National GIS
Data Sets

Seamless Data Distribution System
The Seamless Data Distribution System (SDDS) is the ultimate location to explore and retrieve data. U.S. Geological Survey (USGS) and the EROS Data Center (EDC) are committed to providing access to geospatial data through The National Map. An approach is to provide free downloads of national base layers, as well as other geospatial data layers. These data layers include the 1 meter Digital Orthoimagery data (DOQQ) as well as the National Elevation Dataset (NED) and National Hydrography Dataset (NHD) which are also available from other sources referenced here. Please explore both the product information and the interactive maps to learn more.

The digital data available through this site are generally in shapefile or SDTS format, and unless noted otherwise are in 1:2,000,000 scale.  Much of the 1:2,000,000 USGS data originated as DLG data, but National Atlas base cartographic data sets have been revised three times in the past ten years and will be maintained on three or five year revision cycles, depending on theme.

The areal extent of the aquifers, as shown in this data, represent the area in which a named aquifer is the shallowest of the principal aquifers.

These aquifer outcrop and shallow subcrop boundaries represent broad, regional categories and should not be interpreted as site specific.

The NED has a resolution of 1 arc-second (approximately 30 meters) for the conterminous United States, Hawaii, and Puerto Rico and a resolution of 2 arc-seconds for Alaska. National Elevation Dataset data sources have a variety of elevation units, horizontal datum, and map projections. In the NED assembly process, the elevation values are converted to decimal meters as a consistent unit of measure, North American Datum 1983 is consistently used as horizontal datum, and all the data are recast in a geographic projection. Older digital elevation models (DEM) produced by methods that are now obsolete have been filtered during the NED assembly process to minimize artifacts that are commonly found in data produced by these methods. Artifact removal greatly improves the quality of the slope, shaded-relief, and synthetic drainage information that can be derived from the elevation data.  NED processing also includes steps to adjust values where adjacent DEM's do not match well and to fill areas of missing data between DEM's. These processing steps ensure that the NED has no void areas and artificial discontinuities have been minimized.

As higher resolution or higher quality data become available, the NED is updated to incorporate the best available coverage. As the USGS's 7.5-minute and 15-minute digital elevation products near completion for the conterminous United States and Alaska respectively, NED data will soon incorporate these sources. For the small areas that are not yet covered, the lower resolution 30-minute and 1-degree USGS DEM products were interpolated to obtain values used in NED.

The DLG files contribute a national coverage of millions of features, including water bodies such as lakes and ponds, linear water features such as streams and rivers, and also point features such as springs and wells. These files provide standardized feature types, delineation, and spatial accuracy. From RF3, the NHD acquires hydrographic sequencing, upstream and downstream navigation for modeling applications, and reach codes. The reach codes provide a way to integrate data from organizations at all levels by linking the data to this nationally consistent hydrographic network. The feature names are from the Geographic Names Information System (GNIS).

The NHD provides comprehensive coverage of hydrographic data for the United States. Some of the anticipated end-user applications of the NHD are multiuse hydrographic modeling and water-quality studies of fish habitats. Although based on 1:100,000-scale data, the NHD is planned so that it can incorporate and encourage the development of the higher resolution data that many users require. The NHD can be used to promote the exchange of data between users at the national, State, and local levels. Many users will benefit from the NHD and will want to contribute to the dataset as well.

The TM multi-band mosaics were processed using an unsupervised clustering algorithm. Both leaves-off and leaves-on data sets were analyzed. The resulting clusters were then labeled using aerial photography and ground observations. Clusters that represented more than one land cover category were also identified and, using various ancillary data sets, models developed to split the confused clusters into the correct land cover categories.

A hierarchical land cover classification scheme of 21 classes was developed and applied in a consistent manner across the entire United States. More information is available at the site.

Multi-Resolution Land Characteristics (MRLC)
The Multi-Resolution Land Characteristics (MRLC) Consortium was originally formed in 1992 in order to meet the needs of several federal agencies (USGS, EPA, NOAA, and USFS) for Landsat 5 imagery, and land-cover information. The on-going need for current Landsat 7 data, land-cover and other geospatial data within the federal government culminated in reforming the MRLC Consortium in 2000 (called MRLC 2000).

