These parcel boundaries represent legal descriptions of property ownership, as recorded in various public documents in the local jurisdiction. The boundaries are intended for cartographic use and spatial analysis only, and not for use as legal descriptions or property surveys. Tax parcel boundaries have not been edge-matched across municipal boundaries.

A web map used to visualize available digital parcel data for Organized Towns and Unorganized Territories throughout the state of Maine. Individual towns submit parcel data on a voluntary basis; the data are compiled by the Maine Office of GIS for dissemination by the Maine GeoLibrary, and where available, the web map also includes assessor data contained in the Parcels_ADB related table.This web map is intended for use within the Maine Geoparcel Viewer Application; it is not intended for use as a standalone web map.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, sometimes many years apart, which affects the currency of Maine GeoLibrary parcels data. 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.

This dataset is designed to represent and identify the property boundaries in Lexington-Fayette County. The original dataset was created in late 1990's by a third party that converted existing paper maps to digital GIS files. The data has since been updated by georeferencing recorded plats for corrections and new additions. In cases where the plats do not appear accurate, aerial photos are utilized in attempt to properly locate the property lines. The only except for this process are changes to highway right-of-way in which calls are run from deeds. The geometry of this data is not of survey quality and should not be used for survey purposes. The data is intended for general reference purposes only.As part of the basemap data layers, the parcel boundary map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).

More MetadataData updated daily.A parcel is a tract or plot of land surveyed and defined by legal ownership. Data were compiled from plats and deeds recorded at the Clerk of the Court and from historic tax maps. Source material was digitized or the coordinates were entered into the database via ARC/INFO Coordinate Geometry (COGO). Digital data from engineering companies has also been incorporated for newer subdivisions. A MCPI number is used to identify each parcel, which is a unique ID number further explained below. Purpose: Parcels are used to support a variety of services including assessment, permitting, subdivision review, planning, zoning, and economic development. Parcel data were initially developed to replace existing tax maps. As a result, there are parcel polygons digitized from tax maps that do not represent land parcels but are taxable entities such as leaseholds or easements. Supplemental Information: 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: Parcels are updated on an hourly basis from recorded deeds and plats. Depending on volume and date of receipt of recordation information, data may be updated 2-3 weeks following recordation. 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. MCPI: 9 digit unique parcel ID that is a combination of: MAP, CELL, and PARCEL. MAP: 3 digit map number (001-701) corresponding with map tile index. CELL: 2 digit map grid location of parcel center; the grid is comprised of 1000 by 1000 ft grid cells numbered as rows and columns (Columns numbered > 5 6 7 8 9 0; Rows numbered > 1 2 3 4). PARCEL: 4 digit location of polygon center based on the 1927 Virginia State Plane coordinate grid where an easting and northing measurement is taken. example: 6654 from: E 2229668 N475545. The MAP, CELL, and PARCEL values of a parcel do not change when a parcel is altered by a boundary line adjustment or becomes residue from a subdivision. The MAP, CELL, and PARCEL values may therefore be inconsistent with the location of polygon center. MAP, CELL, and PARCEL values have been manually altered for some parcels to agree with other databases; as a result, not all parcels can be located by the MAP, CELL, and PARCEL values. Data Owner: Office of Mapping and Geographic Information

OSA web map to view State of Colorado property data

GIS Map view look up parcel information including owner, taxes, market value and more.Important Mailing Label Information:The "Mailing Labels" button is is copy of the Parcels Layer and is intended to be turned OFF on the map, and is there just for the "Public Notification" Widget. This widget obtains information on the pop-up of a selected layer to create "Mailing Labels." This said, this layer contains the Owners Mailing Address information. Below is Arcaded used to customize the pop-up:Made three custom Arcade Lines below: Proper($feature["OWNER_NAM1"]) + Proper($feature["OWNER_NAM2"])Proper($feature["OWNER_ADDR"])Proper($feature["OWNER_CITY"]) + ',' + $feature["OWNER_STAT"] + ',' + $feature["OWNER_ZIP"]Below is the custom pop-up:{expression/expr0}{expression/expr1}{expression/expr2}

FOR PLAT MAPS AND OTHER LAND DOCUMENTS, PLEASE VISIT THE COUNTY CLERK’S OFFICIAL RECORDS SEARCH: HTTPS://BEXAR.TX.PUBLICSEARCH.US.The Bexar County GIS Team does not have purview over plat maps and other land records. Please visit the Bexar County Clerk’s Official Records Search.

