Configure this Atlas Instant App with your organization's OneMap SDI groups containing data layers and maps.

Data set: The name of the data set; Markers/chr: Number of markers simulated on each chromosome; Total markers: The total number of markers; Genetic map: Number of markers in the genetic map after filtering for informative markers (and the corresponding percentage of all simulated markers); Markers/LG: The average number of markers on each LG (and the marker density range).

The Digital Geomorphic-GIS Map of the Avon Area (1:24,000 scale 2007 mapping), North Carolina is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (avon_geomorphology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (avon_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (avon_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (caha_fora_wrbr_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (caha_fora_wrbr_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (avon_geomorphology_metadata_faq.pdf). Please read the caha_fora_wrbr_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: North Carolina Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (avon_geomorphology_metadata.txt or avon_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This web service depicts raster contour lines that are generated on-the-fly from 3-ft. NC Dept. of Public Safety DEMs using the ArcGIS contour function. They are created for visualization and have been smoothed for a more cartographic-pleasing appearance. These contour lines do not have elevation values attached to them. However, if displayed in a GIS application, an "identify" on the map will display the elevation value of the contour based on the 3-ft. DEM source. This contour layer can be overlaid on a map and provide information regarding terrain without obscuring the underlying data. The contour interval is 2 ft.The DEMs these raster contours are based on can be downloaded from the Direct Data Downloads section on the NCOneMap.gov website. Unsmoothed vector contour lines can also be downloaded.

AddressNC has been prioritized by the North Carolina Geographic Information Coordinating Council (GICC) as a critical framework dataset. The AddressNC Program runs parallel to and is derived from the North Carolina 911 Board Next Generation 911 (NG911) Program.  Address data has been identified as mission critical for validation and accurate call routing within NG911 and the AddressNC Program completes a full-circle approach of address maintenance and sustainability through applied enhancements and quality control beyond 911 requirements.  A primary goal of AddressNC is to continually develop and maintain quality address points on a continuous cycle through updates published in NG911. Various agencies in federal, state, and local government can benefit by applying practical applications of quality addressing in their own programs, negating the need to rely on outdated statewide addressing data and/or using paid address data sets from third party sources.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

Feature layer generated from running the Join Features solution

Abstract copyright UK Data Service and data collection copyright owner. This project dealt with the phonetic details of intonation in Dutch and English. It focused on the alignment of intonational targets (e.g. local peaks and valleys) with the vowels and consonants in speech. Limited past research had suggested that this is systematic, but the factors that affect it are not well understood. The depositor's earlier research suggested that in many cases intonation targets are anchored to specific sounds (e.g. the beginning of the vowel following a stressed syllable). This kind of precision was rather unexpected, because investigators have concentrated on more variable effects (e.g. the closer a target is to the end of a word, the earlier it is aligned). The main goal of this project was to determine how general this anchoring is, what kind of landmarks (consonants, vowels, word ends, etc.) can serve as anchors, and how much the alignment of anchored targets can be affected by more variable factors. One practical motivation for this research was to provide the basic knowledge for improvements to synthetic speech. Most of the empirical research of the proposed project consisted of experiments in both English and Dutch, in which carefully selected sentences were read aloud and detailed acoustical measurements made of the speech. The depositor also studied short (5-10 minute) dialogues spoken under somewhat controlled conditions these are the Map Task dialogues deposited in this dataset. English and Dutch were chosen because their sound structures are similar enough that conclusions can be generalised from one language to the other, yet different enough that certain kinds of experimental controls can be used in one language which would be impossible in the other. Also, both languages support important speech technology industries. Main Topics: This corpus of natural Dutch conversation was collected as part of a project primarily concerned with the phonology and phonetics of intonation. The Map Task procedure for collecting spontaneous speech was used. The Map Task is a widely used tool in the study of dialogue, because it allows researchers to study conversations which are completely spontaneous and yet remarkably predictable and consistent. The task works as follows: the two participants to the conversation each have a map showing a variety of pictured landmarks with names like shepherd's hut or Green Mountain. The maps may differ slightly in detail; crucially, one map (the instruction giver's map) has a route marked on it; and the other (the instruction follower's map) does not. Neither speaker can see the other's map, and in some versions of the task (but not this one) the speakers cannot see each other. The task is for the instruction giver to explain to the instruction follower where the route passes, referring to the various landmarks along the way, accurately enough that the instruction follower can reproduce the route on his or her own map. The basic reference on the Map Task is Anderson et al, (1991), The HCRC Map Task Corpus, Language and Speech 34, 351-366. Further information on the Map Task is available at: http://www.hcrc.ed.ac.uk/dialogue/maptask.html The point of using the Map Task was to obtain natural productions of certain intonation patterns (e.g. various kinds of question intonation) which are difficult to obtain in reading experiments without explicitly instructing the speakers how to speak (and sometimes not even then). The most important manipulation of the maps was to select landmark names that manifested the phonological structures that the depositor was interested in, and that contained consonant types which would permit easy analysis of pitch patterns. However, the basic conversational task was unaffected by these manipulations, and conversations in the corpus are entirely comparable to those recorded in various languages elsewhere. So far as the depositor is aware, no other Map Task corpus exists in Dutch. The conversations were recorded at the phonetics laboratory of the University of Nijmegan on 5 February 1999 (day 1) and 8 February 1999 (day 2). In both cases a complete quad (4 speakers, 8 conversations) was recorded. The speakers were all students at the university. The maps were based on maps from the original HCRC Map Task. The distribution of the landmarks and the route on the giver's map were identical to the originals, but the actual names of the landmarks were in Dutch and in most cases the pictures had to be adapted as well.

