland and pasture, 22 - agriculture - orchards, 23 - agriculture - confined feeding operations and 24 - agriculture - other) were used; this version does not distinguish between the different agricultural lands. Code 20 is now used to designate agriculture. Due to new technology and the enhancements to this database, direct comparison between LandPro99, LandPro2001, LandPro2003 and landPro2005 and all successive updates are now possible, with the 1999 database serving as ARC's new baseline. Please note that as a result of the 2003 mapping effort, LandPro2001 has been adjusted for better comparison to LandPro2003 and is named "LandPro01_adj." Likewise, LandPro99 was previously adjusted when LandPro2001 was completed, but was not further adjusted following the 2003 update. Although some adjustments were originally made to the 1995 land use/cover database for modeling applications, direct comparisons to previous versions of ARC land use/cover before 1999 should be avoided in most cases.The 2010 update has moved away from using the (1:14,000) scale, as will any future updates. Due to the use of local parcels, we have begun to snap LandPro boundaries to the parcel data, making a more accurate dataset. The major change in this update was to make residential areas reflect modern zoning codes more closely. Due to these changes you will no longer be able to compare this dataset to previous years. High density (113) has changed from lots below .25 to lots .25 and smaller. Medium density (112) has changed from .25 to 2 acre lots, to .26 to 1 acre lots. Low density has changed from 2 to 5 acre lots to 1.1 to 2 acre lots. It must be noted that in the 2010 update, you still have old acreage standards reflected in the low density. This will be corrected in the 2011 and 2012 updates. The main focus of the 2010 update was to make sure the LandPro' residential areas reflected the local parcels and change LandPro based on the parcel acreage. DeKalb is the only county not corrected at this time because no parcels were available. The future updates will consist of but are not limited to, reclassifying areas in 111 that do not meet the new acreage standards, delineating and reclassifying Cell Towers, substations and transmission lines/power cuts from TCU (14) to a subset of this (142), reclassifying airports as 141 form TCU, and reclassifying landfills form urban other (17) to 174. Other changes are delineating more roads other than just Limited Access Highways, making sure parks match the already existing Land use parks layer, and beginning to differentiate office from commercial and commercial/industrial.Classification System:111: Low Density Single Family Residential - Houses on 1.1 - 2 acre lots. Though 2010 still reflects the old standard of lots up to 5 acres.112: Medium Density Single Family Residential - These areas usually occur in urban or suburban zones and are generally characterized by houses on .26 to 1 acre lots. This category accounts for the majority of residential land use in the Region and includes a wide variety of neighborhood types.113: High Density Residential - Areas that have predominantly been developed for concentrated single family residential use. These areas occur almost exclusively in urban neighborhoods with streets on a grid network, and are characterized by houses on lots .25 acre or smaller but may also include mixed residential areas with duplexes and small apartment buildings.117: Multifamily Residential - Residential areas comprised predominantly of apartment, condominium and townhouse complexes where net density generally exceeds eight units per acre. Typical apartment buildings are relatively easy to identify, but some high rise structures may be interpreted as, or combined with, office buildings, though many of these dwellings were identified and delineated in downtown and midtown for the first time with the 2003 update. Likewise, some smaller apartments and townhouses may be interpreted as, or combined with, medium- or high-density single family residential. Housing on military bases, campuses, resorts, agricultural properties and construction work sites is not included in this or other residential categories.119: Mobile Home Parks - Areas that have been developed for single family mobile home use. These residential areas may occur in urban, suburban, or rural zones throughout the Region, with or without a significant mix of forested land cover. Due to their sparse distribution, individual mobile homes are