'USGS and Non USGS Agencies Aerial Photo Reference Mosaics inventory contains indexes to aerial photographs. The inventory contains imagery from various government agencies that are now archived at the USGS Earth Resources Observation and Science (EROS) Center. The film types, scales, and acquisition schedules differed according to project requirements. Low-, middle-, and high-altitude photographs were collected. '

Approximate boundaries for all land parcels in New Brunswick. The boundaries are structured as Polygons. The Property Identifier number or PID is included for each parcel.

Welcome to the StoryMap for the Westchester County Mobility & Transit Plan! Here, you’ll be able to explore the suggested changes to Bee-Line service. The StoryMap allows you to either scroll down through the story or navigate with the links in the ribbon above. Further below, you may also explore suggested recommendations by selecting a specific route or using the glider between existing and suggested maps. The content contained in this storymap provides the information that was previously hosted at westchestermobility.org.The ideas, route labels, and route names presented below are study suggestions and the County is currently coordinating an implementation process. Status updates, public meetings and hearings and roll-out schedule will be shared as that information becomes available.

Webmap of Allegheny municipalities and parcel data. Zoom for a clickable parcel map with owner name, property photograph, and link to the County Real Estate website for property sales information.

All 191 US highway streets from 1926 including plate icons and street line coordinates for plotting on a map. This is the US highways dataset, which differs from the Interstate and the State highways. The included coordinates folder allows users to plot individual highway streets on a map. For more details of use cases for this dataset, please take a look at the US Highway Street Visual Study and Analysis notebook.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16711385%2Fee470c62ad52b33f3fffd4525ba8ef2b%2Fhighways.png?generation=1703907203483360&alt=media" alt="">

Feature Description

• street_number: The street number of the highway. Format is US #, i.e. US 1.
• street_name: The street name of the highway.
• states: The states associated with the highway.
• formed: The year the highway was formed. yyyy format.
• removed: The year the highway was removed. yyyy format.
• length_(mi): The length of the highway in mile.
• length_(km): The length of the highway in km.
• southern_or_western_terminus: The southern or western terminus.
• northern_or_eastern_terminus: The northern or eastern terminus.
• wiki_link: The link to the highway's Wikipedia page.
• wiki_id: The ID associated with the highway's Wikipedia page.
• coordinates_name: The folder name containing the associated coordinates.
• icon_name: The plate image file name associated with the highway.
• notes: Notes about the highway.

Acknowledgements

• Source data were collected from Wikipedia.

