7: National Spatial Data Infrastructure II

David DiBiase

7.1. Overview

Chapters 6 and 7 consider the origins and characteristics of the framework data themes that make up the United States’ proposed National Spatial Data Infrastructure (NSDI). Chapter 6 discussed the geodetic control and orthoimagery themes. This chapter describes the origins, characteristics and current status of the elevation, transportation, hydrography, governmental units and cadastral themes.

7.2. Checklist

The following checklist is for Penn State students who are registered for classes in which this text, and associated quizzes and projects in the ANGEL course management system, have been assigned. You may find it useful to print this page out first so that you can follow along with the directions.

7.3. Theme: Elevation

The NSDI Framework Introduction and Guide (FGDC, 1997, p. 19) points out that “elevation data are used in many different applications.” Civilian applications include flood plain delineation, road planning and construction, drainage, runoff, and soil loss calculations, and cell tower placement, among many others. Elevation data are also used to depict the terrain surface by a variety of means, from contours to relief shading and three-dimensional perspective views.

The NSDI Framework calls for an “elevation matrix” for land surfaces. That is, the terrain is to be represented as a grid of elevation values. The spacing (or resolution) of the elevation grid may vary between areas of high and low relief (i.e., hilly and flat). Specifically, the Framework Introduction states that

Elevation values will be collected at a post-spacing of 2 arc-seconds (approximately 47.4 meters at 40° latitude) or finer. In areas of low relief, a spacing of 1/2 arc-second (approximately 11.8 meters at 40° latitude) or finer will be sought (FGDC, 1997, p. 18).

The elevation theme also includes bathymetry–depths below water surfaces–for coastal zones and inland water bodies. Specifically,

For depths, the framework consists of soundings and a gridded bottom model. Water depth is determined relative to a specific vertical reference surface, usually derived from tidal observations. In the future, this vertical reference may be based on a global model of the geoid or the ellipsoid, which is the reference for expressing height measurements in the Global Positioning System (Ibid).

USGS has lead responsibility for the elevation theme. Elevation is also a key component of USGS’ National Map. The next several pages consider how heights and depths are created, how they are represented in digital geographic data, and how they may be depicted cartographically.

7.4. Vector and Raster Approaches

The terms raster and vector were introduced back in Chapter 1 to denote two fundamentally different strategies for representing geographic phenomena. Both strategies involve simplifying the infinite complexity of the Earth’s surface. As it relates to elevation data, the raster approach involves measuring elevation at a sample of locations. The vector approach, on the other hand, involves measuring the locations of a sample of elevations. I hope that this distinction will be clear to you by the end of this chapter.

Vector and raster representations of the same terrain surface.

The illustration above compares how elevation data are represented in vector and raster data. On the left are elevation contours, a vector representation that is familiar with anyone who has used a USGS topographic map. The technical term for an elevation contour isisarithm, from the Greek words for “same” and “number.” The termsisoline, isogram, and isopleth all mean more or less the same thing. (See any cartography text for the distinctions.)

As you will see later in this chapter, when you explore Digital Line Graph hypsography data using Global Mapper or dlgv 32 Pro, elevations in vector data are encoded as attributes of line features. The distribution of elevation points across the quadrangle is therefore irregular. Raster elevation data, by contrast, consist of grids of points at which elevation is encoded at regular intervals. Raster elevation data are what’s called for by the NSDI Framework and the USGS National Map. Digital contours can now be rendered easily from raster data. However, much of the raster elevation data used in the National Map was produced from digital vector contours and hydrography (streams and shorelines). For this reason we’ll consider the vector approach to terrain representation first.

7.5. Contours

Contour lines trace the elevation of the terrain surface at regularly-spaced intervals (Raisz, 1948. © McGraw-Hill, Inc. Used by permission).

Drawing contour lines is a way to represent a terrain surface with a sample of elevations. Instead of measuring and depicting elevation at every point, you measure only along lines at which a series of imaginary horizontal planes slice through the terrain surface. The more imaginary planes, the more contours, and the more detail is captured.