The Canadian County Parcel Data Public View is a set of geospatial features representing the surface ownership of property in fee simple for property tax purposes as required by 68 O.S. § 2821 and other related data used to produce the parcels such as subdivision boundaries and subdivision lots. The data is created from source documentation filed with the Canadian County Clerk's Office including deeds, easements, and plats. Other data sources such as filed Certified Corner Records filed with the State of Oklahoma or highway plans produced by the Department of Transportation may be used to adjust parcel boundaries. Single legal descriptions may be split up into two or more parcels if the description crosses the boundaries of multiple taxing jurisdictions or crosses quarter section boundaries. Accuracy of parcel data can vary considerably due to a combination of factors. Most parcels and subdivision legal descriptions reference a quarter section or quarter section corner. The accuracy of the quarter section corners is discussed with Canadian County's Public Land Survey System Data. Accuracy is further enhanced or degraded by the quality of the legal description used to create the feature. Generally, legal descriptions created from surveys will have higher accuracy the newer they were created due to improvements in the field of surveying. However, it can be difficult to determine the age of a legal description as descriptions are generally reused on subsequent deeds after the description was first created. Legal descriptions can occasionally contain updated bearings and distances and may denote the updates. The Assessor's Office uses the latest available legal description for creating parcels. Legal descriptions may lack specificity such as the use of "North" instead of a measured bearing or have missing parameters such as missing bearings for curved boundaries. In these cases, parcel data accuracy can be degraded. Further, if a legal description contains a specific landmark or boundary, sometimes called a "bound", the boundary is drawn to that point or landmark regardless of whether the bearing and/or distance accurately arrive at that point. For instance, if a legal description reads "...to the south line of the southeast quarter", the boundary is drawn to the south line of the quarter section even if the bearing and distance are short of or extend beyond that point. Because parcel data must be created for the entire county regardless of the accuracy of the descriptions used to create those parcels, parcels may need to be "stretched" or "squeezed" to make them fit together. When possible, the Assessor's Office relies on the most accurate legal descriptions to set the boundaries and then fits older boundaries to them. Due to the large number of variables, parcel data accuracy cannot be guaranteed nor can the level of accuracy be described for the entire dataset. While Canadian County makes every reasonable effort to make sure parcel data is accurate, this data cannot be used in place of a survey performed by an Oklahoma Licensed Professional Land Surveyor.ParcelDataExternal - Polygons representing surface fee simple title. This parcel data formatted and prepared for public use. Some fields may be blank to comply with 22 O.S. § 60.14 & 68 O.S. § 2899.1Attributes:Account (account): The unique identifier for parcel data generated by the appraisal software used by the Assessor's Office"A" Number (a_number): An integer assigned in approximate chronological order to represent each parcel divided per quarter sectionParcel ID (parcel_id): Number used to identify parcels geographically, see Parcel Data Export Appendix A for an in-depth explanation. This identifier is not unique for all parcelsParcel Size (parcel_size): Size of the parcels, must be used in conjunction with following units fieldParcel Size Units (parcel_size_units): Units for the size of the parcel. Can be "Acres" or "Lots" for parcels within subdivisions that are valued per lotOwner's Name (owners_name): Name of the surface owner of the property in fee simple on recordMailing Information (mail_info): Extra space for the owners name if needed or trustee namesMailing Information 2 (mail_info2): Forwarded mail or "In care of" mailing informationMailing Address (mail_address): Mailing address for the owner or forwarding mailing addressMailing City (mail_city): Mailing or postal cityMailing State (mail_state): Mailing state abbreviated to standard United States Postal Service codesMailing ZIP Code (mail_zip): Mailing ZIP code as determined by the United States Postal ServiceTax Area Code (tax_area): Integer numeric code representing an area in which all the taxing jurisdictions are the same. See Parcel Data Appendix B for a more detailed description of each tax areaTax Area Description (tax_area_desc): Character string code representing the tax area. See Parcel Data Appendix B for