# Recovering layer (*.lyr) file that is missing shapefile?

Somebody in the office deleted a point shapefile that has a lyr file as well. How can I recover it? Is there a Script that allows me to recover it?

Assuming you know where that shapefile was located you should be able to recover it from either the Recycle Bin or a system backup. Once restored to its original location the layer file should once again be able to locate it.

If you need to point your layer file at a new data source you can add it to your Table of Contents, use the Source tab of its properties to locate the new source shapefile, and then use Save As Layer File to either overwrite the original or to create another one with a new name.

## Physiographic Zones of the Sea Floor for Vineyard and western Nantucket Sounds, Massachusetts (polygon shapefile, Geographic, WGS84)

Baldwin, Wayne, 2016, Physiographic Zones of the Sea Floor for Vineyard and western Nantucket Sounds, Massachusetts (polygon shapefile, Geographic, WGS84): Open-File Report 2016-1119, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

Baldwin, Wayne E. , Foster, David S. , Pendleton, Elizabeth A. , Barnhardt, Walter A. , Schwab, William C. , Andrews, Brian D. , and Ackerman, Seth D. , 2016, Shallow Geology, Sea-Floor Texture, and Physiographic Zones of Vineyard and western Nantucket Sounds, Massachusetts: Open-File Report 2016-1119, U.S. Geological Survey, Reston, VA.

West_Bounding_Coordinate: -71.029685 East_Bounding_Coordinate: -70.430489 North_Bounding_Coordinate: 41.561614 South_Bounding_Coordinate: 41.320506

Beginning_Date: 05-Sep-2001 Ending_Date: 31-Aug-2011 Currentness_Reference: ground condition of the source data that this interpretation is based on

Geospatial_Data_Presentation_Form: vector digital data

Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.000001. Longitudes are given to the nearest 0.000001. Latitude and longitude values are specified in Decimal degrees.

The horizontal datum used is D_WGS_1984.
The ellipsoid used is WGS_1984.
The semi-major axis of the ellipsoid used is 6378137.000000.
The flattening of the ellipsoid used is 1/298.257224.

VineyardNantucketSound_Pzones Physiographic zones shapefile for Vineyard and western Nantucket Sounds (Source: U.S. Geological Survey)

FID Internal feature number. (Source: ESRI)

Sequential unique whole numbers that are automatically generated.

Shape Feature geometry. (Source: Esri)

Coordinates defining the features.

Pzone_name Based on geologic maps produced for the Western Gulf of Maine (Kelley and others, 1996), the sea floor within the study area can be divided into geologic environments, or physiographic zones, which are delineated from sea-floor morphology and the dominant texture of surficial material. (Source: U.S. Geological Survey)

ValueDefinition
Rocky ZoneRocky zones are rugged areas of high bathymetric relief, characterized by fields of mounded boulders and cobble near the terminal moraines, to relatively flat, gravel-covered, and isolated till highs in central portions of the sounds. Water depths range from 1 to 30 m. Although boulders and coarse-grained sediment are found within all physiographic zones defined here, they dominate the sea floor in rocky rones.
Nearshore BasinNearshore basins are areas of shallow, low relief sea floor adjacent to the mainland and separated from offshore areas by islands and shoals. Water depths range from 0 to 35 m. Along their landward margins, the basin sediment merges with the intertidal zone, often a nearshore ramp, in a gradational contact.
Nearshore RampNearshore ramps are areas of gently sloping sea floor with generally shore-parallel bathymetric contours. Water depths range from 0 to 30 m. This zone is covered by primarily sandy sediment, though patches of cobbles and boulders crop out on the sea floor in places. Nearshore ramps are typically adjacent to arcuate shoreline areas and grade into deeper-water nearshore basin, hardbottom plain, or outer basin zones.
Ebb Tidal DeltaEbb-tidal deltas are lobate sandy shoals found on the side of inlets that form through the interaction of waves and ebbing tidal currents. Water depths range from 1 to 14 m. Ebb-tidal deltas in the study area are located on the soundward sides of several bays located along the south coast of Cape Cod on Nantucket Sound, as well as adjacent to tidal passages between the Elizabeth Islands
Hard-Bottom PlainsHard-bottom plains are mostly low-relief but rough zones of sea floor composed primarily of coarse sands and gravels that are situated adjacent and between the shoal/sand wave zones in the sounds. Water depths range from 4 to 30 m.
Outer BasinOuter basins are low-relief and mostly smooth zones of sea floor located in water depths greater than 30 m. The only outer basin identified in the study area lies beyond the western mouth of Vineyard Sound, on the margin of Rhode Island Sound. The broad depression is characterized by primarily muddy sands
Coastal EmbaymentCoastal embayments include several small bays and harbors in the vicinity of Woods Hole and along the shorelines of the Elizabeth Islands.
Shell ZonesShell zones are areas that are nearly completely covered by carbonate shells. A portion of sea floor between L’Hommediue and Hedge Fence shoals in western Nantucket Sound was mapped where high densities of slipper shells (Crepidula fornicata) cover the sea floor. Despite high acoustic backscatter in sidescan-sonar data, sediment samples recovered primarily muddy sediments beneath the shells. The shell zone water depths range from 15 to 18 m.
Shoal - Sand WavesShoal/sand wave areas are sea-floor zones dominated by linear to sinuous, high relief bedforms (up to 16 m locally) primarily composed of sandy sediments that have been reworked from adjacent glacial deposits by energetic tidal currents. Water depths range from approximately 3 to 30 m. The largest shoals form elongate chains in central portions of the Sounds that geologic and geophysical data suggest are at least partly cored by Pleistocene glacial material, which crops out at the sea floor locally along their lengths.

Area_sqkm Area of feature in kilometers squared (UTM, Zone 19, WGS84). (Source: U.S. Geological Survey)

Range of values
Minimum:0.000939
Maximum:72.066428
Units:square kilometers
Resolution:0.000001

### Who produced the data set?

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

### How was the data set created?

Poppe and others, 2007 (source 1 of 12)

Poppe, L.J., Ackerman, S.D., Foster, D.S., Blackwood, D.S., Butman, B., Moser, M.S., and Stewart, H.F., 2007, Sea-floor character and surface processes in the vicinity of Quicks Hole, Elizabeth Islands, Massachusetts: Open-File Report 2006-1357, U.S. Geological Survey, Reston, VA.

Poppe and others, 2010 (source 2 of 12)

Poppe, L.J., McMullen, K.Y., Foster, D.S., Blackwood, D.S., Williams, S.J., Ackerman, S.D., Moser, M.S., and Glomb, K.A., 2010, Geological interpretation of the sea floor offshore of Edgartown, Massachusetts: Open-File Report 2009-1001, U.S. Geological Survey, Reston, VA.

Pendleton and others, 2012 (source 3 of 12)

Pendleton, E.A., Twichell, D.C., Foster, D.S., Worley, C.R, Irwin, B.J., and Danforth, W.W., 2012, High-resolution geophysical data from the sea floor surrounding the Western Elizabeth Islands, Massachusetts: Open-File Report 2011-1184, U.S. Geological Survey, Reston, VA.

Andrews and others, 2014 (source 4 of 12)

Andrews, B.D., Ackerman, S.D., Baldwin, W.E., Foster, D.S., and Schwab, W.C., 2014, High-Resolution Geophysical Data from the Inner Continental Shelf at Vineyard Sound, Massachusetts: Open-File Report 2012-1006, U.S. Geological Survey, Reston, VA.

Pendleton and others, 2014 (source 5 of 12)

Pendleton, E.A., Andrews, B.D., Danforth, W.W., and Foster, D.S., 2014, High-resolution geophysical data collected aboard the U.S. Geological Survey research vessel Rafael to supplement existing datasets from Buzzards Bay and Vineyard Sound, Massachusetts: Open-File Report 2013-1020, U.S. Geological Survey, Reston, VA.

CZM sample database (source 6 of 12)

Poppe and others, 2008 (source 7 of 12)

Poppe, L.J., McMullen, K.Y., Foster, D.S., Blackwood, D.S., Williams, S.J., Ackerman, S.D., Barnum, S.R., and Brennan, R.T., 2008, Sea-floor character and sedimentary processes in the vicinity of Woods Hole, Massachusetts: Open File Report 2008-1004, U.S. Geological Survey, Reston, VA.