Contour lines representing the same terrain as in the first figure, but in plan view. (Raisz, 1948. © McGraw-Hill, Inc. Used by permission).

Until photogrammetric methods came of age in the 1950s, topographers in the field sketched contours on the USGS 15-minute topographic quadrangle series. Since then, contours shown on most of the 7.5-minute quads were compiled from stereoscopic images of the terrain, as described in Chapter 6. Today computer programs draw contours automatically from the spot elevations that photogrammetrists compile stereoscopically.

Although it is uncommon to draw terrain elevation contours by hand these days, it is still worthwhile to know how. In the next few pages you’ll have a chance to practice the technique, which is analogous to the way computers do it.

7.6. Contouring By Hand

This page will walk you through a methodical approach to rendering contour lines from an array of spot elevations (Rabenhorst and McDermott, 1989). To get the most from this demonstration, I suggest that you print the illustration in the attached image file. Find a pencil (preferably one with an eraser!) and straightedge, and duplicate the steps illustrated below. A “Try This!” activity will follow this step-by-step introduction, providing you a chance to go solo.

Beginning a triangulated irregular network.

Starting at the highest elevation, draw straight lines to the nearest neighboring spot elevations. Once you have connected to all of the points that neighbor the highest point, begin again at the second highest elevation. (You will have to make some subjective decisions as to which points are “neighbors” and which are not.) Taking care not to draw triangles across the stream, continue until the surface is completely triangulated.

Complete TIN. Note that the triangle sides must not cross hydrologic features (i.e., the stream) on a terrain surface.

The result is a triangulated irregular network (TIN). A TIN is a vector representation of a continuous surface that consists entirely of triangular facets. The vertices of the triangles are spot elevations that may have been measured in the field by leveling, or in a photogrammetrist’s workshop with a stereoplotter, or by other means. (Spot elevations produced photogrammetrically are called mass points.) A useful characteristic of TINs is that each triangular facet has a single slope degree and direction. With a little imagination and practice, you can visualize the underlying surface from the TIN even without drawing contours.

Wonder why I suggest that you not let triangle sides that make up the TIN cross the stream? Well, if you did, the stream would appear to run along the side of a hill, instead of down a valley as it should. In practice, spot elevations would always be measured at several points along the stream, and along ridges as well. Photogrammetrists refer to spot elevations collected along linear features as breaklines (Maune, 2007). I omitted breaklines from this example just to make a point.

You may notice that there is more than one correct way to draw the TIN. As you will see, deciding which spot elevations are “near neighbors” and which are not is subjective in some cases. Related to this element of subjectivity is the fact that the fidelity of a contour map depends in large part on the distribution of spot elevations on which it is based. In general, the density of spot elevations should be greater where terrain elevations vary greatly, and sparser where the terrain varies subtly. Similarly, the smaller the contour interval you intend to use, the more spot elevations you need.

(There are algorithms for triangulating irregular arrays that produce unique solutions. One approach is called Delaunay Triangulationwhich, in one of its constrained forms, is useful for representing terrain surfaces. The distinguishing geometric characteristic of a Delaunay triangulation is that a circle surrounding each triangle side does not contain any other vertex.)

Tick marks drawn where elevation contours cross the edges of each TIN facet.

Now draw ticks to mark the points at which elevation contours intersect each triangle side. For instance, see the triangle side that connects the spot elevations 2360 and 2480 in the lower left corner of the illustration above? One tick mark is drawn on the triangle where a contour representing elevation 2400 intersects. Now find the two spot elevations, 2480 and 2750, in the same lower left corner. Note that three tick marks are placed where contours representing elevations 2500, 2600, and 2700 intersect.

This step should remind you of the equal interval classification scheme you read about in Chapter 3. The right choice of contour interval depends on the goal of the mapping project. In general, contour intervals increase in proportion to the variability of the terrain surface. It should be noted that the assumption that elevations increase or decrease at a constant rate is not always correct, of course. We will consider that issue in more detail later.

Threading elevation contours through a TIN.