a more detailed description of each tax areaProperty Class (prop_class): The Assessor's Office classification of each parcel by rural (no city taxes) or urban (subject to city taxes) and exempt, residential, commercial, or agriculture. This classification system is for property appraisal purposes and does not reflect zoning classifications in use by municipalities. See Parcel Data Appendix B for a more detailed description of each property classificationLegal Description (legal): A highly abbreviated version of the legal description for each parcel. This legal description may not match the most recent legal description for any given property due to administrative divisions as described above, or changes made to the property by way of recorded instruments dividing smaller parcels from the original description. This description may NOT be used in place of a true legal descriptionSubdivision Code (subdiv_code): A numeric code representing a recorded subdivision plat which contains the parcel. This value will be "0" for any parcel not part of a recorded subdivision plat.Subdivision Name (subdiv_name): The name of the recorded subdivision plat abbreviated as needed to adapt to appraisal software field limitationsSubdivision Block Number (subdiv_block): Numeric field representing the block number of a parcel. This value will be "0" if the parcel is not in a recorded subdivision plat or if the plat did not contain block numbersSubdivision Lot Number (subdiv_lot): Numeric field representing the lot number of a parcel. This value will be "0" if the parcel is not in a recorded subdivision platTownship Number (township): Numeric field representing the Public Land Survey System tier or township the parcel is located in. All townships or tiers in Canadian County are north of the base line of the Indian Meridian.Range Number (range): Numeric field representing the Public Land Survey System range the parcel is located in. All Ranges in Canadian County are west of the Indian MeridianSection Number (section): Numeric field representing the Public Land Survey System section number the parcel is located inQuarter Section Code (quarter_sec): Numeric field with a code representing the quarter section a majority of the parcel is located in, 1 = Northeast Quarter, 2 = Northwest Quarter, 3 = Southwest Quarter, 4 = Southeast QuarterSitus Address (situs): Address of the property itself if it is knownSitus City (situs_city): Name of the city the parcel is actually located in (regardless of the postal city) or "Unincorporated" if the parcel is outside any incorporated city limitsSitus ZIP Code (situs_zip): ZIP Code as determined by the United States Postal Service for the property itself if it is knownLand Value (land_val): Appraised value of the land encompassed by the parcel as determined by the Assessor's OfficeImprovement Value (impr_val): Appraised value of the improvements (house, commercial building, etc.) on the property as determined by the Assessor's OfficeManufactured Home Value (mh_val): Appraised value of any manufactured homes on the property and owned by the same owner of the land as determined by the Assessor's OfficeTotal Value (total_val): Total appraised value for the property as determined by the Assessor's OfficeTotal Capped Value (cap_val): The capped value as required by Article X, Section 8B of the Oklahoma ConstitutionTotal Assessed Value (total_assess): The capped value multiplied by the assessment ratio of Canadian County, which is 12% of the capped valueHomestead Exempt Amount (hs_ex_amount): The amount exempt from the assessed value if a homestead exemption is in placeOther Exempt Value (other_ex_amount): The amount exempt from the assessed value if other exemptions are in placeTaxable Value (taxable_val): The amount taxes are calculated on which is the total assessed value minus all exemptionsSubdivisions - Polygons representing a plat or subdivision filed with the County Clerk of Canadian County. Subdivision boundaries may be revised by vacations of the plat or subdivision or by replatting a portion or all of a subdivision. Therefore, subdivision boundaries may not match the boundaries as shown on the originally filed plat.Attributes:Subdivision Name (subdivision_name): The name of the plat or subdivisionSubdivision Number (subdivision_number): An ID for each subdivision created as a portion of the parcel ID discussed in Parcel Data Export Appendix APlat Book Number (book): The book number for the recorded documentPlat Book Page Number (page): The page number for the recorded documentRecorded Acres (acres): The number of acres within the subdivision if knownRecorded Date (recorded_date): The date the document creating the subdivision was recordedDocument URL (clerk_url): URL to download a copy of the document recorded by the Canadian County Clerk's OfficeBlocks - Polygons derived from subdivision lots representing the blocks

