The National Geodetic Survey (NGS), an office of NOAA's National Ocean Service, manages a network of Continuously Operating Reference Stations (CORS) that provide Global Navigation Satellite System (GNSS) data consisting of carrier phase and code range measurements in support of three dimensional positioning, meteorology, space weather, and geophysical applications throughout the United States, its territories, and a few foreign countries. Surveyors, GIS users, engineers, scientists, and the public at large that collect GPS or GNSS data can use CORS data to improve the precision of their positions. CORS enhanced post-processed coordinates approach a few centimeters relative to the National Spatial Reference System, both horizontally and vertically. The CORS network is a multi-purpose cooperative endeavor involving government, academic, and private organizations. The sites are independently owned and operated. Each agency shares their data with NGS, and NGS in turn analyzes and distributes the data free of charge. As of August 2016, the CORS network provides data from more than 1,950 active sites. CORS data holdings to include decommissioned stations, comes to a total of 2,674 sites. These sites are contributed by over 230 different organizations, and the network continues to expand.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

An Exit point layer (excluding NYC) suitable for use in a GIS. For more information about the SAM Program, please visit: https://gis.ny.gov/streets-addresses. This map service is available to the public. Spatial Reference of Source Data: NAD_1983_UTM_Zone_18N. Spatial Reference of Map Service: WGS 1984 Web Mercator Auxiliary.

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. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

This dataset provides a comprehensive geospatial reference of global airports, capturing critical identification and locational metadata. It offers researchers, travel professionals, and data analysts a robust resource for airport-related spatial and temporal investigations.

Key features of this dataset include:

• Comprehensive coverage of 6,671 unique airports
• 5 critical attributes for precise airport identification
• Global geographical representation
• Standardized timezone information
• International airport code standards

This dataset is ideal for:

• Geographic information system (GIS) projects
• Transportation network analysis
• Aviation industry research
• Global positioning and routing studies

This dataset contains the results of the reference rot analysis performed over the catalogues registered in the INSPIRE Discovery Services of EU and EFTA countries.

This is a link to the New Jersey Office of GIS Geospatial Data Portal.

Services:2015_Parking (FeatureServer)AGO_MAP_2019 (FeatureServer)Bike_Racks_2020 (FeatureServer)BikeParking2017 (FeatureServer)Chicago_Central_Business_District (FeatureServer)Chicago_Wards_hosted (FeatureServer)ChicagoMayHwys (FeatureServer)Control_Points_Interlockings (FeatureServer)ControlPoints_Interlockings (FeatureServer)Cook_County_Districts_hosted (FeatureServer)CTA_Bus_Routes (FeatureServer)CTA_Bus_Routes_2019 (FeatureServer)cta_rail_lines (FeatureServer)CTABusRoutes2019 (FeatureServer)FRA_Crossings (FeatureServer)FreightRailroads (FeatureServer)Grade_Crossings (FeatureServer)Illinois_House_Districts (FeatureServer)Illinois_Senate_Districts (FeatureServer)Lines_COVID19 (FeatureServer)Metra_Bridges (FeatureServer)Metra_facilities (FeatureServer)metra_lines_2018 (FeatureServer)Metra_Routes_Test (FeatureServer)metra_stations_2018 (FeatureServer)MetraLines_2016 (FeatureServer)MetraLines2017 (FeatureServer)MetraLines2019_CreateRoutes (FeatureServer)MetraPoliceBeats (FeatureServer)MetraStations2017new (FeatureServer)Municipalities (FeatureServer)NICTD_South_Shore_Line (FeatureServer)NICTD_Stations (FeatureServer)Pace_ParkNRide_Facilities (FeatureServer)Pace_Routes_03_25_2019 (FeatureServer)PaceRoutes2020 (FeatureServer)Parking_Lots_2016 (FeatureServer)parking_lots_2017 (FeatureServer)Parking_Survey_2018_AGO_Published (FeatureServer)Parking_Survey_2018_Final (FeatureServer)Parking_Survey_2019_Final (FeatureServer)ParkingLots2017 (FeatureServer)Police_Beats_2020_Draft (FeatureServer)Police_tows (FeatureServer)Six_County_Service_Area (FeatureServer)Stations_COVID19 (FeatureServer)Tie_Substations (FeatureServer)TrainsPerDay (FeatureServer)US_Congressional_Districts (FeatureServer)Yards_Points (FeatureServer)yards_points_2019 (FeatureServer)Yards_Polygons (FeatureServer)yards_polygons_2018 (FeatureServer)

Reference parcels are continuous agricultural parcels delimited by the boundaries of stable, nature-identifiable objects or by the boundaries of real estate (minimum area of the reference parcel is 0.30 ha)

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This data supports the paper entitled "Mapping the landscape of geospatial data citations". The dataset covers geospatial data-intensive research papers published between 2015-2018 retrieved using Scopus. The article's citations were assessed for data citation occurances, and coded using a data citation classification. Data were enhanced and linked to subject coverage and journal policy status information using Excel & SPSS. For more information about how the data were created and coded please review the 'Methodology' section of the paper. More information is provided below, including supplemental documentation and related publications. Abstract (paper) ABSTRACT Data citations, similar to article and other research citations, are important references to research data that underlie published research results. In support of open science directives, these citations must adhere to specific conventions in terms of consistency of both placement within an article, and the actual availability or access to research data. To better understand the level to which geospatial research data are currently cited, we undertook a study to analyse the rate of data citation within a set of data-intensive geospatial research articles. After analysing 1717 scholarly articles published between 2015 and 2018, we found that very few, or 78 (5%), meaningfully cited primary or secondary geospatial data sources in the cited references section of the article. Even fewer researchers, only 25 or 1.5%, were found to have cited data using a DOI. Given the relatively low data citation rate, a focus on contributing factors including barriers to citing geospatial data is needed. And while open sharing requirements for geospatial data may change over time, driving data citation as a result, understanding benchmarks for data citation for monitoring purposes is useful.