Finally, draw your contour lines. Working downslope from the highest elevation, thread contours through ticks of equal value. Move to the next highest elevation when the surface seems ambiguous.

Keep in mind the following characteristics of contour lines (Rabenhorst and McDermott, 1989):

• Contours should always point upstream in valleys
• Contours should always point downridge along ridges
• Adjacent contours should always be sequential or equivalent
• Contours should never split into two
• Contours should never cross or loop
• Contours should never spiral
• Contours should never stop in the middle of a map

How does your finished map compare with the one I drew below?

7.7. Digital Line Graph (DLG)

7.8. Digital Elevation Model (DEM)

The term “Digital Elevation Model” has both generic and specific meanings. In general, a DEM is any raster representation of a terrain surface. Specifically, a DEM is a data product of the U.S. Geological Survey. Here we consider the characteristics of DEMs produced by the USGS Later in this chapter we’ll consider sources of global terrain data.

7.9. Interpolation

DEMs are produced by various methods. The method preferred by USGS is to interpolate elevations grids from the hypsography and hydrography layers of Digital Line Graphs.

A USGS 7.5-minute DEM and the DLG hypsography and hydrography layers from which it was produced.

The elevation points in DLG hypsography files are not regularly spaced. DEMs need to be regularly spaced to support the slope, gradient, and volume calculations they are often used for. Grid point elevations must be interpolated from neighboring elevation points. In the figure below, for example, the gridded elevations shown in purple were interpolated from the irregularly spaced spot elevations shown in red.

Elevation values in DEMs are interpolated from irregular arrays of elevations measured through photogrammetric methods, or derived from existing DLG hypsography and hydrography data.

Here’s another example of interpolation for mapping. The map below shows how 1995 average surface air temperature differed from the average temperature over a 30-year baseline period (1951-1980). The temperature anomalies are depicted for grid cells that cover 3° longitude by 2.5° latitude.

1995 Surface Temperature Anomalies. (National Climatic Data Center, 2005).

The gridded data shown above were estimated from the temperature records associated with the very irregular array of 3,467 locations pinpointed in the map below. The irregular array is transformed into a regular array through interpolation. In general, interpolation is the process of estimating an unknown value from neighboring known values.

The Global Historical Climate Network. (Eischeid et al., 1995).

Elevation data are often not measured at evenly-spaced locations. Photogrammetrists typically take more measurements where the terrain varies the most. They refer to the dense clusters of measurements they take as “mass points.” Topographic maps (and their derivatives, DLGs) are another rich source of elevation data. Elevations can be measured from contour lines, but obviously contours do not form evenly-spaced grids. Both methods give rise to the need for interpolation.

Interpolating an intermediate value on a number line.

The illustration above shows three number lines, each of which ranges in value from 0 to 10. If you were asked to interpolate the value of the tick mark labeled “?” on the top number line, what would you guess? An estimate of “5″ is reasonable, provided that the values between 0 and 10 increase at a constant rate. If the values increase at a geometric rate, the actual value of “?” could be quite different, as illustrated in the bottom number line. The validity of an interpolated value depends, therefore, on the validity of our assumptions about the nature of the underlying surface.

As I mentioned in Chapter 1, the surface of the Earth is characterized by a property called spatial dependence. Nearby locations are more likely to have similar elevations than are distant locations. Spatial dependence allows us to assume that it’s valid to estimate elevation values by interpolation.

Many interpolation algorithms have been developed. One of the simplest and most widely used (although often not the best) is theinverse distance weighted algorithm. Thanks to the property of spatial dependence, we can assume that estimated elevations are more similar to nearby elevations than to distant elevations. The inverse distance weighted algorithm estimates the value z of a point P as a function of the z-values of the nearest n points. The more distant a point, the less it influences the estimate.

The inverse distance weighted interpolation procedure.

7.10. Slope

Slope is a measure of change in elevation. It is a crucial parameter in several well-known predictive models used for environmental management, including the Universal Soil Loss Equation and agricultural non-point source pollution models.