Repository includes maps of available water capacity as defined by United States soil survey program (1/3 to 15 bar).

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: awc_r_0_cm_2D_QRF.tif

Indicates available water content (awc) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma.

The Terrestrial 30x30 Conserved Areas map layer was developed by the CA Nature working group, providing a statewide perspective on areas managed for the protection or enhancement of biodiversity. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.Terrestrial and Freshwater Data• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or small group of parcels which comprise the spatial features of CPAD, generally corresponding to ownership boundaries. • The California Conservation Easement Database (CCED), managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity. Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership. •The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such, boundaries represented within it may not align with other data sources. • Numerous datasets representing designated boundaries for entities such as National Parks and Monuments, Wild and Scenic Rivers, Wilderness Areas, and others, were downloaded from publicly available sources, typically hosted by the managing agency.Methodology1.CPAD and CCED represent the most accurate location and ownership information for parcels in California which contribute to the preservation of open space and cultural and biological resources.2. Superunits are collections of parcels (Holdings) within CPAD which share a name, manager, and access policy. Most Superunits are also managed with a generally consistent strategy for biodiversity conservation. Examples of Superunits include Yosemite National Park, Giant Sequoia National Monument, and Anza-Borrego Desert State Park. 3. Some Superunits, such as those owned and managed by the Bureau of Land Management, U.S. Forest Service, or National Park Service , are intersected by one or more designations, each of which may have a distinct management emphasis with regards to biodiversity. Examples of such designations are Wilderness Areas, Wild and Scenic Rivers, or National Monuments.4. CPAD Superunits and CCED easements were intersected with all designation boundary files to create the operative spatial units for conservation analysis, henceforth 'Conservation Units,' which make up the Terrestrial 30x30 Conserved Areas map layer. Each easement was functionally considered to be a Superunit. 5. Each Conservation Unit was intersected with the PAD-US dataset in order to determine the management emphasis with respect to biodiversity, i.e., the GAP code. Because PAD-US is national in scope and not specifically parcel aligned with California assessors' surveys, a direct spatial extraction of GAP codes from PAD-US would leave tens of thousands of GAP code data slivers within the 30x30 Conserved Areas map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single GAP code was uniformly assigned that code. Additionally, the total area of GAP codes 1 and 2 were summed for the remaining uncoded Conservation Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2. 6.Subsequent to this stage of analysis, certain Conservation Units remained uncoded, either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset. 7.These uncoded Conservation Units were then broken down into their constituent, finer resolution Holdings, which were then analyzed according to the above workflow. 8. Areas remaining uncoded following the two-step process of coding at the Superunit and then Holding levels were assigned a GAP code of 4. This is consistent with the definition of GAP Code 4: areas unknown to have a biodiversity management focus. 9. Greater than 90% of all areas in the Terrestrial 30x30 Conserved Areas map layer were GAP coded at the level of CPAD Superunits intersected by designation boundaries, the coarsest land units of analysis. By adopting these coarser analytical units, the Terrestrial 30X30 Conserved Areas map layer avoids hundreds of thousands of spatial slivers that result from intersecting designations with smaller, more numerous parcel records. In most cases, individual parcels reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.10. PAD-US is a principal data source for understanding the spatial distribution of GAP coded lands, but it is national in scope, and may not always be the most current source of data with respect to California holdings. GreenInfo Network, which develops and maintains the CPAD and CCED datasets, has taken a lead role in establishing communication with land stewards across California in order to make GAP attribution of these lands as current and accurate as possible. The tabular attribution of these datasets is analyzed in addition to PAD-US in order to understand whether a holding may be considered conserved. Tracking Conserved Areas The total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing improvements in our understanding of existing biodiversity conservation efforts. The California Nature Project is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to investigate how their lands are represented in the Terrestrial 30X30 Conserved Areas Map Layer. This can be accomplished by using the Conserved Areas Explorer web application, developed by the CA Nature working group. Users can zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data. CPAD, CCED, and PAD-US are built from the ground up. Data is derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Terrestrial 30X30 Conserved Areas map layer, please use this link to initiate a review.The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

