Loudoun County Parcel X,Y coordinates table. Available in Latitude and Longitude decimal degrees and Virginia State Plane North.

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2016 and 2019 along the Florida Reef Tract (FRT) from Miami to Key West within a 939.4 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Fehr and others (2021) derived from an elevation-change analysis between two elevation datasets acquired in 2016/2017 and 2019 using the methods of Yates and others (2017). Most of the elevation data from the 2016/2017 time period were collected during 2016, so as an abbreviated naming convention, we refer to this time period as 2016. Due to file size limitations, the elevation-change data was divided into five blocks. A seafloor stability threshold was determined for the 2016-2019 FRT elevation-change datasets based on the vertical uncertainty of the 2016 and 2019 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (total of 235,153,117 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2016 to 2019 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created for each block at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and 14 habitat types found along the FRT. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS Pro map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files for each block; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068. Coral restoration locations were provided by Mote Marine Laboratory under Special Activity License SAL-18-1724-SCRP.

This map shows the bike paths of all Pace rides in 2018 in the form of XY coordinate points. The NSC (Neighborhood Service Center) Quadrant feature layer lays underneath the point layer as to give a visual division of activity in each of the four quadrants of Rochester.Note: depending on the basemap you choose, you may have to zoom out and locate to Rochester.

Points are digitized by use of the 1998 orthophotos as a backdrop and do not represent the exact location of one or any actual signal heads. The general intersection where the signal is located is shown. Lineage: August 25, 2010 - loaded into EDITSDE geodatabase. XY and attribute data came from tables in database traffic_signals.mdb on Mapguide server: Slights_Sta, TrafficSignalsTable and PlansTable. Database was the backend for a Mapguide digi app used by TDOT to add/delete signal data.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactJim RobinsonCity of Tucson Transportation and Mobility520-837-6734Jim.Robinson@tucsonaz.gov Update FrequencyUpdated nightly Monday through Friday

Abstract The dataset was derived by the Bioregional Assessment Programme. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains the hand-contoured potentiometric surfaces of the various aquifers within the Galilee subregion. Contour intervals are 20m. Purpose To depict the various thickness contours for the Aquifers which …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains the hand-contoured potentiometric surfaces of the various aquifers within the Galilee subregion. Contour intervals are 20m. Purpose To depict the various thickness contours for the Aquifers which lie within the Galilee subregion. Dataset History Created using the QLD DNRM bores waterlevel dataset [ff44f450-8fec-486b-b60d-0d53333a478d]. Contours were interpreted and drawn by hand based on the above source data well lithology information. These contours were then scanned and digitized into ArcGIS shape files. Data was then refined using the ArcGIS spatial analyst tool set - 'smooth contour tools' to smooth the contours. All edits and geoprocessing were performed using ESRI ArcGIS 10.3 software. QAQC: Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data. Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors. Dataset Citation Bioregional Assessment Programme (2015) GAL Group 2 contour data. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/803b0f5c-acb8-4adf-9ae1-3cb385ed4061. Dataset Ancestors Derived From QLD DNRM Galilee Mine Groundwater Bores - Water Levels