The MRLC 2000 strategy includes:

1. acquisition of a multi-temporal Landsat 7 TM data centered around the year 2000 that includes three dates of imagery per path-row;
2. preprocessing of these data using common methods and standards, and;
3. generation of a consistent national land-cover data base.

MRLC 2000 pre-processing specifications include:

1. a minimum of three dates per path/row (representing different seasonality) for the conterminous lower 48 US states, Alaska, Hawaii and Puerto Rico;
2. geometric terrain-corrected registration to within one pixel spatial accuracy;
3. data referenced to the National Albers Equal map projection;
4. imagery re-sampled using cubic convolution to 30m pixels, and;
5. all 8 TM bands processed (including thermal and pan bands) for Landsat 7 data.

These Landsat 7 scenes are available on CD Rom media for a price of $45 per CD. The CDs will be distributed with the associated DEM used for terrain correction. There will be a buffer zone of path/rows along the Canada and Mexico border that will not have the DEM file attached due to the distribution restrictions associated with the DTED data covering foreign areas.

USGS/National Biological Information Infrastructure (NBII) GAP Analysis Program
GAP has collaborated with many other organizations,including other federal government agencies, state and local governments, academic institutions, non-profits, and private businesses to provide regional assessments of the conservation status of native vertebrate species and natural land cover types.

• Data - GAP themes are: Landcover, Stewardship, Animals, Analysis, and Aquatic.

• Ecoregions

• Hydrologic Cataloging Units:
  -1:2,000,000
  -1:250,000

Census Bureau Cartographic Information
These boundary files and other mapping resources are selected extracts from TIGER. They have been developed for various internal Census Bureau projects and have been made available on the Internet on an "as is" basis for the general public. They are generally in Arc/Info export or shapefile format.

The data originates from the TIGER data (mostly TIGER/line `95) and dates to the 1990 census. Scales on the TIGER data may be as precise as 1:24,000, but a number of the files that provide national coverage contain thinned data, and have been grouped in such as way that they are no longer that accurate. National coverages are probably better than the 1:2,000,000 referenced for the other data above, but can be easily combined with that data.

**The 2007 TIGER/Line Shapefiles (110th Congressional Districts) are available to download by nation, state, county, or American Indian Area (AIA). For example, the state shapefile is nation based, therefore it will include all 50 states, District of Columbia, Puerto Rico and the Island areas. For more information about how to download the data, please refer to the organizational structure of the shapefiles and relationship files. The 2007 TIGER/Line metadata in Extensible Markup Language (XML) format is included with each compressed TIGER/Line Shapefile.

• 103rd Congressional Districts
• 104th Congressional Districts
• 105th Congressional Districts
• 106th Congressional Districts
• 107th Congressional Districts
• 108th Congressional Districts
• 109th Congressional Districts
• 110th Congressional Districts
• 110th Congressional Districts**

• 103rd Congressional Districts
• 104th Congressional Districts
• 105th Congressional Districts
• 106th Congressional Districts
• 107th Congressional Districts
• 108th Congressional Districts
• 109th Congressional Districts
• 110th Congressional Districts
• 110th Congressional Districts - 2007 TIGER/Line Shapefiles

The geospatial data sets distributed by the Bureau of Transportation Statistics (BTS) depict transportation facilities, networks, and services of national significance. Databases are designed to be used with Geographic Information System (GIS) software packages to locate transportation features and provide a framework for transportation network analysis.

The 2002 data consist of transportation networks, transportation facilities, and other spatial data used as geographic reference. The 2002 data is available online and on compact disc. The 2001 data is still available online and on compact disc.

Metadata is available at http://www.bts.gov/gis/download_sites/ntad02/statedownloadform.html.

NORTAD Data Dictionary

NORTAD is a collection of geospatial data sets of transportation facilities for Canada, Mexico, and the United States. Also included are highway and rail Border Crossing facilities located at the border of the United States and Canada or the United States and Mexico.

All geospatial data distributed by the BTS is stored in an ASCII fixed length record format. Please refer to the Data Dictionary and Database Formats document for the generic field descriptions and file relationships. Please refer to the metadata included with each data set for more detailed information.