This is SCAG 2019 Regional Land Use dataset developed for the final 2024 Connect SoCal, the 2024-2050 Regional Transportation Plan/Sustainable Communities Strategy (RTP/SCS), including general plan land use, specific plan land use, zoning code, and existing land use at parcel-level (approximately five million parcels) for 197 local jurisdictions in the SCAG region.The regional land use dataset is developed (1) to aid in SCAG’s regional transportation planning, scenario planning and growth forecasting, (2) facilitate policy discussion on various planning issues, and (3) enhance information database to better serve SCAG member jurisdictions, research institutes, universities, developers, general public, etc. It is the most frequently and widely utilized SCAG geospatial data. From late 2019 to early 2020, SCAG staff obtained the 2019 parcel boundary GIS file and tax roll property information from county assessor’s offices. After months of data standardization and clean-up process, SCAG staff released the 2019 parcel boundary GIS files along with the 2019 Annual Land Use dataset in February 2021. In December 2021, SCAG staff successfully developed the preliminary dataset of the 2019 regional land use data and released the draft SCAG Data/Map Book in May 2022. The preliminary land use data was reviewed by local jurisdictions during the Local Data Exchange (LDX) process for Connect SoCal 2024. As a part of the final 2019 regional land use data development process, SCAG staff made every effort to review the local jurisdictions’ inputs and comments and incorporated any updates to the regional land use datasets. The products of this project has been used as one of the key elements for Connect SoCal 2024 plan development, growth forecasting, scenario planning, and SCAG’s policy discussion on various planning issues, as well as Connect SoCal key growth strategy analysis.Note: This dataset is intended for planning purposes only, and SCAG shall incur no responsibility or liability as to the completeness, currentness, or accuracy of this information. SCAG assumes no responsibility arising from use of this information by individuals, businesses, or other public entities. The information is provided with no warranty of any kind, expressed or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Users should consult with each local jurisdiction directly to obtain the official land use information.2019 SCAG Land Use Codes: LegendLand Use Description Single Family Residential1110 Single Family Residential 1111 High Density Single Family Residential (9 or more DUs/ac) 1112 Medium Density Single Family Residential (3-8 DUs/ac) 1113 Low Density Single Family Residential (2 or less DUs/ac)Multi-Family Residential1120 Multi-Family Residential 1121 Mixed Multi-Family Residential1122 Duplexes, Triplexes and 2- or 3-Unit Condominiums and Townhouses1123 Low-Rise Apartments, Condominiums, and Townhouses1124 Medium-Rise Apartments and Condominiums1125 High-Rise Apartments and CondominiumsMobile Homes and Trailer Parks1130 Mobile Homes and Trailer Parks1131 Trailer Parks and Mobile Home Courts, High-Density1132 Mobile Home Courts and Subdivisions, Low-DensityMixed Residential1140 Mixed Residential1100 ResidentialRural Residential 1150 Rural ResidentialGeneral Office1210 General Office Use 1211 Low- and Medium-Rise Major Office Use 1212 High-Rise Major Office Use 1213 SkyscrapersCommercial and Services1200 Commercial and Services1220 Retail Stores and Commercial Services 1221 Regional Shopping Center 1222 Retail Centers (Non-Strip With Contiguous Interconnected Off-Street Parking) 1223 Retail Strip Development1230 Other Commercial 1231 Commercial Storage 1232 Commercial Recreation 1233 Hotels and MotelsFacilities1240 Public Facilities1241 Government Offices1242 Police and Sheriff Stations1243 Fire Stations1244 Major Medical Health Care Facilities1245 Religious Facilities1246 Other Public Facilities1247 Public Parking Facilities1250 Special Use Facilities1251 Correctional Facilities1252 Special Care Facilities1253 Other Special Use FacilitiesEducation1260 Educational Institutions1261 Pre-Schools/Day Care Centers1262 Elementary Schools1263 Junior or Intermediate High Schools1264 Senior High Schools1265 Colleges and Universities1266 Trade Schools and Professional Training FacilitiesMilitary Installations1270 Military Installations1271 Base (Built-up Area)1272 Vacant Area1273 Air Field1274 Former Base (Built-up Area)1275 Former Base Vacant Area1276 Former Base Air FieldIndustrial1300 Industrial 1310 Light Industrial1311 Manufacturing, Assembly, and Industrial Services1312 Motion Picture and Television Studio Lots1313 Packing Houses and Grain Elevators1314 Research and Development1320 Heavy Industrial1321 Manufacturing1322 Petroleum Refining and Processing1323 Open Storage1324 Major Metal Processing1325 Chemical Processing1330 Extraction1331 Mineral Extraction - Other Than Oil and Gas1332 Mineral Extraction - Oil and Gas1340 Wholesaling and WarehousingTransportation, Communications, and Utilities1400 Transportation, Communications, and Utilities 1410 Transportation1411 Airports1412 Railroads1413 Freeways and Major Roads1414 Park-and-Ride Lots1415 Bus Terminals and Yards1416 Truck Terminals1417 Harbor Facilities1418 Navigation Aids1420 Communication Facilities1430 Utility Facilities1431 Electrical Power Facilities1432 Solid Waste Disposal Facilities1433 Liquid Waste Disposal Facilities1434 Water Storage Facilities1435 Natural Gas and Petroleum Facilities1436 Water Transfer Facilities 1437 Improved Flood Waterways and Structures1438 Mixed Utilities1440 Maintenance Yards1441 Bus Yards1442 Rail Yards1450 Mixed Transportation1460 Mixed Transportation and UtilityMixed Commercial and Industrial1500 Mixed Commercial and IndustrialMixed Residential and Commercial1600 Mixed Residential and Commercial 1610 Residential-Oriented Residential/Commercial Mixed Use 1620 Commercial-Oriented Residential/Commercial Mixed UseOpen Space and Recreation1800 Open Space and Recreation 1810 Golf Courses 1820 Local Parks and Recreation 1830 Regional Parks and Recreation 1840 Cemeteries 1850 Wildlife Preserves and Sanctuaries 1860 Specimen Gardens and Arboreta 1870 Beach Parks 1880 Other Open Space and Recreation 1890 Off-Street TrailsAgriculture2000 Agriculture2100 Cropland and Improved Pasture Land2110 Irrigated Cropland and Improved Pasture Land2120 Non-Irrigated Cropland and Improved Pasture Land2200 Orchards and Vineyards2300 Nurseries2400 Dairy, Intensive Livestock, and Associated Facilities2500 Poultry Operations2600 Other Agriculture2700 Horse RanchesVacant3000 Vacant3100 Vacant Undifferentiated3200 Abandoned Orchards and Vineyards3300 Vacant With Limited Improvements3400 Beaches (Vacant)1900 Urban VacantWater4000 Water4100 Water, Undifferentiated4200 Harbor Water Facilities4300 Marina Water Facilities4400 Water Within a Military Installation4500 Area of Inundation (High Water)Specific Plan7777 Specific PlanUnder Construction1700 Under ConstructionUndevelopable or Protected Land8888 Undevelopable or Protected LandUnknown9999 Unknown