California State Lands Commission Offshore Oil Leases in the vicinity of Santa Barbara, Ventura, and Orange County.The polygons in this layer show the position of Offshore Oil Leases as documented by former State Lands Senior Boundary Determination Officer, Cris N. Perez and as reviewed and updated by GIS and Boundary staff.Background: This layer represents active offshore oil and gas agreements in California waters, which are what remain of the more than 60 originally issued. These leases were issued prior to the catastrophic 1969 oil spill from Platform A in federal waters off Santa Barbara County, and some predate the formation of the Commission. Between 2010 and 2014, the bulk of the approximately $300 million generated annually for the state's General Fund from oil and gas agreements was from these offshore leases.In 1921, the Legislature created the first tidelands oil and gas leasing program. Between 1921 and 1929, approximately 100 permits and leases were issued and over 850 wells were drilled in Santa Barbara and Ventura Counties. In 1929, the Legislature prohibited any new leases or permits. In 1933, however, the prohibition was partially lifted in response to an alleged theft of tidelands oil in Huntington Beach. It wasn't until 1938, and again in 1955, that the Legislature would allow new offshore oil and gas leasing. Except for limited circumstances, the Legislature has consistently placed limits on the areas that the Commission may offer for lease and in 1994, placed the entirety of California's coast off-limits to new oil and gas leases. Layer Creation Process:In 1997 Cris N. Perez, Senior Boundary Determination Officer of the Southern California Section of the State Lands Division, prepared a report on the Commission’s Offshore Oil Leases to:A. Show the position of Offshore Oil Leases. B. Produce a hard copy of 1927 NAD Coordinates for each lease. C. Discuss any problems evident after plotting the leases.Below are some of the details Cris included in the report:I have plotted the leases that were supplied to me by the Long Beach Office and computed 1927 NAD California Coordinates for each one. Where the Mean High Tide Line (MHTL) was called for and not described in the deed, I have plotted the California State Lands Commission CB Map Coordinates, from the actual field surveys of the Mean High Water Line and referenced them wherever used. Where the MHTL was called for and not described in the deed and no California State Lands Coordinates were available, I digitized the maps entitled, “Map of the Offshore Ownership Boundary of the State of California Drawn pursuant to the Supplemental Decree of the U.S. Supreme Court in the U.S. V. California, 382 U.S. 448 (1966), Scale 1:10000 Sheets 1-161.” The shore line depicted on these maps is the Mean Lower Low Water (MLLW) Line as shown on the Hydrographic or Topographic Sheets for the coastline. If a better fit is needed, a field survey to position this line will need to be done.The coordinates listed in Cris’ report were retrieved through Optical Character Recognition (OCR) and used to produce GIS polygons using Esri ArcGIS software. Coordinates were checked after the OCR process when producing the polygons in ArcMap to ensure accuracy. Original Coordinate systems (NAD 1927 California State Plane Zones 5 and 6) were used initially, with each zone being reprojected to NAD 83 Teale Albers Meters and merged after the review process.While Cris’ expertise and documentation were relied upon to produce this GIS Layer, certain polygons were reviewed further for any potential updates since Cris’ document and for any unusual geometry. Boundary Determination Officers addressed these issues and plotted leases currently listed as active, but not originally in Cris’ report. On December 24, 2014, the SLA boundary offshore of California was fixed (permanently immobilized) by a decree issued by the U.S. Supreme Court United States v. California, 135 S. Ct. 563 (2014). Offshore leases were clipped so as not to exceed the limits of this fixed boundary. Lease Notes:PRC 1482The “lease area” for this lease is based on the Compensatory Royalty Agreement dated 1-21-1955 as found on the CSLC Insider. The document spells out the distinction between “leased lands” and “state lands”. The leased lands are between two private companies and the agreement only makes a claim to the State’s interest as those lands as identified and surveyed per the map Tract 893, Bk 27 Pg 24. The map shows the State’s interest as being confined to the meanders of three