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National Elevation Dataset Now Available 11.14.2002

November 14, 2002
USGS NED has been developed by merging the highest-resolution, best-quality elevation data available across the United States into a seamless raster format.

The USGS National Elevation Dataset (NED) has been developed by merging the highest-resolution, best-quality elevation data available across the United States into a seamless raster format. NED is the result of the maturation of the USGS effort to provide 1:24,000-scale Digital Elevation Model (DEM) data for the conterminous US and 1:63,360-scale DEM data for Alaska.

Ordering N.E.D Purchase NED Nationwide Data Bundle $1,449.95 Download NED from GISDataDepot - The data is grouped in 2 degree latitude by 6 degree longitude blocks. To determine which coverage block is required, see coverage map. Note: Premium data download account is required to access these data.

Description
The NED is a new raster product assembled by the U.S. Geological Survey. NED is designed to provide National elevation data in a seamless form with a consistent datum, elevation unit, and projection. Data corrections were made in the NED assembly process to minimize artifacts, perform edge matching, and fill sliver areas of missing data. NED has a resolution of one arc-second (approximately 30 meters) for the conterminous United States, Hawaii, and a resolution of two arc-seconds for Alaska.

Specifications
Coverage: Complete Coverage of Contintental US, AK, and HI

Size: 40 GB (Compressed) 56GB Uncompressed

Format: ArcGrid Why use NED? Most users of USGS DEM data are aware that the USGS develops data products that conform to the USGS quadrangle format, requiring users to access multiple, often numerous files, in order to produce a DEM layer for GIS project study areas. There are often many challenges and obstacles faced by users as they attempt to tile together datasheets to create a seamless DEM layer. These obstacles include the typical frustration encountered when working with data crossing UTM boundaries, data stored in different projections, datums, and perhaps most frustrating, gaps or errors in data due to slivers and poor edge-matching.