The spatial accuracy of the lot fabric for some townships has been improved through the Ontario Parcel, Township Realignment and Township Improvement projects. Improvements to the fabric may include:road allowance widthsspatial changes to better represent the location of lot boundariesmore consistent concession names.Data is collected on an on-going basis. The time period “end date” may be more recent than indicated here.Additional DocumentationLot Fabric Improved - Data DescriptionLot Fabric Improved - DocumentationLot Fabric Improved - FAQStatusOn going: data is being continually updatedMaintenance and Update FrequencyAnnually: data is updated every yearContactOffice of the Surveyor General, landtenuremapping@ontario.ca

Metadata creation by Eric Lubell (GIS Coordinator) on 7/15/24. Current GIS Coordinator is taking over from a retired employee who curated the data for 25 years, with a varied degree of attention to detail. This is a small, rural County, and we apologize in advance for any errors.MAP - this is the last 3 digits of the parcel number and needs to be updated whenever a parcel number is changed. This field should not be null.LOT - this is typically from the plat and may or may not exist. This field, historically, has many values other than the LOT stored in it. This field has not been reliably populated and is assumed to have many errors that will eventually be fixed. No ETA on accuracy QAQC. This field can be null.BLK - current GIS Coordinator still not entirely sure what this field represents. This field can be null.SUB - this field is supposed to represent the subdivision that the parcel is contained within. This field has not been reliably populated and is assumed to have many errors that will eventually be fixed. No ETA on accuracy QAQC. This field can not be null.MAPLOC - this is a combination of data. The first 4 digits should represent the State of Colorado's Assessment Mapping and Parcel Identification ID. The next 2 digits are the PLSS Section number. I'm not exactly sure what any additional digits represent but is is common to have additional digits. It is also common to only have 4 digits and, as always, QAQC is needed and an ETA is unknown.ORDER_ - C stands for Calculated (Aliquot parts w/o survey infromation) and S stands for Survey. This field can be null and I suspect there are many errors, not really sure if it should be kept at all. REMOVED 6/16/25 after backup.MR - this is for Mineral Rights. 600s are for improvements on leased land, 700s are mineral rights, 800s are mining claims. MRO is mineral rights only. This field can be null and I suspect there are many errors, not really sure if it should be kept at all.PI - this is for Partial Interest. This field can be null and I suspect there are many errors, not really sure if it should be kept at all. REMOVED 6/16/25 after backup.MapType - clearly something related to PLSS data but I suspect this field is highly inaccurate. This field can be null and I suspect there are many errors, not really sure if it should be kept at all. REMOVED 6/16/25 after backup.ORDERNO - this field is no longer relevant and will be deleted. DELETED 8/1/24.STREETNO - the street number of the address. Some parcels have yet to be assigned an address, so this field can be null.PREDIRECTION - the direction of the address, for example W for West before something like Main Street. This field can be null.STREETNAME - the street name of the address. This field can be null but should probably be filled in more than it is.STREETTYPE - the street type, drive, circle, etc. This field can be null.UNITTYPE - field used to identify the type of unit, currently only building, common elements, hangar, lot, or unit. UNITTYPE and LOT seem to have been used interchangeably, so some QAQC needs to happen. This is primarily for multi family housing. This field can be null.UNITNAME - the number or letter assigned to the unit. This field can be null.PROPERTYCITY - the city or township where the parcel is situated. Unincorporated indicates it is on County land. This field should not be null.EXEMPT - this field identifies exempt parcels: BV is Buena Vista, CC is Chaffee County, CO is State of Colorado, OT is Other (think churches), PS is Poncha Springs, SA is Salida, SA/CC is Salida and Chaffee County, SD is School District, US is the Federal Government, DPT = Department of Property Taxation. These are tax exempt properties. This field can be null.BUSINESSNAME - no clue as to the accuracy of this data but an attempt at identifying the business that exists on the parcel. It should probably be removed from the parcel data and only remain in the exports from RealWare. This field can be null.ADDRES - the full address concatenated from the other address related fields. This field should not be null.TaxDist - the tax district of the parcel. With the exception of parcels that split a tax district, this field should be accurate as of 7/14/24. This field should not be null.AREA - this is where the area was captured from the plat map. However, it was not filled in consistently. It will be replaced with the next 4 fields for better accuracy. This field should not be null.Plat_Acres - if there is an acres value on the plat, it should be recorded here. This field can be null.GIS_Acres - calculated with geodesic value on all parcels and used as a QAQC on the plat acres (if applicable). This field should not be null.Plat_SqFt - if there is a square footage value on the plat, it should be recorded here. This field can be null.GIS_SqFt - calculated with geodesic value on all parcels and used as a QAQC on the plat square footage (if applicable). This field should not be null.NOTE_01 - field added by current GIS Coordinator to capture notes not applicable to other fields. This field was used in some cases to capture previously entered data that wasn't entered into the correct place. This field can be null.NOTE_02 - second field added by current GIS Coordinator to capture notes not applicable to other fields. This field was used in some cases to capture previously entered data that wasn't entered into the correct place. This field can be null.Added "ReceptNum" on 9/20/24 to record the reception number from the Recorder's Office.PROCESSING NOTES: Over the last six months, the current GIS Coordinator has spent significant amounts of time and effort to clean this layer up. Starting on 7/14/24, additional QA/QC and processing notes will be recorded here:7/18/24 - used Select by Attributes to identify 75 parcels where the MAP value did NOT match the last three digits of the Parcel and fixed them accordingly. PARCEL LIKE '%' || CAST(MAP AS VARCHAR(3)). This was the field calculator expression to fix the issue: !PARCEL![-3:]8/1/24 - added and attributed the Fire District and Tax District fields. Also exported the Owner table from ListBuilder so I could populate the STATUS field and the GIS_TYPE field.8/8/24 - added in the redacted accounts (Status = Redacted) so that I can be sure NOT to deliver the ownership data for these parcels in data requests. The query STATUS LIKE '%Redacted%' will identify them all.8/8/24 - Ran the Condo Search in RW and updated the GIS_TYPE field accordingly. My initial accounting of Condos in GIS came up with 554 parcels but the RW export showed 689 so much more accurate.