Ackerman and others, 2014 (source 8 of 12)

Ackerman, S.D., Pappal, A.L., Huntley, E.C., Blackwood, D.S., and Schwab, W.C., 2015, Geological Sampling Data and Benthic Biota Classification: Buzzards Bay and Vineyard Sound, Massachusetts: Open file Report 2014-1221, U.S. Geological Survey, Reston, VA.

USACE-JALBTCX, 2009 (source 9 of 12)

U.S. Army Corps of Engineers - Joint Airborne Lidar Bathymetry Center of Expertise, 2009, 2005 - 2007 US Army Corps of Engineers (USACE) Topo/Bathy Lidar: Maine, Massachusetts, and Rhode Island: NOAA National Ocean Service (NOS), Coastal Services Center (CSC), Charleston, SC.

NOAA, 2008 (source 10 of 12)

National Oceanic and Atmospheric Administration, 2008, Descriptive report, navigable area survey H11920, Vineyard Sound, Massachusetts, Gay Head to Cedar Tree Neck: National Oceanographic and Atmospheric Administration - National Ocean Survey, Norfolk, VA.

NOAA, 2008 (source 11 of 12)

National Oceanic and Atmospheric Administration, 2008, Descriptive report, navigable area survey H11921, Vineyard Sound, Massachusetts, Sow and Pigs reef to Quicks Hole: National Oceanographic and Atmospheric Administration - National Ocean Survey, Norfolk, VA.

NOAA Single-Beam Soundings (source 12 of 12)

NOAA National Geophysical Data Center, 2015, NOS Hydrographic Survey Data.

Date: 2014 (process 1 of 3) Sea floor physiographic zones were qualitatively defined in ArcGIS, following the criteria defined by Kelley and others (1996), primarily on the basis of acoustic backscatter, bathymetrically derived slope and roughness, surficial geologic interpretations from seismic-reflection data, and textural information from bottom photographs and sediment samples. The interpretation was initiated by digitizing a polygon shapefile (file > new > shapefile in ArcCatalog 9.3.1, then editor > 'create new feature' in ArcMap 9.3.1) around the extent of the regional bathymetric DEM (see vns10m_navd88 in the larger work citation), and a new field called 'Pzone_name' was created in the shapefile attribute table. The polygon was then partitioned into multiple physiographic zone polygons using 'cut polygon' and 'auto-complete polygon' in an edit session. As each new polygon area was created, the 'Pzone_name' attribute field was populated with the appropriate physiographic zone label. In general, polygon editing was done at scales between 1:5,000 and 1:20,000, depending on the size of the interpreted zone and the resolution of the source data.

Person who carried out this activity:

508-5488700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Date: 2014 (process 2 of 3) The polygon shapefile containing the physiographic zone units was imported as a feature class within a file geodatabase feature dataset, and topological rules were established (ArcCatalog 9.3.1). Topological errors, primarily overlaps and gaps, were identified and remedied using the topology toolbar in ArcMap (9.3.1).

Person who carried out this activity:

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Date: 2014 (process 3 of 3) The physiographic zone polygon feature class was exported back to a shapefile and the 'Shape_Area' and 'Shape_Length' fields were deleted from its attribute table (ArcCatalog and ArcMap 9.3.1). XTools Pro (7.1.0) was then used to add and populate a new attribute field containing polygon area in square kilometers based on UTM, zone 19 N, WGS84. Finally, the shapefile was reprojected from UTM zone 19 N, WGS84 to GCS WGS84 using ArcToolbox > Data Management Tools > Projections and Transformations > Feature > Project.

Person who carried out this activity:

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Kelley, J.T., Barnhardt, W.A., Belknap, D.F., Dickson, S.M., and Kelley, A.R., 1998, The Seafloor Revealed: The Geology of the Northwestern Gulf of Maine Inner Continental Shelf: Maine Geological Survey Open-File Report 96-6, Maine Geological Survey, Natural Resources Information and Mapping Center, Augusta, Maine.

McMullen, K.Y., Paskevich, V.F., and Poppe, L.J., 2011, GIS data catalog (version 2.2), in Poppe, L.J., Williams, S.J., and Paskevich, V.F., eds., 2005, USGS East-coast Sediment Analysis: Procedures, Database, and GIS Data: Open-File Report 2005-1001, U.S. Geological Survey, Reston, VA.

Ford, K.H., and Voss, S.E, 2010, Seafloor Sediment Composition in Massachusetts Determined Using Point Data: Massachusetts Division of Marine Fisheries Technical Report TR-45, Massachusetts Division of Marine Fisheries, New Bedford, MA.

### How reliable are the data what problems remain in the data set?

These data were produced qualitatively from acoustic and sample data with varying resolutions. Horizontal uncertainty associated with sample collection especially, can be quite high (100's of meters), much higher than positional uncertainty associated with acoustic data (usually less than <10's of meters). The date of sample collection and ship station positioning all contribute to sample position uncertainty. These qualitatively derived polygons outlining sea floor features are estimated to be within 50 meters, horizontally, but locally may be higher when sediment texture delineation is based on sample information alone.

These physiographic zones are defined for areas where source data exists. In general, gaps in the coverage coincide with gaps in the source data. However, some small data gaps were interpreted through extrapolation. Areas of lower data quality and incomplete coverage are noted in a data confidence attribute field.

### How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?

Access_Constraints: None Use_Constraints: Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey (USGS) as the source of this information. Additionally, there are limitations associated with qualitative sediment mapping interpretations. Because of the scale of the source geophysical data and the spacing of samples, not all changes in sea floor texture are captured. The data were mapped between 1:5,000 and 1:20,000, but the recommended scale for application of these data is 1:25,000.

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

VineyardNantucketSound_Pzones.zip from USGS Open File Report 2016-1119. WinZip v. 14.5 file that contains qualitatively derived polygons that define sea floor physiographic zones in Vineyard and western Nantucket Sounds, MA and the associated metadata

Availability in digital form:

Data format: WinZip v. 14.5 file contains qualitatively derived polygons that define sea floor physiographic zones in Vineyard and western Nantucket Sounds, MA and the associated metadata in format Shapefile (version ArcMap 9.3.1) Esri Polygon Shapefile Size: 0.475

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Generated by mp version 2.9.12 on Mon Jul 25 14:06:18 2016

## Deleting data by location using another shapefile

I have two shapefiles: one is main file and another is a part divided (manually by selecting) from the main file.

Now i want to delete the divided part from the main file with the help of divided part. How this possible in QGIS?

If those overlapping features (points from the divided part) share the same location with features (points from the main layer) I will simply use the "Difference" from the Vector > Geoprocessing Tools > Difference .

This algorithm extracts features from the Input layer that fall outside, or partially overlap, features in the Overlay layer. Input layer features that partially overlap feature(s) in the Overlay layer are split along those features' boundary and only the portions outside the Overlay layer features are retained.

Attributes are not modified, although properties such as area or length of the features will be modified by the difference operation. If such properties are stored as attributes, those attributes will have to be manually updated.

Answered 3 months ago by Taras with 1 upvote

I suppose that you have two layers with overlapping objects.

Processing → Extract by location.

Extract features from the main layer by comparing them with features from second layer. The predicate is disjoint. After launch, you'll get the layer without points from the second layer.

## Recovering layer (*.lyr) file that is missing shapefile? - Geographic Information Systems

USGS Topo Map Vector Data (Vector) 29990 Montopolis, Texas 20190724 for 7.5 x 7.5 minute Shapefile vector digital data

This dataset depicts geographic features on the surface of the earth. It is a general purpose dataset for users who are not GIS experts. The geospatial data in this dataset are from selected National Map data holdings and other government sources.

Irregular -97.7499999999999 -97.6249999999999 30.2500000000001 30.1250000000001 None contours transportation structures geographic names hydrography boundary Public Land Survey System woodland Combined Vector 7.5 x 7.5 minute Shapefile

Geographic Names Information System

There is a diversity of national data sources in this product. Although the data accuracy varies, the data generally have high enough horizontal accuracy to support NMAS and mapping at 1:24,000 scale. NMAS horizontal accuracy requires that at least 90 percent of well-defined points tested are within 0.02 inch of the true position. Contours are generated from the National Elevation Dataset (NED). The most recently published figure of overall absolute vertical accuracy of the NED within the conterminous US, expressed as the root mean square error (RMSE) of 25,310 reference points is 1.55 meters. However, the vertical accuracy actually varies significantly across the US because of differences in source quality, terrain relief, land cover, and other factors. Details of this analysis are published in "Accuracy Assessment of the U.S. Geological Survey National Elevation Dataset, and Comparison with Other Large-Area Elevation Datasets-SRTM and ASTER: U.S. Geological Survey Open-File Report 2014-1008," http://pubs.usgs.gov/2014.1008/ .