One way to express slope is as a percentage. To calculate percent slope, divide the difference between the elevations of two points by the distance between them, then multiply the quotient by 100. The difference in elevation between points is called the rise. The distance between the points is called the run. Thus, percent slope equals (rise / run) x 100.

Calculating percent slope. A rise of 100 feet over a run of 100 feet yields a 100 percent slope. A 50-foot rise over a 100-foot run yields a 50 percent slope.

Another way to express slope is as a slope angle, or degree of slope. As shown below, if you visualize rise and run as sides of a right triangle, then the degree of slope is the angle opposite the rise. Since degree of slope is equal to the tangent of the fraction rise/run, it can be calculated as the arctangent of rise/run.

A rise of 100 feet over a run of 100 feet yields a 45° slope angle. A rise of 50 feet over a run of 100 feet yields a 26.6° slope angle.

You can calculate slope on a contour map by analyzing the spacing of the contours. If you have many slope values to calculate, however, you will want to automate the process. It turns out that slope calculations are much easier to calculate for gridded elevation data than for vector data, since elevations are more or less equally spaced in raster grids.

Several algorithms have been developed to calculate percent slope and degree of slope. The simplest and most common is called theneighborhood method. The neighborhood method calculates the slope at one grid point by comparing the elevations of the eight grid points that surround it.

The neighborhood algorithm estimates percent slope in cell 5 by comparing the elevations of neighboring grid cells.

The neighborhood algorithm estimates percent slope at grid cell 5 (Z5) as the sum of the absolute values of east-west slope and north-south slope, and multiplying the sum by 100. The diagram below illustrates how east-west slope and north-south slope are calculated. Essentially, east-west slope is estimated as the difference between the sums of the elevations in the first and third columns of the 3 x 3 matrix. Similarly, north-south slope is the difference between the sums of elevations in the first and third rows (note that in each case the middle value is weighted by a factor of two).

The neighborhood algorithm for calculating percent slope.

The neighborhood algorithm calculates slope for every cell in an elevation grid by analyzing each 3 x 3 neighborhood. Percent slope can be converted to slope degree later. The result is a grid of slope values suitable for use in various soil loss and hydrologic models.

7.11. Relief Shading

You can see individual pixels in the zoomed image of a 7.5-minute DEM below. I used dlgv32 Pro’s “Gradient Shader” to produce the image. Each pixel represents one elevation point. The pixels are shaded through 256 levels of gray. Dark pixels represent low elevations, light pixels represent high ones.

A digital elevation model in which light pixels represent high elevations, and dark pixels represent low elevations.

It’s also possible to assign gray values to pixels in ways that make it appear that the DEM is illuminated from above. The image below, which shows the same portion of the Bushkill DEM as the image above, illustrates the effect, which is called terrain shading, hill shading or shaded relief.

Shaded terrain image produced from the same DEM as shown in the above figures, using dlgv32 Pro’s Daylight Shader option, with the Surface Color set to gray.

The appearance of a shaded terrain image depends on several parameters, including vertical exaggeration. Click the buttons under the image below to compare the four terrain images of North America shown below, in which elevations are exaggerated 5 times, 10 times, 20 times, and 40 times respectively. (You will need to have the Adobe Flash player installed in order to complete this exercise. If you do not already have the Flash player, you can download it for free from Adobe.)

Effects of vertical exaggeration on a shaded terrain image

Another influential parameter is the angle of illumination. Click the buttons to compare terrain images that have been illuminated from the northeast, southeast, southwest, and northwest. Does the terrain appear to be inverted in one or more of the images? To minimize the possibility of terrain inversion, it is conventional to illuminate terrain from the northwest.

Effects of illumination angle on a shaded terrain image.

7.12. Lidar

For many applications, 30-meter DEMs whose vertical accuracy is measured in meters are simply not detailed enough. Greater accuracy and higher horizontal resolution can be produced by photogrammetric methods, but precise photogrammetry is often too time-consuming and expensive for extensive areas. Lidar is a digital remote sensing technique that provides an attractive alternative.