description: The Ouray National Wildlife Refuge (ONWR) was established in 1960 as an inviolate sanctuary for migratory birds and any other management purpose. In 2000, the Refuge published a Comprehensive Conservation Plan in accordance with the 1997 National Wildlife Refuge Improvement Act. The plan shifted the Refuge s emphasis toward ecosystem-based management of all resident and migratory species. Refuge and Regional staff asked that a detailed and accurate vegetation map be developed for planning and for managing the Refuge effectively. The Bureau of Reclamation s Remote Sensing and Geographic Information Group (RSGIS) was contracted by US Fish and Wildlife Service to map vegetation and land-use classes at ONWR using remote sensing and GIS technologies originally developed for the National Park Service s Vegetation Mapping Program. The diverse vegetation and complicated land-use history of Ouray National Wildlife Refuge presented a unique challenge to mapping vegetation at the plant association level of the US National Vegetation Classification. To meet this challenge, the project consisted of two linked phases: (1) vegetation classification and (2) digital vegetation map production. To classify the vegetation, we sampled representative plots located throughout the 14,025-acre (5676 ha) project area. Analysis of the plot data using ordination and clustering techniques yielded 58 distinct plant associations. To produce the digital map, we used a combination of new color-infrared aerial photography and fieldwork to interpret the complex patterns of vegetation and land-use at ONWR. Eighty-one map units were developed and the vegetation units matched to the corresponding plant associations. The interpreted map data were converted to a GIS database using ArcInfo. Draft maps created from the vegetation classification were field-tested and revised before an independent ecologist conducted an assessment of the map s accuracy. The accuracy assessment revealed an overall database accuracy of 75.2%. Products developed for the Ouray National Wildlife Refuge Vegetation Mapping Project include the final report, vegetation key, map accuracy assessment results and contingency table, and photo interpretation key; spatial database coverages of the vegetation map, vegetation plots, accuracy assessment sites, and flight line index; digital photos (scanned from 35mm slides) of each vegetation type; graphics of all spatial database coverages; Federal Geographic Data Committee-compliant metadata for all spatial database coverages and field data. 12 In addition, the Refuge and USFWS copies of this report contain original aerial photographs of the project area; digital data files and hard copy data sheets of the observation points, vegetation field plots, and accuracy assessment sites; original slides of each vegetation type.; abstract: The Ouray National Wildlife Refuge (ONWR) was established in 1960 as an inviolate sanctuary for migratory birds and any other management purpose. In 2000, the Refuge published a Comprehensive Conservation Plan in accordance with the 1997 National Wildlife Refuge Improvement Act. The plan shifted the Refuge s emphasis toward ecosystem-based management of all resident and migratory species. Refuge and Regional staff asked that a detailed and accurate vegetation map be developed for planning and for managing the Refuge effectively. The Bureau of Reclamation s Remote Sensing and Geographic Information Group (RSGIS) was contracted by US Fish and Wildlife Service to map vegetation and land-use classes at ONWR using remote sensing and GIS technologies originally developed for the National Park Service s Vegetation Mapping Program. The diverse vegetation and complicated land-use history of Ouray National Wildlife Refuge presented a unique challenge to mapping vegetation at the plant association level of the US National Vegetation Classification. To meet this challenge, the project consisted of two linked phases: (1) vegetation classification and (2) digital vegetation map production. To classify the vegetation, we sampled representative plots located throughout the 14,025-acre (5676 ha) project area. Analysis of the plot data using ordination and clustering techniques yielded 58 distinct plant associations. To produce the digital map, we used a combination of new color-infrared aerial photography and fieldwork to interpret the complex patterns of vegetation and land-use at ONWR. Eighty-one map units were developed and the vegetation units matched to the corresponding plant associations. The interpreted map data were converted to a GIS database using ArcInfo. Draft maps created from the vegetation classification were field-tested and revised before an independent ecologist conducted an assessment of the map s accuracy. The accuracy assessment revealed an overall database accuracy of 75.2%. Products developed for the Ouray National Wildlife Refuge Vegetation Mapping Project include the final report, vegetation key, map accuracy assessment results and contingency table, and photo interpretation key; spatial database coverages of the vegetation map, vegetation plots, accuracy assessment sites, and flight line index; digital photos (scanned from 35mm slides) of each vegetation type; graphics of all spatial database coverages; Federal Geographic Data Committee-compliant metadata for all spatial database coverages and field data. 12 In addition, the Refuge and USFWS copies of this report contain original aerial photographs of the project area; digital data files and hard copy data sheets of the observation points, vegetation field plots, and accuracy assessment sites; original slides of each vegetation type.