Abstract The dataset was derived by the Bioregional Assessment Programme from Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013. The source dataset ia identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Displays the original Hydstra measurement (HYDMEAS) tabular data records (as stored in the Hydstra software platform) in a GIS format for …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013. The source dataset ia identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Displays the original Hydstra measurement (HYDMEAS) tabular data records (as stored in the Hydstra software platform) in a GIS format for interpretation and analysis. Analysis completed on this dataset includes extracts to determine location and status of current monitoring bores: HYDMEAS - original tabular database file (dbf) showing groundwater levels HYDMEAS_XY_all - displays all original tabular data in GIS shapefile format HYDMEAS_unique_bores - shows one record for each unique bore station ID HYDMEAS_2008 - All HYDMEAS data from 2008 or later HYDMEAS_2008to2013_mulitple_reading - All HYDMEAS data from 2008 or later which has been monitored twice or more (in that period), produced to estimate groundwater level monitoring bores National Groundwater Information System (NGIS) data supplied as a comparison of HYDMEAS monitoring estimates. Hydstra is a water resources time series data management system developed by KISTERS Pty Ltd. Purpose Provide spatial distribution of groundwater level monitoring status and reading for New South Wales. Dataset History HYDMEAS - original tabular data HYDMEAS_XY_all - displays all original tabular data in GIS format - Displayed as XY in ArcGIS based on Lat and Long attributes and exported as a point shapefile HYDMEAS_unique_bores - shows one record for each unique bore ID - Dissolved HYDMEAS_XY_all based on STATION field HYDMEAS_2008 - All HYDMEAS data from 2008 or later - Selected based on DATE field HYDMEAS_2008to2013_mulitple_reading - All HYDMEAS data from 2008 or later which has been monitored twice or more (in that period), produced to estimate groundwater level monitoring bores - HYDMEAS_2008 dataset dissolved based on STATION and a count field added. Only bores with count of 2 or more were retained Dataset Citation Bioregional Assessment Programme (2014) GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/d414c703-aabd-43af-81e0-30aab4d9dfb1. Dataset Ancestors Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2004 and 2016 at Looe Key coral reef near Big Pine Key, Florida (FL), within a 16.37 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Yates and others (2019) derived from an elevation-change analysis between two elevation datasets acquired in 2004 and 2016 using the methods of Yates and others (2017). A seafloor stability threshold was determined for the 2004-2016 Looe Key elevation-change dataset based on the vertical uncertainty of the 2004 and 2016 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (4,086,712 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2004 to 2016 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and ten habitat types found at Looe Key. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068. Coral restoration locations were provided by Mote Marine Laboratory under Special Activity License SAL-18-1724-SCRP.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2002 and 2016 in the Upper Florida Keys (UFK) from Triumph Reef to Pickles Reef within a 242.4 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Murphy and others (2021) derived from an elevation-change analysis between two elevation datasets acquired in 2001/2002 and 2016/2017 using the methods of Yates and others (2017). Most of the elevation data from these two time periods were collected during 2002 and 2016, so as an abbreviated naming convention, we refer to this study time period as 2002-2016. A seafloor stability threshold was determined for the 2002-2016 UFK elevation-change dataset based on the vertical uncertainty of the 2002 and 2016 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (60,585,610 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2002 to 2016 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and 13 habitat types found in the UFK. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS Pro map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

Abstract This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains a geodatabase with point Feature Classes for each hydrogeological formation in the Galilee Basin subregion. These Feature Classes show the spatial distribution of Hydrochemistry data, which has …Show full descriptionAbstract This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains a geodatabase with point Feature Classes for each hydrogeological formation in the Galilee Basin subregion. These Feature Classes show the spatial distribution of Hydrochemistry data, which has been quality assessed for Total Dissolved Solid (TDS) measurement accuracy, for their respective hydrogeological formation. Purpose This dataset is used to show the spatial distribution of TDS measurements in each hydrogeological formation within the Gaililee Basin subregion. Dataset History Each Feature Class was created from its source spreadsheet found the in the dataset; GAL Hydrochemistry Formations QC for TDS v02, GUID: d70448e7-79b5-48a4-930f-b9492184ed5b. In ArcCatalog, a new 'XY Feature Class' was created from the '...._new$' sheet within the source dataset. Process: Right Click > Create Feature Class > From XY Table Input Fields X Field = Dec_long Y Field = Dec_lat Z Field = None Coordinate System of Input Coordinates = GCS_GDA_1994 Dataset Citation Bioregional Assessment Programme (XXXX) GAL Hydrochemistry Formations QC for TDS v02 GIS. Bioregional Assessment Derived Dataset. Viewed 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/109a21cd-a167-4320-84be-ab56cfc12cee. Dataset Ancestors Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204 Derived From QLD DNRM Hydrochemistry with QA/QC Derived From Natural Resource Management (NRM) Regions 2010 Derived From QLD Hydrochemistry QA QC GAL v02 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From GAL Hydrochemistry Formations QC for TDS v02 Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014 Derived From Bioregional Assessment areas v03 Derived From GAL Hydrochemistry Formations QC for TDS v01 Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From RPS Galilee Hydrogeological Investigations - Appendix tables B to F (original) Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) Derived From Carmichael Coal Mine and Rail Project Environmental Impact Statement Derived From QLD Department of Natural Resources and Mining Groundwater Database Extract 20131111

Boston Public Schools (BPS) schools for the school year 2018-2019. Updated September 2018.