Lidar stands for LIght Detection And Ranging. Like radar (RAdio Detecting And Ranging), lidar instruments transmit and receive energy pulses, and enable distance measurement by keeping track of the time elapsed between transmission and reception. Instead of radio waves, however, lidar instruments emit laser light (laser stands for Light Amplifications by Stimulated Emission of Radiation).

Lidar instruments are typically mounted in low altitude aircraft. They emit up to 5,000 laser pulses per second, across a ground swath some 600 meters wide (about 2,000 feet). The ground surface, vegetation canopy, or other obstacles reflect the pulses, and the instrument’s receiver detects some of the backscatter. Lidar mapping missions rely upon GPS to record the position of the aircraft, and upon inertial navigation instruments (gyroscopes that detect an aircraft’s pitch, yaw, and roll) to keep track of the system’s orientation relative to the ground surface.

In ideal conditions, lidar can produce DEMs with 15-centimeter vertical accuracy, and horizontal resolution of a few meters. Its cost is prohibitive for small missions, but is justified for larger projects in which detail is essential. For example, lidar has been used successfully to detect subtle changes in the thickness of the Greenland ice sheet that result in a net loss of over 50 cubic kilometers of ice annually.

Image of Greenland, viewed from the south, showing changes in ice thickness measured by airborne lidar. Ice sheet thickness decreasing at 40-60 cm per year in darker blue areas (Goddard Space Flight Center, n.d.).

To learn more about the use of lidar in mapping changes in the Greenland ice sheet, visit NASA’s Scientific Visualization Studio.

7.13. Global Elevation Data

This page profiles three data products that include elevation (and, in one case, bathymetry) data for all or most of the Earth’s surface.

7.14. Bathymetry

The term bathymetry refers to the process and products of measuring the depth of water bodies. The U.S. Congress authorized the comprehensive mapping of the nation’s coasts in 1807, and directed that the task be carried out by the federal government’s first science agency, the Office of Coast Survey (OCS). That agency is now responsible for mapping some 3.4 million nautical square miles encompassed by the 12-mile territorial sea boundary, as well as the 200-mile Exclusive Economic Zone claimed by the U.S., a responsibility that entails regular revision of about 1,000 nautical charts. The coastal bathymetry data that appears on USGS topographic maps, like the one shown below, is typically compiled from OCS charts.

”Isobaths” (the technical term for lines of constant depth) shown on a USGS topographic map.

Early hydrographic surveys involved sampling water depths by casting overboard ropes weighted with lead and marked with depth intervals called marks and deeps. Such ropes were called leadlines for the weights that caused them to sink to the bottom. Measurements were called soundings. By the late 19th century, piano wire had replaced rope, making it possible to take soundings of thousands rather than just hundreds of fathoms (a fathom is six feet).

Seaman paying out a sounding line during a hydrographic survey of the East coast of the U.S. in 1916. (NOAA, 2007).

Echo sounders were introduced for deepwater surveys beginning in the 1920s. Sonar (SOund NAvigation and Ranging) technologies have revolutionized oceanography in the same way that aerial photography revolutionized topographic mapping. The seafloor topography revealed by sonar and related shipborne remote sensing techniques provided evidence that supported theories about seafloor spreading and plate tectonics.

Below is an artist’s conception of an oceanographic survey vessel operating two types of sonar instruments: multibeam and side scan sonar. On the left, a multibeam instrument mounted in the ship’s hull calculates ocean depths by measuring the time elapsed between the sound bursts it emits and the return of echoes from the seafloor. On the right, side scan sonar instruments are mounted on both sides of a submerged “towfish” tethered to the ship. Unlike multibeam, side scan sonar measures the strength of echoes, not their timing. Instead of depth data, therefore, side scanning produces images that resemble black-and-white photographs of the sea floor.

Multibeam and side scan sonar in use for bathymetric mapping. (NOAA, 2002).

A detailed report of the recent bathymetric survey of Crater Lake, Oregon, USA, is published by the USGS here.