Land Cover - Woodland Vector digital data The Woodland Tint is a derivative land cover product created using several national map layers: three National Land Cover Database (NLCD) 2011 raster layers (Tree Canopy, Imperviousness, and Land Cover) and two vector layers (National Hydrography Dataset and Transportation). The process begins with masking the NLCD 2011 Tree Canopy Data cartographic with NLCD 2011 Imperviousness (values from 1-100), and NLCD 2011 Land Cover (value 11 = Open Water). The resulting raster data with canopy values of 20 and greater are converted to woodland vector polygons and smoothed via the Paek Algorithm. The woodland polygons are masked with buffered Transportation (Roads, Airport Runways, and Railroads) and Hydrography (NHD Areas excluding Inundation Area and NHD Waterbodies excluding Swamp/Marsh). The resulting polygons are checked for scale appropriate size (minimum size of one acre), and the small woodland polygons as well as small clearings within the woodland polygons are deleted. For Hawaii and Puerto Rico, two National Land Cover Database (NLCD) raster layers (Tree Canopy 2011 and Imperviousness 2001): and two vector layers(National Hydrography Dataset and Transportation) are used. The resulting raster data is carried out as before to produce the polygon vector data. For Alaska, the Woodland Tint is a derivative land cover product created using five national map layers: one raster layer, National Land Cover Database (NLCD) 2011 (Land Cover) and four vector layers (National Hydrography Dataset, Transportation Roads, Transportation Airports and Transportation Railroads). The process begins with combining three NLCD 2011 Land Cover V1 Classes (41 - Deciduous Forest, 42 - Evergreen Forest, and 43 - Mixed Forest). The resulting raster data was converted to woodland vector polygons, and smoothed via the Paek Algorithm. The woodland polygons are masked with buffered Transportation (Roads, Airport Runways, and Railroads) and Hydrography (NHD Areas excluding Inundation Areas and NHD Waterbodies excluding Swamp/Marsh). The resulting polygons are checked for scale appropriate size (minimum size of one acre), and the small woodland polygons as well as small clearings within the woodland polygons are deleted. http://nationalmap.gov https://www.mrlc.gov/nlcd2011.php 24000 digital data 2016 2016 publication date Land Cover - Woodland National Landcover Dataset National Hydrography Dataset National Transportation Dataset U.S. Geological Survey in cooperation with U.S. Environmental Protection Agency, USDA Forest Service, and other Federal, State and local partners. National Hydrography Dataset is a component of a comprehensive base geospatial data model.

Hydrography vector digital data The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. The high-resolution NHD was originally created using 1:24,000-scale data. State and Local Stewards are improving the data by incorporating local updates based on more current and more accurate source data. Water features in the real world are relatively dynamic and the differences at the time of data collection mean that water features may not register exactly to other layers. The hydrographic feature names contained in and displayed by the NHD are extracted and validated from the Geographic Names Information System (GNIS). Spatial objects may be filtered or generalized to achieve a 1:24,000-scale representation. http://nhd.usgs.gov/ http://nhd.usgs.gov/gnis.html http://nhdgeo.usgs.gov/metadata/nhd_high.htm 24000 digital data 20100820 20100820 publication date Hydrography Hydrography features and feature names Global Land Ice Measurements from Space initiative (GLIMS)

Gaging Stations vector digital data This dataset, termed "GAGES II", an acronym for Geospatial Attributes of Gages for Evaluating Streamflow, version II, provides geospatial data and classifications for 9,322 stream gages maintained by the U.S. Geological Survey (USGS). It is an update to the original GAGES in 2010. The GAGES II dataset consists of gages which have had either 20+ complete years (not necessarily continuous) of discharge record since 1950, or are currently active, as of water year 2009, and whose watersheds lie within the United States, including Alaska, Hawaii, and Puerto Rico. Only active stations, as identified by the GAGES II dataset, are symbolized. http://water.usgs.gov/lookup/getspatial?gagesII_Sept2011 http://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml 24000 digital data publication date Hydrography - Gaging Stations Hydrography features and gaging stations U.S. Geological Survey, National Geospatial Technical Operations Center - National Elevation Dataset is a component of a comprehensive base geospatial data model.

Hypsography Vector digital data This contour featureclass was generated from the 1/3 arc-second version of the 3D Elevation Program. The intended viewing scale for these features is 1:24,000. The contours are derived from a filtered elevation raster to achieve smoother arcs. In some areas, the 3DEP data may be modified by the National Hydrography Dataset (NHD) flow lines and water bodies to facilitate improved integration between the hypsography and hydrography on USGS map products. These contours were generated primarily for use as a layer in GeoPDFs created in the digital mapping program. The raster data source of contours is the 3D Elevation Program 1/3 arc-second layer. Secondary datasets include the high resolution flow lines, water bodies, and areas from the National Hydrography Dataset (NHD). The NHD layers are used in hydro-enforcement of the DEM prior to contour generation. The goals of the hydro-enforcement are to prevent contour lines from extending over the surface of water bodies and to align the contour reentrants with the NHD single- line streams. The 3DEP raster cells are converted to points. Those points, along with the NHD flow lines are input into an interpolation tool to create a new surface. The NHD water bodies and areas are preprocessed to attach the minimum and maximum elevation to each polygon. From these precalculated values, an appropriate value is calculated by which to raise the elevation cells under the NHD polygons. The NHD polygons are then converted into rasters, which in turn will be used to generate a mosaic that includes the new raster surface. The mosaic is filtered to provide smoother contour lines. Contours are generated and depression and index contours are identified. There is no guarantee or warranty concerning the accuracy of the data. Users should be aware that temporal changes may have occurred since these data were collected and generated and that some parts of these data may no longer represent actual surface conditions. Hydro-enforcement and generalization can also significantly alter the spatial characteristics of the contours. Users should not use these data for critical applications without a full awareness of its limitations. http://Hs.gov/ 24000 digital data publication date Hypsography Contours USFS

Transportation, USFS FSTopo roads Vector digital data The FSTopo database was originally populated with Cartographic Feature File (CFF) data. CFF data were derived from the standard Forest Service Primary Base Series (PBS) or Single Edition Series (SES) map as part of the Forest Service National Geographic Information System Plan. PBS and SES maps were developed from the U.S. Geological Survey 1:24,000-scale, 7.5-minute topographic map series, with enhancements and regular revisions to satisfy Forest Service needs. Except in Alaska, where 1:63,360-scale maps are used, the original USGS 1:24,000-scale source maps were constructed to meet National Map Accuracy Standards, which require that 90 percent of all well-defined features shown on the map are within .02 inches of their true location. CFF data were collected using methods and the best technologies available to ensure that digitized elements were captured within .003 inches of corresponding elements shown on source maps. The USDA Geospatial Service and Technology Center (GSTC) uses the same data collection accuracy standard for additions and revisions to the data. Only maps in USDA Forest Service areas will contain USDA Forest roads. http://www.fs.fed.us/gstc/ 24000 digital data 2012 2015 publication date Roads - USFS FSTopo Road centerlines, route numbers, road classification, street names Federal Railroads Administration

Transportation, Railroads Vector digital data Railroad are acquired annually from the FRA. Rail lines and sidings are converted into the National Transportation Dataset. The rail lines layer represents the freight lines of the nation's railroad system. The data set covers all 50 states and the District of Columbia, as well as territories and possessions of the United States. No rail lines exist in American Samoa, Guam, Northern Mariana Islands, and the Virgin Islands of the US. 24000 digital data 2016 2016 publication date Federal Railroads Administration Main track centerlines Federal Aviation Administration

Airports Vector digital data Airport points and runway polygons are for Federal Aviation Administration (FAA)-recognized public and private airports in the United States. FAA airports and runway shapefiles are used to the update the existing airports and runways in the National Transportation Dataset. Digital data were inspected for attribute accuracy, spatial accuracy, and completeness. http://www.faa.gov 24000 digital data 2011 2012 publication date Transportation - Airports runways Various government agencies and volunteer organizations

U.S.-Canada National Boundary vector digital data The boundary is a digital representation of the International boundary between the United States and Canada as per the Treaty of 1908. It has been generated from a combination of recent surveys and datum conversions. It is intended for general mapping purposes only. The boundary dataset is composed of 29 segments that correspond to the original 256 boundary maps. Attributes of each segment define the scale in which the line in that area may be accurately depicted. It is produced for mapping purposes only and not intended to illustrate the boundary beyond the limits of the scale for any given segment http://www.internationalboundarycommission.org/ http://www.internationalboundarycommission.org/index-eng.html digital data 2006 2015 publication date U.S. / Canada International Boundary International Boundary between Canada and the United States U.S. Geological Survey, U.S. Department of Agriculture, and the Instituto Nacional de Estadistica y Geografia of Mexico.