The Board on Geographic Names has many files available for download. Please review the file format and metadata for specific information regarding each file. If you have questions or problems with downloading please view the Download Data section of the How Do I? page or if your question is not there, please send an email to gnis_manager@usgs.govIn preparation for the updating of GNIS, a last set of text files have been prepared. The text files will be the only way that users will have to access GNIS data until the new application comes online. All files are pipe-delimited text files and updated routinely. Data contains XY attributes and can be presented geospatially using add XY geometry into ArcGIS.For more information, visit the GNIS Page: https://www.usgs.gov/u.s.-board-on-geographic-names/download-gnis-data

This dataset contains an ESRI Geotiff with 1 meter cell size representing the bathymetry of the Mid Shelf Reef south of St. Thomas, US Virgin Islands.NOAA's NOS/NCCOS/CCMA Biogeography Team, in collaboration with NOAA vessel Nancy Foster and territory, federal, and private sector partners, acquired multibeam bathymetry data in the US Virgin Islands from 2/1/05 to 2/12/05. Data was acquired with a pole-mounted Reson 8101 ER multibeam echosounder (240 kHz) and processed by a NOAA contractor using CARIS HIPS v5.4 software. Data has all correctors applied (attitude, sound velocity) and has been reduced to mean lower low water (MLLW) using final approved tides and zoning from NOAA COOPS. Data is in UTM zone 20 north, datum NAD83. The processed CARIS data was used to generate a CARIS BASE surface based on swath angle. An ASCII XYZ file was exported from the BASE surface and opened in ESRI ArcMap 9 as an XY event. Then the ArcToolbox conversion tool 'Feature to Raster' was used to generate the final ESRI Geotiff.The project was conducted to meet IHO Order 1 and 2 accuracy standards, dependant on the project area and depth. All users should individually evaluate the suitability of this data according to their own needs and standards.

A detailed vegetation map of a 120m by 28m study site was published by Rudolph (1963). A paper copy of the original maps of this research was obtained from archives at the University of Ohio.The map was digitised into a GIS layer and converted to meters. In the field, the plot was found because some of the marking pegs were still present on site and aerial photographs were used to locate points. GPS was used to determine the real world coordinates of the plot location. The site was remapped using a one metre square grid and change analysis undertaken using a GIS. Rudolph's map classifies the cover of mosses, lichens and algae into four classes: Heavy (40-90%), Patch (10-40%), Scattered (less than 10%) and none (0%). The combination of these classes was used to describe the vegetation in 2004. Within each one metre square the percentage cover of mosses, lichens and algae were recorded. The x,y distance of the cell centre from the north west corner of the plot was also recorded together with a description of the surface rock, wetness and percentage under snow. Vegetation change was able to be compared between 1962 and 2004. The changes in relation to the physical characteristics of the surface of the plot, such as rock type, wetness and slope were analysed. The data was converted to a Dbase file and then imported to a GIS point layer using the xy location as the geographical coordinates. The vegetation was also described using relevee measurements to determine cover of vegetation. The grid was 20 x 10 cm (200 point relevee) and analysed to determine species association. The 2004 map was compared with the 1962 map with statistics generated that describe the change.

This dataset contains an ESRI Geotiff with 2 meter cell size representing the bathymetry of the southwest shore of La Parguera, Puerto Rico. NOAA's NOS/NCCOS/CCMA Biogeography Team, in collaboration with NOAA vessel Nancy Foster and territory, federal, and private sector partners, acquired multibeam bathymetry data in the US Virgin Islands and Puerto Rico from 3/21/06 to 4/2/06. Data was acquired with a hull-mounted Kongsberg Simrad EM 1002 multibeam echosounder (95 kHz) and processed by a NOAA contractor using CARIS HIPS software. Data has all correctors applied (attitude, sound velocity) and has been reduced to mean lower low water (MLLW) using final approved tides and zoning from NOAA COOPS. Data is in UTM zone 19 north, datum NAD83. The processed CARIS data was used to generate a CARIS BASE surface based on swath angle. An ASCII XYZ file was exported from the BASE surface and opened in ESRI ArcMap 9 as an XY event. Then the ArcToolbox conversion tool 'Feature to Raster' was used to generate the final ESRI Geotiff.The project was conducted to meet IHO Order 1 and 2 accuracy standards, dependant on the project area and depth. All users should individually evaluate the suitability of this data according to their own needs and standards.