AddressNC has been prioritized by the North Carolina Geographic Information Coordinating Council (GICC) as a critical framework dataset. The AddressNC Program runs parallel to and is derived from the North Carolina 911 Board Next Generation 911 (NG911) Program.  Address data has been identified as mission critical for validation and accurate call routing within NG911 and the AddressNC Program completes a full-circle approach of address maintenance and sustainability through applied enhancements and quality control beyond 911 requirements.  A primary goal of AddressNC is to continually develop and maintain quality address points on a continuous cycle through updates published in NG911. Various agencies in federal, state, and local government can benefit by applying practical applications of quality addressing in their own programs, negating the need to rely on outdated statewide addressing data and/or using paid address data sets from third party sources.

The Digital Geomorphic-GIS Map of Cape Lookout National Seashore, North Carolina (1:10,000 scale 2008 mapping) is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (calo_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (calo_geomorphology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (calo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (calo_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (calo_geomorphology_metadata_faq.pdf). Please read the calo_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: East Carolina University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (calo_geomorphology_metadata.txt or calo_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:10,000 and United States National Map Accuracy Standards features are within (horizontally) 8.5 meters or 27.8 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The ESA Orthorectified Map-oriented (Level 1) Products collection is composed of MOS-1/1B MESSR (Multi-spectral Electronic Self-Scanning Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02. The products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the MOS Product Format Specification for further details. The collection consists of data products of the following type: MES_GEC_1P: Geocoded Ellipsoid GCP Corrected Level 1 MOS-1/1B MESSR products which are the default products generated by the MOS MESSR processor in all cases (where possible), with usage of the latest set of Landsat improved GCP (Ground Control Points). These are orthorectified map-oriented products, corresponding to the old MOS-1/1B MES_ORT_1P products with geolocation improvements. MESSR Instrument Characteristics Band Wavelength Range (nm) Spatial Resolution (m) Swath Width (km) 1 (VIS) 510 – 690 50 100 2 (VIS) 610 – 690 50 100 3 (NIR) 720 – 800 50 100 4 (NIR) 800 – 1100 50 100

Sentinel-1 performs systematic acquisition of bursts in both IW and EW modes. The bursts overlap almost perfectly between different passes and are always located at the same place. With the deployment of the SAR processor S1-IPF 3.4, a new element has been added to the products annotations: the Burst ID, which should help the end user to identify a burst area of interest and facilitate searches. The Burst ID map is a complementary auxiliary product. The maps have a validity that covers the entire time span of the mission and they are global, i.e., they include as well information where no SAR data is acquired. Each granule contains information about burst and sub-swath IDs, relative orbit and burst polygon, and should allow for an easier link between a certain burst ID in a product and its corresponding geographic location.