U.S.-Mexico National Boundary vector digital data The international boundary between Mexico and the United States, defined as a joint venture between the U.S. Department of Agriculture (USDA) and the Instituto Nacional de Estadistica y Geografia of Mexico (INEGI), resulted in an unofficial United States-Mexico boundary dataset that was further enhanced by the U.S. Geological Survey's Border Environmental Health Initiative (BEHI). With the data frame scale set to 1:5,000 in ArcMap, the center of the Rio Grande/Rio Bravo was digitized using the NAIP 2004 Imagery. In areas with dense stands of salt cedar (bounding box = UL -104.714 30.038, UR -104.664 30.037, LR -104.666 29.933, LL -104.717 29.934 NAD83), the center of the channel was difficult, and sometimes impossible, to easily determine. To determine the location of the boundary, the GIS analyst compared the location of the line in the INEGI 1:250K Limite feature class with the NAIP 2004 Imagery and adjusted the boundary to the image, thus, the delineation of the international boundary is less certain in these areas. The remaining part of the border was extracted from the INEGI 1:250K Limite feature class and appended to the line feature class created along the Rio Grande/Rio Bravo. The U.S. Geological Survey reviewed the original USDA data against 2007 NAIP imagery and further edited 9 line segments in the Rio Grande areas to conform to National Map Accuracy Standards. https://ibwc.gov/GIS_Maps/GIS_Program.html digital data 1972 2006 publication date U.S. / Mexico International Boundary International Boundary between Mexico and the United States U.S. Department of Agriculture (USDA) Forest Service - Washington Office Automated Lands Program (ALP).

USDA Forest Service Boundary vector digital data The forest service boundaries defined by the USDA Forest Service encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area. The nationwide Proclaimed Forest dataset was created by the USDA Forest Service, Washington Office Automated Lands Program (ALP) staff from collected source data created by the Regional Offices. Only maps in USDA Forest Service areas will contain USDA Forest boundaries. 24000 digital data 2009 2017 publication date USDA Forest Service Boundaries National Forest Service Boundaries Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate

U.S. Military Installations, Ranges, and Training Areas vector digital data This dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories. These sites encompass land which is federally owned or otherwise managed. This dataset was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program. This dataset represents the baseline for georeferenced boundaries of sites selected from the 2010 Base Structure Report. The boundary locations are intended for planning purposes only and do not represent the legal or surveyed land parcel boundaries. This list does not necessarily represent a comprehensive collection of all DoD facilities, and only those in the fifty United States and US Territories were considered for inclusion. Maps produced at a scale of 1:50,000 or larger which otherwise comply with National Map Accuracy Standards will remain compliant if this data is incorporated. Although these data have been provided by the DoD components, no warranty expressed or implied is made regarding the utility of the data on any other system, in derived products or data alterations, nor shall the act of distribution constitute such warranty. https://www.acq.osd.mil/eie/BSI/BEI_DISDI.html 50000 digital data 2011 2017 publication date U.S. Department of Defense Military Installations Boundary lines, installation names U.S. Census Bureau

State and Equivalent Boundary vector digital data The Census Bureau collects boundaries from state and county governments through the Boundary and Annexation Survey, and publishes the results as TIGER files. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2016 publication date State Boundaries State and Equivalent Boundary U.S. Census Bureau

County and Equivalent Boundary vector digital data The Census Bureau collects boundaries from state and county governments through the Boundary and Annexation Survey (BAS), and publishes the results as TIGER files. The USGS uses the TIGER data without editing or alteration for US Topo. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date County Boundaries County and Equivalent Boundary U.S. Census Bureau

Incorporated Places Boundary vector digital data The boundaries of most incorporated places are as of January 1, 2017, as reported through the Census Bureau's Boundary and Annexation Survey. Limited updates that occurred after January 1, 2017, such as newly incorporated places, are also included. The boundaries of all Census Designated Places (CDP) were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date Incorporated Places Boundaries Incorporated Places Boundaries U.S. Census Bureau

Jurisdictional Boundary The 115th Congress is seated from January 2017 to 2019. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts are provided to the Census Bureau through the Redistricting Data Program (RDP). The USGS uses the TIGER data without editing or alteration for US Topo. http://www.census.gov/geo/maps-data/data/tiger.html 24000 digital data 2016 2016 publication date Jurisdictional Boundaries 115th Congressional Districts Boundaries U.S. Census Bureau

Minor Civil Divisions Boundary vector digital data The boundaries of most legal Minor Civil Divisions (MCD) are as of January 1, 2017, as reported through the Census Bureau's Boundary and Annexation Survey. The boundaries of all Census Designated Places (CDP)are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date Minor Civil Divisions Boundaries Minor Civil Divisions Boundaries U.S. Census Bureau

Native American Area Boundary vector digital data The Native American Area boundaries are as of January 1, 2017 as reported through Census Bureau's Boundary and Annexation Survey. The USGS dataset is a combination of two Census Bureau files. It includes the American Indian/Alaska Native/Native Hawaiian Areas National (AIANNH) National TIGER/Line shapefile, including the following legal entities: federally recognized American Indian reservations and off-reservation trust land areas, state-recognized American Indian reservations, and Hawaiian home lands. Also included is the Alaska Native Regional Corporation (ANRC) State-based TIGER/Line shapefile containing a record of the 12 Alaska Native Regional Corporations used to conduct both the for-profit and non-profit affairs of Alaska Natives within a defined region of Alaska. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date Native American Area Boundaries Native American Area Boundaries National Park Service - Land Resources Division

National Park Service Boundary vector digital data This dataset depicts National Park Service unit boundaries for display and general analysis purposes. The USGS converted areas of generally 3 acres or less to point features to facilitate cartographic display on the US Topo digital map product. See Source URL for link to complete dataset. This data set is complete but subject to continual updates to reflect boundary amendments, legislation, and acquisitions, and improved processing techniques. The data is being regularly updated with verified boundaries from NPS Land Resources Division. The data is intended for use as a tool for display and general GIS analysis purposes only. It is in no way intended for engineering or legal purposes. The data accuracy is checked against best available sources which may be dated. NPS assumes no liability for use of this data. Boundaries from the Land Resources Division have separate polygons for each type of unit. For example Denali National Park and Denali National Preserve are separate individual polygons. https://irma.nps.gov/App/Portal digital data 2002 2018 publication date National Park Service Boundary Current Administrative Boundaries of the National Park System Units U.S. Census Bureau

Unincorporated Places Boundary vector digital data The boundaries of most unincorporated places in this shapefile are as of January 1, 2017, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). Limited updates that occurred after January 1, 2017, such as newly unincorporated places, are also included. The boundaries of all Census Designated Places (CDPs) were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date Unincorporated Places Boundaries Unincorporated Places Boundaries U.S. Department of Interior, U.S. Fish and Wildlife Service

NCA National Cemeteries vector digital data This dataset represents boundaries of National cemeteries administered by the U.S. Department of Veterans Affairs, National Cemetery Administration. This layer may also contain a small number of boundaries for those managed by the Department of the Army. This data is subject to change as other national cemetery areas are authorized. This dataset is intended for general mapping and reference purposes only. https://nationalmap.gov/boundaries.html https://www.cem.va.gov/cem/cems/index.asp digital data 2013 2018 publication date National Cemetery Boundary Name, general location information from NCA, Boundaries from combination of NCA boundary data, parcel data and other mapping datasets. U.S. Geological Survey, National Geospatial Technical Operations Center