This map shows the bike paths of all Pace rides in 2019 in the form of XY coordinate points. The NSC (Neighborhood Service Center) Quadrant feature layer lays underneath the point layer as to give a visual division of activity in each of the four quadrants of Rochester.

This dataset contains an ESRI Geotiff with 3 meter cell size representing the bathymetry of the north shore of St. Croix, U.S. Virgin Islands. NOAA's NOS/NCCOS/CCMA Biogeography Team, in collaboration with NOAA vessel Nancy Foster and territory, federal, and private sector partners, acquired multibeam bathymetry data in the US Virgin Islands and Puerto Rico from 3/21/06 to 4/2/06. Data was acquired with a hull-mounted Kongsberg Simrad EM 1002 multibeam echosounder (95 kHz) and processed by a NOAA contractor using CARIS HIPS software. Data has all correctors applied (attitude, sound velocity) and has been reduced to mean lower low water (MLLW) using final approved tides and zoning from NOAA COOPS. Data is in UTM zone 20 north, datum NAD83. The processed CARIS data was used to generate a CARIS BASE surface based on swath angle. An ASCII XYZ file was exported from the BASE surface and opened in ESRI ArcMap 9.1 as an XY event. Then the ArcToolbox conversion tool 'Feature to Raster' was used to generate the final ESRI Geotiff. The project was conducted to meet IHO Order 1 and 2 accuracy standards, dependent on the project area and depth. All users should individually evaluate the suitability of this data according to their own needs and standards.

Snapshot of all Yield and Yield Ahead Signs in West Virginia as extracted by Mutcdname from an overall Sign Dataset. Datasets include RouteID, SignID, County Code, Route Numbered, Sub Route Number, Sign System, supplemental code, Supplemental Description, Direction, Milepoint, Number of Signs, Location, Mutcdname and Mutcode, Mutcdcat, Text, County, Photo URL, and XY Coordinates. Data is current as of 2015 and is updated as needed. Coordinate System: NAD_1983_UTM_Zone_17N

This dataset contains an ESRI Geotiff with 2 meter cell size representing the bathymetry of Grammanik Bank south of St. Thomas, US Virgin Islands.NOAA's NOS/NCCOS/CCMA Biogeography Team, in collaboration with NOAA vessel Nancy Foster and territory, federal, and private sector partners, acquired multibeam bathymetry data in the US Virgin Islands from 2/1/05 to 2/12/05. Data was acquired with a pole-mounted Reson 8101 ER multibeam echosounder (240 kHz) and processed by a NOAA contractor using CARIS HIPS v5.4 software. Data has all correctors applied (attitude, sound velocity) and has been reduced to mean lower low water (MLLW) using final approved tides and zoning from NOAA COOPS. Data is in UTM zone 20 north, datum NAD83. The processed CARIS data was used to generate a CARIS BASE surface based on swath angle. An ASCII XYZ file was exported from the BASE surface and opened in ESRI ArcMap 9 as an XY event. Then the ArcToolbox conversion tool 'Feature to Raster' was used to generate the final ESRI Geotiff.The project was conducted to meet IHO Order 1 and 2 accuracy standards, dependant on the project area and depth. All users should individually evaluate the suitability of this data according to their own needs and standards.

Authoritative Points-of-Interest layer for King County. Contains over 45 different domain classes showing locations and names for a range of different features. Some classes of features are extensively documented (such as school sites and hospitals) while other classes represent a selected set of all the features of that type (such as hotels and shopping centers). Each feature is represented by a single point describing the centroid of the feature. Multiple points for a single XY coordinate may exist if more than one domain is represented. In other words, a single feature may represent a hospital, but that same point may be represented by another feature (i.e., record) described as a Public Health Clinic.