7.15. Statistical Surfaces

Strategies used to represent terrain surfaces can be used for other kinds of surfaces as well. For example, one of my first projects here at Penn State was to work with a distinguished geographer, the late Peter Gould, who was studying the diffusion of the Acquired Immune Deficiency Syndrome (AIDS) virus in the United States. Dr. Gould had recently published the map below.

Oblique view of contour lines representing distribution of AIDS cases in the U.S. 1988. (Gould, 1989. © Association of American Geographers. All rights reserved. Reproduced here for educational purposes only).

Gould portrayed the distribution of disease in the same manner as another geographer might portray a terrain surface. The portrayal is faithful to Gould’s conception of the contagion as a continuous phenomenon. It was important to Gould that people understood that there was no location that did not have the potential to be visited by the epidemic. For both the AIDS surface and a terrain surface, a quantitative attribute (z) exists for every location (x,y). In general, when a continuous phenomenon is conceived as being analogous to the terrain surface, the conception is called a statistical surface.

7.16. Theme: Hydrography

The NSDI Framework Introduction and Reference (FGDC, 1997) envisions the hydrography theme in this way:

Framework hydrography data include surface water features such as lakes and ponds, streams and rivers, canals, oceans, and shorelines. Each of these features has the attributes of a name and feature identification code. Centerlines and polygons encode the positions of these features. For feature identification codes, many federal and state agencies use the Reach schedule developed by the U.S. Environmental Protection Agency (EPA).

Many hydrography data users need complete information about connectivity of the hydrography network and the direction in which the water flows encoded in the data. To meet these needs, additional elements representing flows of water and connections between features may be included in framework data (p. 20).

7.17. Theme: Transportation

Transportation network data are valuable for all sorts of uses, including two we considered in Chapter 4: geocoding and routing. The Federal Geographic Data Committee (1997, p. 19) specified the following vector features and attributes for the transportation framework theme:

7.18. Theme: Governmental Units

The FGDC framework also includes boundaries of governmental units, including:

• Nation
• States and statistically equivalent areas
• Counties and statistically equivalent areas
• Incorporated places and consolidated cities
• Functioning legal minor civil divisions
• Federal- or state-recognized American Indian reservations and trustlands
• Alaska native regional corporations

FGDC specifies that:

Each of these features includes the attributes of name and the applicable Federal Information Processing Standard (FIPS) code. Features boundaries include information about other features (such as road, railroads, or streams) with which the boundaries are associated and a description of the association (such as coincidence, offset, or corridor. (FGDC, 1997, p. 20-21)

7.19. Theme: Cadastral

FGDC (1997, p. 21) points out that:

Cadastral data represent the geographic extent of the past, current, and future rights and interests in real property. The spatial information necessary to describe the geographic extent and the rights and interests includes surveys, legal description reference systems, and parcel-by-parcel surveys and descriptions.

However, no one expects that legal descriptions and survey coordinates of private property boundaries (as depicted schematically in the portion of the plat map shown below) will be included in the USGS National Map any time soon. As discussed at the outset of Chapter 6, this is because local governments have authority for land title registration in the U.S., and most of these governments have neither the incentive nor the means to incorporate such data into a publicly-accessible national database.

Plat maps are supplementary records that depict property parcel boundaries in graphic form. The geometric accuracy of plats is notoriously poor. The investment required to convert plat maps to properly georeferenced digital data is substantial. Many local governments have converted these records to digital form, or are in the process of doing so.

FGDC’s modest goal for the cadastal theme of the NSDI framework is to include:

…cadastral reference systems, such as the Public Land Survey System (PLSS) and similar systems not covered by the PLSS … and publicly administered parcels, such as military reservations, national forests, and state parks. (Ibid, p. 21)

FGDC’s Cadastral Data Content Standard is published here.

The colored areas on the map below show the extent of the United States Public Land Surveys, which commenced in 1784 and took nearly a century to complete (Muehrcke and Muehrcke, 1998). The purpose of the surveys was to partition “public land” into saleable parcels in order to raise revenues needed to retire war debt, and to promote settlement. A key feature of the system is its nomenclature, which provides concise, unique specifications of the location and extent of any parcel.