Geographic Names Information System (GNIS) Vector digital data The Geographic Names Information System (GNIS) is the Federal and national standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS in support of the U.S. Board on Geographic Names as the official repository of domestic geographic names data, the official vehicle for geographic names use by all departments of the Federal Government, and the source for applying geographic names to Federal electronic and printed products. https://geonames.usgs.gov/ 24000 digital data 2012 2012 publication date Geographic Names Geographic feature names Federal land management agencies

Points of Interest vector digital data Includes campgrounds, trailheads, visitor centers, picnic areas, Ranger stations and federal land management agency headquarters. Point data was provided by various federal agencies, such as NPS, US Forest Service, BLM, US FWS. This data is subject to change at any time. http://nationalmap.usgs.gov 24000 digital data 2016 2018 ground condition Structures - various Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Fire Stations Vector digital data Any location where fire fighters are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Fire Departments which are Mobile Units and not having a permanent location, are included, in which case their location has been depicted at the city/town hall or at the center of their service area if a city/town hall does not exist. This dataset includes those locations primarily engaged in forest or grasslands fire fighting, including fire lookout towers if the towers are in current use for fire protection purposes. This dataset includes both private and governmental entities. Locations that serve only administrative function are excluded. Locations serving both administrative and operational functions are included. http://nationalmap.usgs.gov 24000 None 2006 2018 ground condition Structures - Fire Stations Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Law Enforcement Vector digital data Included are locations where sworn officers of a law enforcement agency are regularly based or stationed. This dataset includes local police, county sheriff's offices, state police or highway patrol locations. Most federal law enforcement agency locations are not included. http://nationalmap.usgs.gov 24000 None 2005 2018 ground condition Structures - Law Enforcement Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Prisons/Correctional Facility Vector digital data Includes both private and government medium and high security prisons and correctional institutions. Low and minimum security institutions such as local jails, prison camps, correctional farms or work farms, detention and treatment centers are generally excluded. http://nationalmap.usgs.gov 24000 None 2007 2018 ground condition Structures - Prisons/Correctional Facility Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Schools Vector digital data The schools within this dataset are composed of Public elementary and secondary education in the US as defined and tracked by the National Center for Education Statistics (NCES), Common Core Dataset (CCD). Private schools in this dataset are composed of Private elementary and secondary education in the US as defined by the Private School Survey, NCES. The colleges and Universities are composed of postsecondary education facilities as defined by the Integrated Post Secondary Education System (IPEDS), NCES. Included are Doctoral and Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical schools and other health care professions, schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges and other specialized institutions. Changes to base school data may occur through the USGS¿ The National Map Corps Volunteer Geographic Information project. http://nationalmap.usgs.gov 24000 None 2008 2018 ground condition Structures - Schools Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Hospitals Vector digital data Includes general medical and surgical hospitals, psychiatric, substance abuse and specialty hospitals such as Children's hospitals, cancer, maternity and rehabilitation hospitals. Other types of hospitals are included if represented in data sets provided by various partners for this compilation. Hospitals operated by the US Department of Veterans Affairs are included. Nursing homes, long term care facilities and Urgent Care facilities are generally excluded. Locations that are administrative offices only are excluded from the dataset. http://nationalmap.usgs.gov 24000 None 2006 2018 ground condition Structures - Hospitals Geographic features and feature names State government websites

State Capitol building vector digital data Includes the official State Capitol buildings for the U.S. states and territories. http://nationalmap.gov 24000 digital data 2014 2014 ground condition Structures - State Capitol building Geographic feature and feature name

The data for this product are created as follows. All geospatial content is taken from national geospatial databases under the stewardship of USGS data programs. The NAIP imagery is provided by a seamless tile service that delivers image data at the resolution and quality of the source imagery. The raster and vector data, including grids and collar information, are processed using ESRI ArcGIS software and exported. Map formatting is performed using a custom application, which includes post-processing to embed the metadata XML document.

Public Land Survey System Vector

Entity point Void polygon composed of rings Complete chain Complete chain

## Recovering layer (*.lyr) file that is missing shapefile? - Geographic Information Systems

USGS Topo Map Vector Data (Vector) 2342 Bannack, Montana 20200721 for 7.5 x 7.5 minute Shapefile vector digital data

This dataset depicts geographic features on the surface of the earth. It is a general purpose dataset for users who are not GIS experts. The geospatial data in this dataset are from selected National Map data holdings and other government sources.

Irregular -113 -112.875 45.2500000000001 45.1250000000001 None contours transportation structures geographic names hydrography boundary Public Land Survey System woodland Combined Vector 7.5 x 7.5 minute Shapefile

Geographic Names Information System

There is a diversity of national data sources in this product. Although the data accuracy varies, the data generally have high enough horizontal accuracy to support NMAS and mapping at 1:24,000 scale. NMAS horizontal accuracy requires that at least 90 percent of well-defined points tested are within 0.02 inch of the true position. Contours are generated from the National Elevation Dataset (NED). The most recently published figure of overall absolute vertical accuracy of the NED within the conterminous US, expressed as the root mean square error (RMSE) of 25,310 reference points is 1.55 meters. However, the vertical accuracy actually varies significantly across the US because of differences in source quality, terrain relief, land cover, and other factors. Details of this analysis are published in "Accuracy Assessment of the U.S. Geological Survey National Elevation Dataset, and Comparison with Other Large-Area Elevation Datasets-SRTM and ASTER: U.S. Geological Survey Open-File Report 2014-1008," http://pubs.usgs.gov/2014.1008/ .

City and Town Hall Buildings vector digital data This dataset contains points representing city hall and town hall government buildings in the U.S., Puerto Rico, and the U.S. Virgin Islands. This includes a building or building complex that serves as a primary location for a local or municipal government¿s administrative functions. These buildings are generally called City Hall, Town Hall, Village Hall, Municipal Building, Municipal Center, City Building or similar designation. The purpose of this dataset is to document the spatial location of such buildings for general cartographic representation purposes on USGS mapping products at 1:24,000 scale. Supplemental information: Excluded are county, state, or federal level administration buildings or historical buildings that are no longer used for government administration. This dataset is dynamic and not complete at this time. Additions and updates are provided by volunteers through the USGS' The National Map Corps (TNMCorps) crowdsourcing project. Although these data and associated metadata have been reviewed for accuracy and completeness, no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map 24000 digital data 2011 2020 ground condition Structures - City/Town Hall Geographic features and feature names U.S. Geological Survey

Courthouse Buildings vector digital data This dataset contains point features representing some types of courthouse buildings in the U.S., Puerto Rico, and the U.S. Virgin Islands. This includes county courthouses, state supreme courthouses, and the Supreme Court of the United States. The purpose is to document the spatial location and physical address of courthouse buildings for general cartographic representation purposes on USGS mapping products at 1:24,000 scale. This dataset does not contain appellate courts, federal courts, tribal courts, municipal, village, or town courts, specialty courts (e.g., family, probate, juvenile, or bankruptcy courts), or historic courthouse buildings which no longer function as an active court. The information in this dataset was collected between 2017 and 2018 by volunteers through the USGS The National Map Corps (TNMCorps) crowdsourcing project. Although these data and associated metadata have been reviewed for accuracy and completeness, no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Supplemental information: The County level court buildings handle the bulk of county-level court functions, usually located in the city designated as a county seat. The state supreme courthouse data represents the court buildings, usually located in the city designated as the state capital, which house the ultimate judicial tribunal in a state's court system. The Supreme Court of the United States is represented by a single data point. County level courts are referred to differently in different states. The data points for county courthouses may also contain superior, circuit, and district courts where the "County" court designation does not apply within an individual state court system. https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map 24000 digital data 2017 2018 ground condition Structures - Courthouse Geographic features and feature names U.S. Geological Survey