The data release for the geologic map of the Butte 1 degree x 2 degrees quadrangle, Montana, is a Geologic Map Schema (GeMS)-compliant version that updates the GIS files for the geologic map published in Montana Bureau of Mines and Geology Open File Report MBMG 363 (Lewis, 1998). The updated digital data present the attribute tables and geospatial features (points, lines and polygons) in the format that meets GeMS requirements. This data release presents the geologic map as shown on the plates and captured in geospatial data for the published map. Minor errors, such as mistakes in line decoration or differences between the digital data and the map image, are corrected in this version. The database represents the geology for the 4.4 million acre, geologically complex Butte 1 x 2 degrees quadrangle, at a publication scale of 1:250,000. The map covers parts of Deer Lodge, Granite, Jefferson, Lewis and Clark, Missoula, Powell, Ravalli, and Silver Bow Counties. These GIS data supersede ...

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary SphereExtent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaVisible Scale: 1:144,000 to 1:1,000Number of Features: 36,569,286Source: USDA Natural Resources Conservation ServicePublication Date: December 2021Data from the gSSURGO database was used to create this layer.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some mapunits have a null value for soil order, a custom script was used to populate this field using the Component Name (compname) and Mapunit Name (muname) fields. This field was created using the dominant soil order of each mapunit.Esri SymbologyHorizon TableEach map unit polygon has one or more components and each component has one or more layers known as horizons. To incorporate this field from the Horizon table into the attributes for this layer, a custom script was used to first calculate the mean value weighted by thickness of the horizon for each component and then a mean value of components weighted by the Component Percentage Representative Value field for each map unit. K-Factor Rock FreeEsri Soil OrderThese fields were calculated from the Component table using a model that included the Pivot Table Tool, the Summarize Tool and a custom script. The first 11 fields provide the sum of Component Percentage Representative Value for each soil order for each map unit. The Soil Order Dominant Condition field was calculated by selecting the highest value in the preceding 11 soil order fields. In the case of tied values the component with the lowest average slope value (slope_r) was selected. If both soil order and slope were tied the first value in the table was selected.Percent AlfisolsPercent AndisolsPercent AridisolsPercent EntisolsPercent GelisolsPercent HistosolsPercent InceptisolsPercent MollisolsPercent SpodosolsPercent UltisolsPercent VertisolsSoil Order - Dominant ConditionEsri Popup StringThis field contains a text string calculated by Esri that is used to create a basic pop-up using some of the more popular SSURGO attributes.Map Unit KeyThe Mapunit key field is found

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

This layer presents the Universal Transverse Mercator (UTM) zones of the world. The layer symbolizes the 6-degree wide zones employed for UTM projection.To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to World UTM Zones Grid.

Data for TOD (Transit-Oriented Development) Zone Change Next Gen Web App

The USGS National Hydrography Dataset (NHD) service from The National Map is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000 (or larger) scale and referred to as high resolution NHD, and the other based on 1:100,000 scale and referred to as medium resolution NHD. The NHD from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. The NHD is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map download client allows free downloads of public domain NHD data in either Esri File Geodatabase or Shapefile formats. For additional information on the NHD, go to https://nhd.usgs.gov/index.html.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Fifty-one map classes were developed to describe the COLM vegetation mapping project area. Of these, 26 are NVC-based vegetation map classes, four are geology map classes, seven are vegetated land use map classes and 14 are non-vegetated land-use map classes. Of the 26 vegetation map classes, 16 represent single NVC plant associations; the other 10 map classes contain multiple plant associations. One map class consists of point data representing seep and spring vegetation. It is contained in a separate coverage from the polygon map classes.