The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:

Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release, https://doi.org/10.5066/P9SK0DO2.

Associated publication:

Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. https://doi.org/10.1002/saj2.20080.

Repository includes maps of carbonate content (caco3) as defined by United States soil survey program. Content is calculated on the fine earth fraction (<2mm).

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: caco3_r_0_cm_2D_QRF_bt.tif

Indicates carbonate (caco3) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest that is has gone through transfomation and backtransformation (_bt) in the modeling process. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma.

This Esri File GeoDatabase (FGDB) contains digital tax parcel data for Maine Organized Towns and includes the following: Parcels (feature layer); Parcels_ADB (table); and GEOCODES (table).Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. Maine Parcels Organized Towns and Maine Parcels Organized Towns ADB are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data; some data are more than ten years old. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions.In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), the Maine Revenue Service is the authoritative source for parcel data. Maine Parcels Unorganized Territory is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of the Maine Revenue Service to compile up-to-date parcel ownership information.Property maps are a fundamental base for many municipal activities. Although GIS parcel data cannot replace detailed ground surveys, the data can assist municipal officials with functions such as accurate property tax assessment, planning and zoning. Towns can link maps to an assessor's database and display local information, while town officials can show taxpayers how proposed development or changes in municipal services and regulations may affect the community. In many towns, parcel data also helps to provide public notices, plan bus routes, and carry out other municipal services.This dataset contains municipality-submitted parcel data along with previously developed parcel data acquired through the Municipal Grants Project supported by the Maine Library of Geographic Information (MLGI). Grant recipient parcel data submissions were guided by standards presented to the MLGI Board on May 21, 2005,outlined in "Standards for Digital Parcel Files".GEOCODES is a table that lists standardized names and unique identifiers for Maine minor civil divisions and reservations, which represents the first official Standard Geographic Code endorsed and adopted by the Governor of Maine, on July 1, 1971.