sloughs, one of which is severed from the bay (Anaheim) by a Tideland sale. It should be noted that the actual sovereign tide and or submerged lands for this area is all those historic tide and submerged lands minus and valid tide land sales patents. The three parcels identified were also compared to what the Orange County GIS land records system has for their parcels. Shapefiles were downloaded from that site as well as two centerline monuments for 2 roads covered by the Tract 893. It corresponded well, so their GIS linework was held and clipped or extended to make a parcel.MJF Boundary Determination Officer 12/19/16PRC 3455The “lease area” for this lease is based on the Tract No. 2 Agreement, Long Beach Unit, Wilmington Oil Field, CA dated 4/01/1965 and found on the CSLC insider (also recorded March 12, 1965 in Book M 1799, Page 801).Unit Operating Agreement, Long Beach Unit recorded March 12, 1965 in Book M 1799 page 599.“City’s Portion of the Offshore Area” shall mean the undeveloped portion of the Long Beach tidelands as defined in Section 1(f) of Chapter 138, and includes Tract No. 1”“State’s Portion of the Offshore Area” shall mean that portion of the Alamitos Beach Park Lands, as defined in Chapter 138, included within the Unit Area and includes Tract No. 2.”“Alamitos Beach Park Lands” means those tidelands and submerged lands, whether filled or unfilled, described in that certain Judgment After Remittitur in The People of the State of California v. City of Long Beach, Case No. 683824 in the Superior Court of the State of California for the County of Los Angeles, dated May 8, 1962, and entered on May 15, 1962 in Judgment Book 4481, at Page 76, of the Official Records of the above entitled court”*The description for Tract 2 has an EXCEPTING (statement) “therefrom that portion lying Southerly of the Southerly line of the Boundary of Subsidence Area, as shown on Long Beach Harbor Department {LBHD} Drawing No. D-98. This map could not be found in records nor via a PRA request to the LBHD directly. Some maps were located that show the extents of subsidence in this area being approximately 700 feet waterward of the MHTL as determined by SCC 683824. Although the “EXCEPTING” statement appears to exclude most of what would seem like the offshore area (out to 3 nautical miles from the MHTL which is different than the actual CA offshore boundary measured from MLLW) the 1964, ch 138 grant (pg25) seems to reference the lands lying seaward of that MHTL and ”westerly of the easterly boundary of the undeveloped portion of the Long Beach tidelands, the latter of which is the same boundary (NW) of tract 2. This appears to then indicate that the “EXCEPTING” area is not part of the Lands Granted to City of Long Beach and appears to indicate that this portion might be then the “State’s Portion of the Offshore Area” as referenced in the Grant and the Unit Operating Agreement. Section “f” in the CSLC insider document (pg 9) defines the Contract Lands: means Tract No. 2 as described in Exhibit “A” to the Unit Agreement, and as shown on Exhibit “B” to the Unit Agreement, together with all other lands within the State’s Portion of the Offshore Area.Linework has been plotted in accordance with the methods used to produce this layer, with record lines rotated to those as listed in the descriptions. The main boundaries being the MHTL(north/northeast) that appears to be fixed for most of the area (projected to the city boundary on the east/southeast); 3 nautical miles from said MHTL on the south/southwest; and the prolongation of the NWly line of Block 50 of Alamitos Bay Tract.MJF Boundary Determination Officer 12-27-16PRC 4736The “lease area” for this lease is based on the Oil and Gas Lease and Agreement as found on the CSLC insider and recorded August 17, 1973 in BK 10855 PG 432 Official Records, Orange County. The State’s Mineral Interests are confined to Parcels “B-1” and “B-2” and are referred to as “State Mineral Lands” comprising 70.00 Acres. The lessee each has a right to certain uses including but not limited to usage of utility corridors, 110 foot radius parcels surrounding well-sites and roads. The State also has access to those same roads per this agreement/lease. Those uses are allowed in what are termed “State Lands”-Parcel E and “Leased Lands” which are defined as the “South Bolsa Lease Area”-Parcel C (2 parcels) and “North Bolsa Lease Area”-Parcel D. The “State Lands”-Parcel E are actually 3 parcels, 2 of which are within road right-of-ways. MJF Boundary Determination Officer 12-28-16