Land Cover - Woodland Vector digital data The Woodland Tint is a derivative land cover product created using several national map layers: three National Land Cover Database (NLCD) 2011 raster layers (Tree Canopy, Imperviousness, and Land Cover) and two vector layers (National Hydrography Dataset and Transportation). The process begins with masking the NLCD 2011 Tree Canopy Data cartographic with NLCD 2011 Imperviousness (values from 1-100), and NLCD 2011 Land Cover (value 11 = Open Water). The resulting raster data with canopy values of 20 and greater are converted to woodland vector polygons and smoothed via the Paek Algorithm. The woodland polygons are masked with buffered Transportation (Roads, Airport Runways, and Railroads) and Hydrography (NHD Areas excluding Inundation Area and NHD Waterbodies excluding Swamp/Marsh). The resulting polygons are checked for scale appropriate size (minimum size of one acre), and the small woodland polygons as well as small clearings within the woodland polygons are deleted. For Hawaii and Puerto Rico, two National Land Cover Database (NLCD) raster layers (Tree Canopy 2011 and Imperviousness 2001): and two vector layers(National Hydrography Dataset and Transportation) are used. The resulting raster data is carried out as before to produce the polygon vector data. For Alaska, the Woodland Tint is a derivative land cover product created using five national map layers: one raster layer, National Land Cover Database (NLCD) 2011 (Land Cover) and four vector layers (National Hydrography Dataset, Transportation Roads, Transportation Airports and Transportation Railroads). The process begins with combining three NLCD 2011 Land Cover V1 Classes (41 - Deciduous Forest, 42 - Evergreen Forest, and 43 - Mixed Forest). The resulting raster data was converted to woodland vector polygons, and smoothed via the Paek Algorithm. The woodland polygons are masked with buffered Transportation (Roads, Airport Runways, and Railroads) and Hydrography (NHD Areas excluding Inundation Areas and NHD Waterbodies excluding Swamp/Marsh). The resulting polygons are checked for scale appropriate size (minimum size of one acre), and the small woodland polygons as well as small clearings within the woodland polygons are deleted. http://nationalmap.gov https://www.mrlc.gov/nlcd2011.php 24000 digital data 2016 2016 publication date Land Cover - Woodland National Landcover Dataset National Hydrography Dataset National Transportation Dataset U.S. Geological Survey in cooperation with U.S. Environmental Protection Agency, USDA Forest Service, and other Federal, State and local partners. National Hydrography Dataset is a component of a comprehensive base geospatial data model.

Hydrography vector digital data The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. The high-resolution NHD was originally created using 1:24,000-scale data. State and Local Stewards are improving the data by incorporating local updates based on more current and more accurate source data. Water features in the real world are relatively dynamic and the differences at the time of data collection mean that water features may not register exactly to other layers. The hydrographic feature names contained in and displayed by the NHD are extracted and validated from the Geographic Names Information System (GNIS). Spatial objects may be filtered or generalized to achieve a 1:24,000-scale representation. http://nhd.usgs.gov/ http://nhd.usgs.gov/gnis.html http://nhdgeo.usgs.gov/metadata/nhd_high.htm 24000 digital data 20100820 20100820 publication date Hydrography Hydrography features and feature names Global Land Ice Measurements from Space initiative (GLIMS)

Gaging Stations vector digital data This dataset, termed "GAGES II", an acronym for Geospatial Attributes of Gages for Evaluating Streamflow, version II, provides geospatial data and classifications for 9,322 stream gages maintained by the U.S. Geological Survey (USGS). It is an update to the original GAGES in 2010. The GAGES II dataset consists of gages which have had either 20+ complete years (not necessarily continuous) of discharge record since 1950, or are currently active, as of water year 2009, and whose watersheds lie within the United States, including Alaska, Hawaii, and Puerto Rico. Only active stations, as identified by the GAGES II dataset, are symbolized. http://water.usgs.gov/lookup/getspatial?gagesII_Sept2011 http://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml 24000 digital data publication date Hydrography - Gaging Stations Hydrography features and gaging stations U.S. Geological Survey, National Geospatial Technical Operations Center - National Elevation Dataset is a component of a comprehensive base geospatial data model.

Hypsography Vector digital data This contour featureclass was generated from the 1/3 arc-second version of the 3D Elevation Program. The intended viewing scale for these features is 1:24,000. The contours are derived from a filtered elevation raster to achieve smoother arcs. In some areas, the 3DEP data may be modified by the National Hydrography Dataset (NHD) flow lines and water bodies to facilitate improved integration between the hypsography and hydrography on USGS map products. These contours were generated primarily for use as a layer in GeoPDFs created in the digital mapping program. The raster data source of contours is the 3D Elevation Program 1/3 arc-second layer. Secondary datasets include the high resolution flow lines, water bodies, and areas from the National Hydrography Dataset (NHD). The NHD layers are used in hydro-enforcement of the DEM prior to contour generation. The goals of the hydro-enforcement are to prevent contour lines from extending over the surface of water bodies and to align the contour reentrants with the NHD single- line streams. The 3DEP raster cells are converted to points. Those points, along with the NHD flow lines are input into an interpolation tool to create a new surface. The NHD water bodies and areas are preprocessed to attach the minimum and maximum elevation to each polygon. From these precalculated values, an appropriate value is calculated by which to raise the elevation cells under the NHD polygons. The NHD polygons are then converted into rasters, which in turn will be used to generate a mosaic that includes the new raster surface. The mosaic is filtered to provide smoother contour lines. Contours are generated and depression and index contours are identified. There is no guarantee or warranty concerning the accuracy of the data. Users should be aware that temporal changes may have occurred since these data were collected and generated and that some parts of these data may no longer represent actual surface conditions. Hydro-enforcement and generalization can also significantly alter the spatial characteristics of the contours. Users should not use these data for critical applications without a full awareness of its limitations. http://Hs.gov/ 24000 digital data publication date Hypsography Contours USFS

Transportation, USFS FSTopo roads Vector digital data The FSTopo database was originally populated with Cartographic Feature File (CFF) data. CFF data were derived from the standard Forest Service Primary Base Series (PBS) or Single Edition Series (SES) map as part of the Forest Service National Geographic Information System Plan. PBS and SES maps were developed from the U.S. Geological Survey 1:24,000-scale, 7.5-minute topographic map series, with enhancements and regular revisions to satisfy Forest Service needs. Except in Alaska, where 1:63,360-scale maps are used, the original USGS 1:24,000-scale source maps were constructed to meet National Map Accuracy Standards, which require that 90 percent of all well-defined features shown on the map are within .02 inches of their true location. CFF data were collected using methods and the best technologies available to ensure that digitized elements were captured within .003 inches of corresponding elements shown on source maps. The USDA Geospatial Service and Technology Center (GSTC) uses the same data collection accuracy standard for additions and revisions to the data. Only maps in USDA Forest Service areas will contain USDA Forest roads. http://www.fs.fed.us/gstc/ 24000 digital data 2012 2015 publication date Roads - USFS FSTopo Road centerlines, route numbers, road classification, street names Federal Railroads Administration

Transportation, FRA Railroads Vector digital data Railroads are acquired annually from the FRA. Rail lines and sidings are converted into the National Transportation Dataset. The rail lines layer represents the freight lines of the nation's railroad system. The data set covers all 50 states and the District of Columbia, as well as territories and possessions of the United States. No rail lines exist in American Samoa, Guam, Northern Mariana Islands, and the Virgin Islands of the US. 24000 digital data 2016 2016 publication date Federal Railroads Administration Main track centerlines Federal Aviation Administration

Transportation, FAA Airports, Runways, Seaplane Bases, Heliports Vector digital data Airport points and runway polygons are for Federal Aviation Administration (FAA)-recognized public and private airports in the United States. USGS updates the National Transportation Dataset (NTD) airports, runways approximately bi-monthly from FAA¿s modification reports. In April 2020, USGS started creating the seaplane base and heliport layers. FAA is the primary source for seaplane bases and heliports. The National Geospatial-Intelligence Agency provided heliport updates for IN, KY, MI, MS, OH, and TN. Digital data were inspected for attribute accuracy, spatial accuracy, and completeness. http://www.faa.gov 24000 digital data 2011 2012 publication date Transportation - Airports runways Various government agencies and volunteer organizations

U.S.-Canada National Boundary vector digital data The boundary is a digital representation of the International boundary between the United States and Canada as per the Treaty of 1908. It has been generated from a combination of recent surveys and datum conversions. It is intended for general mapping purposes only. The boundary dataset is composed of 29 segments that correspond to the original 256 boundary maps. Attributes of each segment define the scale in which the line in that area may be accurately depicted. It is produced for mapping purposes only and not intended to illustrate the boundary beyond the limits of the scale for any given segment http://www.internationalboundarycommission.org/ http://www.internationalboundarycommission.org/index-eng.html digital data 2006 2015 publication date U.S. / Canada International Boundary International Boundary between Canada and the United States U.S. Geological Survey, U.S. Department of Agriculture, and the Instituto Nacional de Estadistica y Geografia of Mexico.