Extent of the U.S. Public Land Survey (Thompson, 1988).

Each Public Land Survey (shown in the colored areas above) commenced from an initial point at the precisely surveyed intersection of a base line and principal meridian. Surveyed lands were then partitioned into grids of townships each approximately six miles square.

Townships are designated by their locations relative to the base line and principal meridian of a particular survey. For example, the township highlighted in gold above is the second township south of the baseline and the third township west of the principal meridian. The Public Land Survey designation for the highlighted township is “Township 2 South, Range 3 West.” Because of this nomenclature, the Public Land Survey System is also known as the “township and range system.” Township T2S, R3W is shown enlarged below.

Townships are subdivided into grids of 36 sections. Each section covers approximately one square mile (640 acres). Notice the back-and-forth numbering scheme. Section 14, highlighted in gold above, is shown enlarged below.

Inidividual property parcels are designated as shown above. For instance, the NE 1/4 of Section 14, Township 2 S, Range 3W, is a 160-acre parcel. Public Land Survey designations specify both the location of a parcel and its area.

The influence of the Public Land Survey grid is evident in the built environment of much of the American Midwest. As Mark Monmonier (1995, p. 114) observes:

The result [of the U.S. Public Land Survey] was an ‘authored landscape’ in which the survey grid had a marked effect on settlement patterns and the shapes of counties and smaller political units. In the typical Midwestern county, roads commonly following section lines, the rural population is dispersed rather than clustered, and the landscape has a pronounced checkerboard appearance.

For more information about the Public Land Survey System, see this article in the in the USGS’ National Atlas.

7.20. Summary

NSDI framework data represent “the most common data themes [that] users need” (FGDC, 1997, p. 3), including geodetic control, orthoimagery, elevation, hydrography, transportation, governmental unit boundaries, and cadastral reference information. Some themes, like transportation and governmental units, represent things that have well-defined edges. In this sense we can think of things like roads and political boundaries as discrete phenomena. The vector approach to geographic representation is well suited to digitizing discrete phenomena. Line features do a good job of representing roads, for example, and polygons are useful approximations of boundaries.

As you recall from Chapter 1, however, one of the distinguishing properties of the Earth’s surface is that it is continuous. Some phenomena distributed across the surface are continuous too. Terrain elevations, gravity, magnetic declination and surface air temperature can be measured practically everywhere. For many purposes, rasterdata are best suited to representing continuous phenomena.

An implication of continuity is that there is an infinite number of locations at which phenomena can be measured. It is not possible, obviously, to take an infinite number of measurements. Even if it were, the mass of data produced would not be usable. The solution, of course, is to collect a sample of measurements, and to estimate attribute values for locations that are left unmeasured. Chapter 7 also considers how missing elevations in a raster grid can be estimated from existing elevations, using a procedure called interpolation. The inverse distance weighted interpolation procedure relies upon another fundamental property of geographic data, spatial dependence.

The chapter concludes by investigating the characteristics and current status of the hydrography, transportation, governmental units, and cadastral themes. You had the opportunity to access, download, and open several of the data themes using viewers provided by USGS as part of its National Map initiative. In general, you should have found that although neither the NSDI or National Map visions have been fully realized, substantial elements of each is in place. Further progress depends on the American public’s continuing commitment to public data, and to the political will of our representatives in government.

7.21. Bibliography

Federal Geographic Data Committee (1997). Framework introduction and guide. Washington DC: Federal Geographic Data Committee.

Eischeid, J. D., Baker, C. B., Karl, R. R., Diaz, H. F. (1995). The quality control of long-term climatological data using objective data analysis. Journal of Applied Meteorology, 34, 27-88.

Gould, P. (1989). Geographic dimensions of the AIDS epidemic.Professional Geographer, 41:1, 71-77.

Maune, D. F. (Ed.) (2007). Digital elevation model technologies and applications: The DEM users manual, 2nd edition. Bethesda, MD: American Society for Photogrammetric Engineering and Remote Sensing.

Monmonier, M. S. (1982). Drawing the line: tales of maps and cartocontroversy. New York, NY: Henry Holt.