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

This dataset is refreshed on a weekly basis from the datasets the team works on daily.Last update date: 20 November 2025.National Highways Operational Highway Boundary (RedLine) maps out the land belonging to the highway for the whole Strategic Road Network (SRN). It comprises two layers; one being the an outline and another showing the registration status / category of land of land that makes up the boundary. Due to the process involved in creating junctions with local highway authority (LHA) roads, land in this dataset may represent LHA highway (owned by National Highways but the responsibility of the LHA to maintain). Surplus land or land held for future projects does not form part of this dataset.The highway boundary is derived from:Ordnance Survey Mastermap Topography,HM Land Registry National Polygon Service (National Highway titles only), andplots researched and digitised during the course of the RedLine Boundary Project.The boundary is split into categories describing the decisions made for particular plots of land. These categories are as follows:Auto-RedLine category is for plots created from an automated process using Ordnance Survey MasterMap Topography as a base. Land is not registered under National Highways' name. For example, but not limited to, unregistered ‘ancient’ highway vested in Highways England, or bridge carrying highways over a rail line.NH Title within RedLine category is for plots created from Land Registry Cadastral parcels whose proprietor is National Highways or a predecessor. Land in this category is within the highway boundary (audited) or meets a certain threshold by the algorithm.NH Title outside RedLine category is for plots created in the same way as above but these areas are thought to be outside the highway boundary. Where the Confidence is Low, land in this category is yet to be audited. Where the Confidence is High, land in this category has been reviewed and audited as outside our operational boundary.National Highways (Technician) Data category is for plots created by National Highways, digitised land parcels relating to highway land that is not registered, not yet registered or un-registerable.Road in Tunnel category, created using tunnel outlines from Ordnance Survey MasterMap Topography data. These represent tunnels on Highways England’s network. Land is not registered under National Highways' name, but land above the tunnel may be in National Highways’ title. Please refer to the definitive land ownership records held at HM Land Registry.The process attribute details how the decision was made for the particular plot of land. These are as follows:Automated category denotes data produced by an automated process. These areas are yet to be audited by the company.Audited category denotes data that has been audited by the company.Technician Data (Awaiting Audit) category denotes data that was created by National Highways but is yet to be audited and confirmed as final.The confidence attribute details how confident you can be in the decision. This attribute is derived from both the decisions made during the building of the underlying automated dataset as well as whether the section has been researched and/or audited by National Highways staff. These are as follows:High category denotes land that has a high probability of being within the RedLine boundary. These areas typically are audited or are features that are close to or on the highway.Moderate category denotes land that is likely to be within the highway boundary but is subject to change once the area has been audited.Low category denotes land that is less likely to be within the highway boundary. These plots typically represent Highways England registered land that the automated process has marked as outside the highway boundary.Please note that this dataset is indicative only. For queries about this dataset please contact the GIS and Research Team.

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.

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

The Resources box to the right includes links to an Interactive Mapping App, an FTP site and a Data Dictionary that together provide access to compiled data from the primary ground-sampling programs managed by the Forest Analysis and Inventory Branch (FAIB). The following is a summary of what’s available in the two links: 1) The Interactive Mapping App provides a spatial view of FAIB ground plots with custom filters to enable selection of areas, BEC zones, species, TSA or plot types of interest. Once plots of interest are selected or filtered, an ‘export data’ button is available to download a plot summary file with limited attributes. 2) The Compiled Ground Plot FTP site contains tree- and plot-level compiled mensurational attributes for each ground plot across a series of repeated measurements. Both the PSP and non-PSP compilation outputs include a Data Dictionary that describes all the tables and attributes found in the downloadable files. FAIB ground-sampling programs include the Permanent Sample Plots (PSPs) that provide long term growth and yield information to support development and testing of growth-and-yield models. Active PSPs are the only plot type protected from harvesting. The Provincial Change Monitoring Inventory (CMI), Provincial Young Stand Monitoring (YSM) and National Forest Inventory (NFI) programs monitor the changes in growth, mortality, and forest health from statistically valid populations. Vegetation Resource Inventory (VRI) plots are used to audit and verify key spatial inventory attributes estimated during photo interpretation.

Context

This is a very simple dataset for a topographic representation project using Leaflet in a data science training course.

My project (as an example) is published here: Cascade Volcanoes

Content

The dataset is 82 rows long and 10 columns wide. It's a plain comma-separated value file with UNIX new-line characters.

Acknowledgements

The dataset information was drawn from Wikipedia.

The header image of the Black Buttes was also taken from Wikipedia.

Author: Ann Wurst, consultantGrade/Audience: high schoolResource type: activitySubject topic(s): physical geography, geographic thinkingRegion: worldStandards: Texas World Geography TEKS (3) Geography. The student understands how physical processes shape patterns in the physical environment. The student is expected to: (A) explain weather conditions and climate in relation to annual changes in Earth-Sun relationships; Objectives: Students will be able to show understandin of how physical processes shape patterns in the physical environment. Students will be able to explain climate in relation to annual changes in Earth-Sun relationships; Summary: Students will map the major world climates and connect climates to locations around the world.

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 silt content (siltdtotal) as defined by United States soil survey program. Silt content is estimated by percent weight of the <2mm portion of the soil.

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 (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: silttotal_r_0_cm_2D_QRF.tif

Indicates silt content (silttotal) 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