U.S.-Mexico National Boundary vector digital data The international boundary between Mexico and the United States, defined as a joint venture between the U.S. Department of Agriculture (USDA) and the Instituto Nacional de Estadistica y Geografia of Mexico (INEGI), resulted in an unofficial United States-Mexico boundary dataset that was further enhanced by the U.S. Geological Survey's Border Environmental Health Initiative (BEHI). With the data frame scale set to 1:5,000 in ArcMap, the center of the Rio Grande/Rio Bravo was digitized using the NAIP 2004 Imagery. In areas with dense stands of salt cedar (bounding box = UL -104.714 30.038, UR -104.664 30.037, LR -104.666 29.933, LL -104.717 29.934 NAD83), the center of the channel was difficult, and sometimes impossible, to easily determine. To determine the location of the boundary, the GIS analyst compared the location of the line in the INEGI 1:250K Limite feature class with the NAIP 2004 Imagery and adjusted the boundary to the image, thus, the delineation of the international boundary is less certain in these areas. The remaining part of the border was extracted from the INEGI 1:250K Limite feature class and appended to the line feature class created along the Rio Grande/Rio Bravo. The U.S. Geological Survey reviewed the original USDA data against 2007 NAIP imagery and further edited 9 line segments in the Rio Grande areas to conform to National Map Accuracy Standards. https://ibwc.gov/GIS_Maps/GIS_Program.html digital data 1972 2006 publication date U.S. / Mexico International Boundary International Boundary between Mexico and the United States U.S. Department of Agriculture (USDA) Forest Service - Washington Office Automated Lands Program (ALP).

USDA Forest Service Boundary vector digital data The forest service boundaries defined by the USDA Forest Service encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area. The nationwide Proclaimed Forest dataset was created by the USDA Forest Service, Washington Office Automated Lands Program (ALP) staff from collected source data created by the Regional Offices. Only maps in USDA Forest Service areas will contain USDA Forest boundaries. 24000 digital data 2009 2017 publication date USDA Forest Service Boundaries National Forest Service Boundaries Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate

U.S. Military Installations, Ranges, and Training Areas vector digital data This dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories. These sites encompass land which is federally owned or otherwise managed. This dataset was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program. This dataset represents the baseline for georeferenced boundaries of sites selected from the 2010 Base Structure Report. The boundary locations are intended for planning purposes only and do not represent the legal or surveyed land parcel boundaries. This list does not necessarily represent a comprehensive collection of all DoD facilities, and only those in the fifty United States and US Territories were considered for inclusion. Maps produced at a scale of 1:50,000 or larger which otherwise comply with National Map Accuracy Standards will remain compliant if this data is incorporated. Although these data have been provided by the DoD components, no warranty expressed or implied is made regarding the utility of the data on any other system, in derived products or data alterations, nor shall the act of distribution constitute such warranty. https://www.acq.osd.mil/eie/BSI/BEI_DISDI.html 50000 digital data 2011 2017 publication date U.S. Department of Defense Military Installations Boundary lines, installation names U.S. Census Bureau

State and Equivalent Boundary vector digital data The Census Bureau collects boundaries from state and county governments through the Boundary and Annexation Survey, and publishes the results as TIGER files. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2016 publication date State Boundaries State and Equivalent Boundary U.S. Census Bureau

County and Equivalent Boundary vector digital data The Census Bureau collects boundaries from state and county governments through the Boundary and Annexation Survey (BAS), and publishes the results as TIGER files. The USGS uses the TIGER data without editing or alteration for US Topo. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date County Boundaries County and Equivalent Boundary U.S. Census Bureau

Incorporated Places Boundary vector digital data The boundaries of most incorporated places are as of January 1, 2017, as reported through the Census Bureau's Boundary and Annexation Survey. Limited updates that occurred after January 1, 2017, such as newly incorporated places, are also included. The boundaries of all Census Designated Places (CDP) were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date Incorporated Places Boundaries Incorporated Places Boundaries U.S. Census Bureau

Jurisdictional Boundary The 115th Congress is seated from January 2017 to 2019. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts are provided to the Census Bureau through the Redistricting Data Program (RDP). The USGS uses the TIGER data without editing or alteration for US Topo. http://www.census.gov/geo/maps-data/data/tiger.html 24000 digital data 2016 2016 publication date Jurisdictional Boundaries 115th Congressional Districts Boundaries U.S. Census Bureau

Minor Civil Divisions Boundary vector digital data The boundaries of most legal Minor Civil Divisions (MCD) are as of January 1, 2017, as reported through the Census Bureau's Boundary and Annexation Survey. The boundaries of all Census Designated Places (CDP)are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date Minor Civil Divisions Boundaries Minor Civil Divisions Boundaries U.S. Census Bureau

Native American Area Boundary vector digital data The Native American Area boundaries are as of January 1, 2017 as reported through Census Bureau's Boundary and Annexation Survey. The USGS dataset is a combination of two Census Bureau files. It includes the American Indian/Alaska Native/Native Hawaiian Areas National (AIANNH) National TIGER/Line shapefile, including the following legal entities: federally recognized American Indian reservations and off-reservation trust land areas, state-recognized American Indian reservations, and Hawaiian home lands. Also included is the Alaska Native Regional Corporation (ANRC) State-based TIGER/Line shapefile containing a record of the 12 Alaska Native Regional Corporations used to conduct both the for-profit and non-profit affairs of Alaska Natives within a defined region of Alaska. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date Native American Area Boundaries Native American Area Boundaries National Park Service - Land Resources Division

National Park Service Boundary vector digital data This dataset depicts National Park Service unit boundaries for display and general analysis purposes. The USGS converted areas of generally 3 acres or less to point features to facilitate cartographic display on the US Topo digital map product. See Source URL for link to complete dataset. This data set is complete but subject to continual updates to reflect boundary amendments, legislation, and acquisitions, and improved processing techniques. The data is being regularly updated with verified boundaries from NPS Land Resources Division. The data is intended for use as a tool for display and general GIS analysis purposes only. It is in no way intended for engineering or legal purposes. The data accuracy is checked against best available sources which may be dated. NPS assumes no liability for use of this data. Boundaries from the Land Resources Division have separate polygons for each type of unit. For example Denali National Park and Denali National Preserve are separate individual polygons. https://irma.nps.gov/App/Portal digital data 2002 2018 publication date National Park Service Boundary Current Administrative Boundaries of the National Park System Units U.S. Census Bureau

Unincorporated Places Boundary vector digital data The boundaries of most unincorporated places in this shapefile are as of January 1, 2017, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). Limited updates that occurred after January 1, 2017, such as newly unincorporated places, are also included. The boundaries of all Census Designated Places (CDPs) were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census. The USGS uses the TIGER data without editing or alteration. http://www.census.gov/geo/maps-data/data/tiger.html digital data 2013 2017 publication date Unincorporated Places Boundaries Unincorporated Places Boundaries U.S. Department of Interior, U.S. Fish and Wildlife Service

NCA National Cemeteries vector digital data This dataset represents boundaries of National cemeteries administered by the U.S. Department of Veterans Affairs, National Cemetery Administration. This layer may also contain a small number of boundaries for those managed by the Department of the Army. This data is subject to change as other national cemetery areas are authorized. This dataset is intended for general mapping and reference purposes only. https://nationalmap.gov/boundaries.html https://www.cem.va.gov/cem/cems/index.asp digital data 2013 2018 publication date National Cemetery Boundary Name, general location information from NCA, Boundaries from combination of NCA boundary data, parcel data and other mapping datasets. U.S. Geological Survey, National Geospatial Technical Operations Center

Geographic Names Information System (GNIS) Vector digital data The Geographic Names Information System (GNIS) is the Federal and national standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS in support of the U.S. Board on Geographic Names as the official repository of domestic geographic names data, the official vehicle for geographic names use by all departments of the Federal Government, and the source for applying geographic names to Federal electronic and printed products. https://geonames.usgs.gov/ 24000 digital data 2012 2012 publication date Geographic Names Geographic feature names Federal land management agencies