This is SCAG 2019 Regional Land Use dataset developed for the final Connect SoCal 2024, the 2024-2050 Regional Transportation Plan/Sustainable Communities Strategy (RTP/SCS), including general plan land use, specific plan land use, zoning code, and existing land use at parcel-level (approximately five million parcels) for 197 local jurisdictions in the SCAG region.The regional land use dataset is developed (1) to aid in SCAG’s regional transportation planning, scenario planning and growth forecasting, (2) facilitate policy discussion on various planning issues, and (3) enhance information database to better serve SCAG member jurisdictions, research institutes, universities, developers, general public, etc. It is the most frequently and widely utilized SCAG geospatial data. From late 2019 to early 2020, SCAG staff obtained the 2019 parcel boundary GIS file and tax roll property information from county assessor’s offices. After months of data standardization and clean-up process, SCAG staff released the 2019 parcel boundary GIS files along with the 2019 Annual Land Use dataset in February 2021. In December 2021, SCAG staff successfully developed the preliminary dataset of the 2019 regional land use data and released the draft SCAG Data/Map Book in May 2022. The preliminary land use data was reviewed by local jurisdictions during the Local Data Exchange (LDX) process for Connect SoCal 2024. As a part of the final 2019 regional land use data development process, SCAG staff made every effort to review the local jurisdictions’ inputs and comments and incorporated any updates to the regional land use datasets. The products of this project has been used as one of the key elements for Connect SoCal 2024 plan development, growth forecasting, scenario planning, and SCAG’s policy discussion on various planning issues, as well as Connect SoCal key growth strategy analysis.Note: This dataset is intended for planning purposes only, and SCAG shall incur no responsibility or liability as to the completeness, currentness, or accuracy of this information. SCAG assumes no responsibility arising from use of this information by individuals, businesses, or other public entities. The information is provided with no warranty of any kind, expressed or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Users should consult with each local jurisdiction directly to obtain the official land use information.2019 SCAG Land Use Codes: LegendLand Use Description Single Family Residential1110 Single Family Residential 1111 High Density Single Family Residential (9 or more DUs/ac) 1112 Medium Density Single Family Residential (3-8 DUs/ac) 1113 Low Density Single Family Residential (2 or less DUs/ac)Multi-Family Residential1120 Multi-Family Residential 1121 Mixed Multi-Family Residential1122 Duplexes, Triplexes and 2- or 3-Unit Condominiums and Townhouses1123 Low-Rise Apartments, Condominiums, and Townhouses1124 Medium-Rise Apartments and Condominiums1125 High-Rise Apartments and CondominiumsMobile Homes and Trailer Parks1130 Mobile Homes and Trailer Parks1131 Trailer Parks and Mobile Home Courts, High-Density1132 Mobile Home Courts and Subdivisions, Low-DensityMixed Residential1140 Mixed Residential1100 ResidentialRural Residential 1150 Rural ResidentialGeneral Office1210 General Office Use 1211 Low- and Medium-Rise Major Office Use 1212 High-Rise Major Office Use 1213 SkyscrapersCommercial and Services1200 Commercial and Services1220 Retail Stores and Commercial Services 1221 Regional Shopping Center 1222 Retail Centers (Non-Strip With Contiguous Interconnected Off-Street Parking) 1223 Retail Strip Development1230 Other Commercial 1231 Commercial Storage 1232 Commercial Recreation 1233 Hotels and MotelsFacilities1240 Public Facilities1241 Government Offices1242 Police and Sheriff Stations1243 Fire Stations1244 Major Medical Health Care Facilities1245 Religious Facilities1246 Other Public Facilities1247 Public Parking Facilities1250 Special Use Facilities1251 Correctional Facilities1252 Special Care Facilities1253 Other Special Use FacilitiesEducation1260 Educational Institutions1261 Pre-Schools/Day Care Centers1262 Elementary Schools1263 Junior or Intermediate High Schools1264 Senior High Schools1265 Colleges and Universities1266 Trade Schools and Professional Training FacilitiesMilitary Installations1270 Military Installations1271 Base (Built-up Area)1272 Vacant Area1273 Air Field1274 Former Base (Built-up Area)1275 Former Base Vacant Area1276 Former Base Air FieldIndustrial1300 Industrial 1310 Light Industrial1311 Manufacturing, Assembly, and Industrial Services1312 Motion Picture and Television Studio Lots1313 Packing Houses and Grain Elevators1314 Research and Development1320 Heavy Industrial1321 Manufacturing1322 Petroleum Refining and Processing1323 Open Storage1324 Major Metal Processing1325 Chemical Processing1330 Extraction1331 Mineral Extraction - Other Than Oil and Gas1332 Mineral Extraction - Oil and Gas1340 Wholesaling and WarehousingTransportation, Communications, and Utilities1400 Transportation, Communications, and Utilities 1410 Transportation1411 Airports1412 Railroads1413 Freeways and Major Roads1414 Park-and-Ride Lots1415 Bus Terminals and Yards1416 Truck Terminals1417 Harbor Facilities1418 Navigation Aids1420 Communication Facilities1430 Utility Facilities1431 Electrical Power Facilities1432 Solid Waste Disposal Facilities1433 Liquid Waste Disposal Facilities1434 Water Storage Facilities1435 Natural Gas and Petroleum Facilities1436 Water Transfer Facilities 1437 Improved Flood Waterways and Structures1438 Mixed Utilities1440 Maintenance Yards1441 Bus Yards1442 Rail Yards1450 Mixed Transportation1460 Mixed Transportation and UtilityMixed Commercial and Industrial1500 Mixed Commercial and IndustrialMixed Residential and Commercial1600 Mixed Residential and Commercial 1610 Residential-Oriented Residential/Commercial Mixed Use 1620 Commercial-Oriented Residential/Commercial Mixed UseOpen Space and Recreation1800 Open Space and Recreation 1810 Golf Courses 1820 Local Parks and Recreation 1830 Regional Parks and Recreation 1840 Cemeteries 1850 Wildlife Preserves and Sanctuaries 1860 Specimen Gardens and Arboreta 1870 Beach Parks 1880 Other Open Space and Recreation 1890 Off-Street TrailsAgriculture2000 Agriculture2100 Cropland and Improved Pasture Land2110 Irrigated Cropland and Improved Pasture Land2120 Non-Irrigated Cropland and Improved Pasture Land2200 Orchards and Vineyards2300 Nurseries2400 Dairy, Intensive Livestock, and Associated Facilities2500 Poultry Operations2600 Other Agriculture2700 Horse RanchesVacant3000 Vacant3100 Vacant Undifferentiated3200 Abandoned Orchards and Vineyards3300 Vacant With Limited Improvements3400 Beaches (Vacant)1900 Urban VacantWater4000 Water4100 Water, Undifferentiated4200 Harbor Water Facilities4300 Marina Water Facilities4400 Water Within a Military Installation4500 Area of Inundation (High Water)Specific Plan7777 Specific PlanUnder Construction1700 Under ConstructionUndevelopable or Protected Land8888 Undevelopable or Protected LandUnknown9999 Unknown