Ground reference data in the form of ecogeomorphic evaluations, topographic survey measurements, and geotagged photographs were collected at four areas of interest (AOI) across Fire Island National Seashore (FIIS), NY, USA September 16-19, 2024, that document site conditions. The overall goals of USGS personnel for the data collection were to: (1) collect ground reference data that assist in the visual interpretation of aerial imagery to produce maps that show changes on the island over time (e.g. land cover changes), and (2) collect ground reference data that will be used to train conditional probability distributions among variables for a Bayesian network that would predict landcover/ecogeomorphic state based on elevation, plant functional type, and distance from ocean shoreline.

This file is part of the MatchingLand testbed and contains the datasets of initial group (point, line, and area) and the list of matching pairs in plain text. The initial group of datasets encompasses the data selected from original sources of Spanish mapping agencies at scales 1:25,000 and 1:10,000. The datasets are in Shapefile format and coordinate reference system EPSG:32628.

1) Whenever the word “state” is used below, it should be taken to mean “state or territory”2) The intent of this template is to augment, not fully replace, existing state laws dealing with a state-specific coordinate system and its relationship to existing or prior datums of the National Spatial Reference System (NSRS).3) The National Geodetic Survey (NGS) will release a new State Plane Coordinate System of 2022 (SPCS2022) as part of the release of the new geometric reference frames in 2022. As such, it is imperative that each state do the following:Ensure that any changes from the 1983 SPCS which the majority of geospatial professionals in the state wish to make, be agreed at the state level and communicated to NGS, prior to deadlines specified in SPCS2022 policy and procedures, andEnsure that any law naming the state-specific coordinate system contains a definition of how that state-specific coordinate system relates to the SPCSFor example, if Michigan wishes to legislate that the “Michigan Plane Coordinate System” be used in the state of Michigan, then the law should specify that the “Michigan Plane Coordinate System” is identical to (or in some other way, defined in the law, related to) the “Michigan portion of the State Plane Coordinate System as defined by the National Geodetic Survey”.Adhere to NGS policy and procedures for requesting or proposing characteristics of SPCS2022 in their state. SPCS2022 policy, procedures, and associated forms are available at https://geodesy.noaa.gov/SPCS/policy.shtml.

In Spring 2019, with funding from several State partners, MassGIS contracted with the U.S. Geological Survey for statewide, 15 cm resolution, 16-bit, 4-band (RGB-IR) digital orthophotos. The data were delivered in early 2020 as 10,218 individual 1,500 m x 1,500 m tiles in a GeoTIFF format. To allow for easier distribution of this free imagery, MassGIS compressed the GeoTIFFS into the JPEG 2000 format, which retains the IR band. The tile naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the southwest corner of the tile.Project specifications are based on the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. The data were developed based on a horizontal projection/datum of NAD 1983 2011 UTM zones 18N and 19N meters and a vertical projection/datum of NAVD 88 (GEOID 12B) meters.This digital orthoimagery was created to provide easily accessible geospatial data which are readily available to enhance the capability of federal, state, and local emergency responders, as well as to plan for homeland security efforts. These data also support The National Map.These images can serve a variety of purposes, from general planning to field reference for spatial analysis, to a tool for data development and revision of vector maps. The imagery can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software and web-based maps.This image service was created using JPEG 2000 versions of the imagery that MassGIS converted from GeoTiffs and distributes online.For more information see the imagery's MassGIS metadata page.

This fileset contains geo-referenced data files. Latitude and longitude are WGS-84 unless described otherwise.

The U.S. National Grid (USNG) provides an efficient way to identify locations at different levels of detail anywhere in the United States. It can increase the usefulness and interoperability of printed maps and location-based services, such as global positioning systems (GPS). It is based on a universally defined geographic coordinate and grid system. For more information about the USNG, see: https://usngcenter.org

A set of reference maps for Minnesota based on the 10K USNG grid shows the location of schools, hospitals, fire and police stations, roads and political boundaries over a street or an air photo background. The maps can be viewed online or downloaded in GeoPDF format.

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.

We converted the photointerpreted data into a GIS-usable format using three fundamental processes; (1) geo-reference, (2) digitize, and (3) database enhancement. The resulting map products are two ArcInfo coverages (the Yellow River Unit and environs and the Sny Magill Unit and environs), each projected in UTM, Zone 15, using NAD83.

This dataset is the product of a geospatial interpolation using groundwater-level data obtained from a U.S. Geological Survey (USGS) synoptic survey of 129 groundwater wells in Fauquier County, VA from October 29 through November 2, 2018 and selected points from the National Hydrography Dataset (NHD) to represent equal-altitude contour lines of groundwater levels in 50-foot intervals. Methodology is detailed in USGS SIR 2022-5014 "Groundwater-level contour map of Fauquier County, VA, October - November 2018." Attributes include groundwater-level altitude in both decimal feet and meters. Horizontal coordinates are referenced to the geographic coordinate system North American Datum of 1983 (NAD 1983) and groundwater-level altitudes are referenced to the National Geodetic Vertical Datum of 1929 (NGVD 29). The projected coordinate system is Albers Conic Equal Area with a central meridian of -96.0, standard parallels of 29.5 and 45.5, and a latitude of origin of 23.0.