Muehrcke, P. C. and Muehrcke, J. O. (1998) Map use, 4th Ed. Madison, WI: JP Publications.

National Aeronautics and Space Administration, Jet Propulsion Laboratory (2006). Shuttle radar topography mission. Retrieved May 10, 2006, from http://www.jpl.nasa.gov/srtm

Goddard Space Flight Center, National Aeronautics and Space Administration (n.d.). Greenland’s receding ice. Retrieved Feburary 26, 2008, from http://svs.gsfc.nasa.gov/stories/greenland/

National Geophysical Data Center (2010). ETOPO1 global gridded 1 arc-minute database. Retrieved March 2, 2010, fromhttp://www.ngdc.noaa.gov/mgg/global/global.html

National Oceanic and Atmospheric Administration, National Climatic Data Center (n. d.). Merged land-ocean seasonal temperature anomalies. Retrieved August 18, 1999, fromwww.ncdc.noaa.giv/onlineprod/landocean/seasonal/form.html(expired)

National Oceanic and Atmospheric Administration (2002). Side scan and multibeam sonar. Retrieved February 18, 2008, fromhttp://www.nauticalcharts.noaa.gov/hsd/hydrog.htm

National Oceanic and Atmospheric Administration (2007). NOAA History. Retrieved February 27, 2008, fromhttp://www.history.noaa.gov/

Rabenhorst, T. D. and McDermott, P. D. (1989). Applied cartography: source materials for mapmaking. Columbus, OH: Merrill.

Raitz, E. (1948). General cartography. New York, NY: McGraw-Hill.

Ralston, B. A. (2004). GIS and public data. Clifton Park NY: Delmar Learning.

Thompson, M. M. (1988) Maps for america, 3rd Ed. Reston, VA: United States Geological Survey.

United States Geological Survey (1987) Digital elevation models. Data users guide 5. Reston, VA: USGS.

United States Geological Survey (1999) The National Hydrography Dataset. Fact Sheet 106-99. Reston, VA: USGS. Retrieved February 19, 2008 from http://erg.usgs.gov/isb/pubs/factsheets/fs10699.html

United States Geological Survey (2000) The National Hydrography Dataset: Concepts and Contents. Reston, VA: USGS. Retrieved February 19, 2008 fromhttp://nhd.usgs.gov/chapter1/chp1_data_users_guide.pdf

United States Geological Survey (2002) The National Map – Hydrography. Fact Sheet 060-02. Reston, VA: USGS. Retrieved February 19, 2008 fromhttp://erg.usgs.gov/isb/pubs/factsheets/fs06002.html Retrieved September 22, 2013 from http://pubs.er.usgs.gov/publication/fs06002

United States Geological Survey (2006a) Digital Line Graphs (DLG). Reston, VA: USGS. Retrieved February 18, 2008 fromedc.usgs.gov/products/map/dlg.html (In 2010 the site becamehttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/DLGs)

United States Geological Survey (2006b) GTOPO30. Retrieved February 27, 2008 fromedc.usgs.gov/products/elevation/gtopo30/gtopo30.html since moved to http://www1.gsi.go.jp/geowww/globalmap-gsi/gtopo30/gtopo30.html

United States Geological Survey (2006c) National Hydrography Dataset (NHD) – High-resolution (Metadata). Reston, VA: USGS. Retrieved February 19, 2008 fromnhdgeo.usgs.gov/metadata/nhd_high.htm

United States Geological Survey (2007). Vector data theme development of The National Map. Retrieved 24 February 2008 frombpgeo.cr.usgs.gov/model/ (expired or moved)

United States Geological Survey (2009) The National Map – Hydrography Dataset. Reston, VA: USGS. Retrieved September 22, 2013 from http://pubs.usgs.gov/fs/2009/3054/pdf/FS2009-3054.pdf

United States Geological Survey (2013) National Hydrography Dataset (NHD) – Get NDH Data. Reston, VA: USGS. Retrieved September 22, 2013 from http://nhd.usgs.gov/data.html