Points of Interest vector digital data Includes campgrounds, trailheads, visitor centers, picnic areas, Ranger stations and federal land management agency headquarters. Point data was provided by various federal agencies, such as NPS, US Forest Service, BLM, US FWS. This data is subject to change at any time. http://nationalmap.usgs.gov 24000 digital data 2016 2018 ground condition Structures - various Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Fire Stations Vector digital data Any location where fire fighters are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Fire Departments which are Mobile Units and not having a permanent location, are included, in which case their location has been depicted at the city/town hall or at the center of their service area if a city/town hall does not exist. This dataset includes those locations primarily engaged in forest or grasslands fire fighting, including fire lookout towers if the towers are in current use for fire protection purposes. This dataset includes both private and governmental entities. Locations that serve only administrative function are excluded. Locations serving both administrative and operational functions are included. http://nationalmap.usgs.gov 24000 None 2006 2018 ground condition Structures - Fire Stations Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Law Enforcement Vector digital data Included are locations where sworn officers of a law enforcement agency are regularly based or stationed. This dataset includes local police, county sheriff's offices, state police or highway patrol locations. Most federal law enforcement agency locations are not included. http://nationalmap.usgs.gov 24000 None 2005 2018 ground condition Structures - Law Enforcement Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Prisons/Correctional Facility Vector digital data Includes both private and government medium and high security prisons and correctional institutions. Low and minimum security institutions such as local jails, prison camps, correctional farms or work farms, detention and treatment centers are generally excluded. http://nationalmap.usgs.gov 24000 None 2007 2018 ground condition Structures - Prisons/Correctional Facility Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Schools Vector digital data The schools within this dataset are composed of Public elementary and secondary education in the US as defined and tracked by the National Center for Education Statistics (NCES), Common Core Dataset (CCD). Private schools in this dataset are composed of Private elementary and secondary education in the US as defined by the Private School Survey, NCES. The colleges and Universities are composed of postsecondary education facilities as defined by the Integrated Post Secondary Education System (IPEDS), NCES. Included are Doctoral and Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical schools and other health care professions, schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges and other specialized institutions. Changes to base school data may occur through the USGS¿ The National Map Corps Volunteer Geographic Information project. http://nationalmap.usgs.gov 24000 None 2008 2018 ground condition Structures - Schools Geographic features and feature names State and Federal Partners, updates from USGS' The National Map Corps volunteers

Hospitals Vector digital data Includes general medical and surgical hospitals, psychiatric, substance abuse and specialty hospitals such as Children's hospitals, cancer, maternity and rehabilitation hospitals. Other types of hospitals are included if represented in data sets provided by various partners for this compilation. Hospitals operated by the US Department of Veterans Affairs are included. Nursing homes, long term care facilities and Urgent Care facilities are generally excluded. Locations that are administrative offices only are excluded from the dataset. http://nationalmap.usgs.gov 24000 None 2006 2018 ground condition Structures - Hospitals Geographic features and feature names State government websites

State Capitol building vector digital data Includes the official State Capitol buildings for the U.S. states and territories. http://nationalmap.gov 24000 digital data 2014 2014 ground condition Structures - State Capitol building Geographic feature and feature name

The data for this product are created as follows. All geospatial content is taken from national geospatial databases under the stewardship of USGS data programs. The NAIP imagery is provided by a seamless tile service that delivers image data at the resolution and quality of the source imagery. The raster and vector data, including grids and collar information, are processed using ESRI ArcGIS software and exported. Map formatting is performed using a custom application, which includes post-processing to embed the metadata XML document.

Public Land Survey System Vector

Entity point Void polygon composed of rings Complete chain Complete chain

## U.S. Streams, Rivers and WaterwayGIS Shapefiles Based on USGS Data

These ArcGIS shapefiles work with the free ArcExplorer GIS viewer, ArcView GIS, ArcGIS, Maptitude, Mapinfo, Manifold, TatukGIS and many other commercial GIS and mapping programs and many free GIS programs as well and they're much less costly than most others. Use our free Learn2Map GIS Tutorial and Atlas to add streets, highways, census information and much more to your maps for free. We have included free national level base maps with each map archive listed below. Also, check our free GIS software and free GIS ArcGIS shapefiles pages for more information and free ArcGIS shapefiles. If you are new to GIS, check out our free Learn2Map GIS Tutorial and Atlas.

Shapefile Description

These shapefiles are derived from the USGS and National Hydrography Dataset. The map scale is 1:1,000,000 providing a high degree of detail. They are available for all 50 states and Puerto Rico by region. Each region is $47. The included readme.txt file describes the contents of the shapefiles. The One Million-Scale Streams map layer shows the major streams and rivers of the United States, Puerto Rico, and the U.S. Virgin Islands that can be represented at a map scale of 1:1,000,000 (1 inch on a map at that scale equals about 15.8 miles on the land surface). If you download this map layer, you will also want to download One Million-Scale Waterbodies and Wetlands as of 2012 to complete the network of surface waters. The data fields in the image to the left are explained in the metadata. A link to current metadata is included with each shapefile. Each regional shapefile map layer is$47. Click next to the shapefile you wish and you will have immediate access to it using our secure merchant account provider, ClickBank/Keynetics or PayPal, whichever you prefer.

Region 1 - Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont - Download Now.

Region 2 - New Jersey, New York and Puerto Rico - Download Now.

Region 3 - Delaware, Maryland, Pennsylvania, Virginia, West Virginia, and the District of Columbia - Download Now.

Region 4 - Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, and Tennessee - Download Now.

Region 5 - Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin - Download Now.

Region 6 - Arkansas, Louisiana, New Mexico, Oklahoma, and Texas - Download Now.

The image on this page is an example of how these shapefiles may look when loaded into a GIS program. Your GIS maps may not look exactly like this. Each GIS program is different. Shapefiles themselves are a collection of points, lines or polygons. They have no attributes. It is up to you, the user, to define colors, line width, symbols and other attributes within the limits and capabilities of the GIS programs you are using.

The geodata and shapefiles found at MapCruzin.com may come from a variety of government, non-governmental and self-reporting private sources. While we try to assure the accuracy of this material, we cannot promise that it is absolutely accurate. We do promise that using the map layer will be fun, entertaining or educational - possibly even frustrating. Beyond this, we make no guarantee as to its suitability for any purpose. We assume no liability or responsibility for errors or inaccuracies. Please understand that you download and use these map layers and data at your own risk.

Michael R. Meuser
Data Research & GIS Specialist

MapCruzin.com is an independent firm specializing in GIS project development and data research. We created the first U.S. based interactive toxic chemical facility maps on the internet in 1996 and we have been online ever since. Learn more about us and our services.

Have a project in mind? If you have data, GIS project or custom shapefile needs contact Mike.

## Recovering layer (*.lyr) file that is missing shapefile? - Geographic Information Systems

Cartopy is a cartographic Python library that was developed for applications in geographic data manipulation and visualization. It is the successor to the the Basemap Toolkit, which was the previous Python library used for geographic visualizations. Cartopy can be used to plot satellite data atop realistic maps, visualize city and country boundaries, track and predict movement based on geographic targeting, and a range of other applications relating to geographic-encoded data systems. In this tutorial, Anaconda 3 will be used to install Cartopy and related geographic libraries. As an introduction to the library and geographic visualizations, some simple tests will be conducted to ensure that the Cartopy library was successfully installed and is working properly. In subsequent tutorials: shapefiles will be used as boundaries, realistic city streets will be mapped, and satellite data will be analyzed.

The NEO-6 is a miniature GPS module designed by u-blox to receive updates from up to 22 satellite on 50 different channels that use trilateration to approximate fixed position of a receiver device every second (or less, for some modules). The particular module used in this tutorial, the NEO-6M, is capable of updating its position every second and communicates with an Arduino board using UART serial communication. The NEO-6M uses the National Marine Electronics Association (NMEA) protocol which provides temporal and geolocation information such as Greenwich Mean Time (GMT), latitude, longitude, altitude, and approximate course speed. The NEO-6M and Arduino board will also be paired with an SD module to create a portable logger that acts as a retrievable GPS tracker.

Calculating latitude and longitude from a GOES-R L1b data file. The GOES-R L1b radiance files contain radiance data and geometry scan information in radians. This information is not enough to plot geographic radiance data right from the file, however, after some geometric manipulation harnessing satellite position and ellipsoid parameters, we can derive latitude and longitude values from the one-dimensional scan angles and plot our data in projected formats familiar to many geographic information tools.