9: Integrating Geographic Data

Objectives

Chapter 9 should help prepare you to:

• Recognize the characteristics of geographic data that must be taken into account to overlay multiple data layers;
• Compare and contrast vector and raster approaches to site suitability studies;
• Have realistic expectations about what geographic data analysis can achieve.

Comments and Questions?

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9.2. 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.

9.3. Context

This section sets a context for two case studies that follow. First, I will briefly define low level radioactive waste (LLRW). Then I discuss the legislation that mandated construction of a dozen or more regional LLRW disposal facilities in the U.S. Finally, I will reflect briefly on how the capability of geographic information systems to integrate multiple data “layers” is useful for siting problems like the ones posed by LLRW.

9.4. Low Level Radioactive Waste

According to the U.S. Nuclear Regulatory Commission (2004), LLRW consists of discarded items that have become contaminated with radioactive material or have become radioactive through exposure to neutron radiation. Trash, protective clothing, and used laboratory glassware make up all but about 3 percent of LLRW. These “Class A” wastes remain hazardous less than 100 years. “Class B” wastes, consisting of water purification filters and ion exchange resins used to clean contaminated water at nuclear power plants, remain hazardous up to 300 years. “Class C” wastes, such as metal parts of decommissioned nuclear reactors, constitute less than 1 percent of all LLRW, but remain dangerous for up to 500 years.

The danger of exposure to LLRW varies widely according to the types and concentration of radioactive material contained in the waste. Low level waste containing some radioactive materials used in medical research, for example, is not particularly hazardous unless inhaled or consumed, and a person can stand near it without shielding. On the other hand, exposure to LLRW contaminated by processing water at a reactor can lead to death or an increased risk of cancer (U.S. Nuclear Regulatory Commission, n.d.).

Production trends and destinations of low level radioactive waste. (U.S. Nuclear Regulatory Commission, 2005).

Hundreds of nuclear facilities across the country produce LLRW, but only a very few disposal sites are currently willing to store it. Disposal facilities at Clive, Utah, Barnwell, South Carolina, and Richland, Washington, accepted over 4,000,000 cubic feet of LLRW in both 2005 and 2006, up from 1,419,000 cubic feet in 1998. By 2008 the volume had dropped to just over 2,000,000 cubic feet (U.S. Nuclear Regulatory Commisssion, 2011a). Sources include nuclear reactors, industrial users, government sources (other than nuclear weapons sites), and academic and medical facilities. (We have a small nuclear reactor here at Penn State that is used by students in graduate and undergraduate nuclear engineering classes.)

9.5. Siting LLRW Storage Facilities

The U.S. Congress passed the Low Level Radioactive Waste Policy Act in 1980. As amended in 1985, the Act made states responsible for disposing of the LLRW they produce. States were encouraged to form regional “compacts” to share the costs of locating, constructing, and maintaining LLRW disposal facilities. The intent of the legislation was to avoid the very situation that has since come to pass, that the entire country would become dependent on a very few disposal facilities.

Regional compacts formed by states in response to the LLRW Policy Act (U.S. Nuclear Regulatory Commission, 2011b).

State government agencies and the consultants they hire to help select suitable sites assume that few if any municipalities would volunteer to host a LLRW disposal facility. They prepare for worst-case scenarios in which states would be forced to exercise their right of eminent domain to purchase suitable properties without the consent of landowners or their neighbors. GIS seems to offer an impartial, scientific, and therefore defensible approach to the problem. As Mark Monmonier has written, “[w]e have to put the damned thing somewhere, the planners argue, and a formal system of map analysis offers an ‘objective,’ logical method for evaluating plausible locations” (Monmonier, 1995, p. 220). As we discussed in our very first chapter, site selection problems pose a geographic question that geographic information systems are well suited to address, namely, which locations have attributes that satisfy all suitability criteria?

9.6. Map Overlay Concept

Environmental scientists and engineers consider many geological, climatological, hydrological, and surface and subsurface land use criteria to determine whether a plot of land is suitable or unsuitable for a LLRW facility. Each criterion can be represented with geographic data, and visualized as a thematic map. In theory, the site selection problem is as simple as compiling onto a single map all the disqualified areas on the individual maps, and then choosing among whatever qualified locations remain. In practice, of course, it is not so simple.

There is nothing new about superimposing multiple thematic maps to reveal optimal locations. One of the earliest and most eloquent descriptions of the process was written by Ian McHarg, a landscape architect and planner, in his influential book Design With Nature. In a passage describing the process he and his colleagues used to determine the least destructive route for a new roadway, McHarg (1971) wrote:

…let us map physiographic factors so that the darker the tone, the greater the cost. Let us similarly map social values so that the darker the tone, the higher the value. Let us make the maps transparent. When these are superimposed, the least-social-cost areas are revealed by the lightest tone. (p. 34).

As you probably know, this process has become known as map overlay. Storing digital data in multiple “layers” is not unique to GIS, of course; computer-aided design (CAD) packages and even spreadsheets also support layering. What’s unique about GIS, and important about map overlay, is its ability to generate a new data layer as a product of existing layers. In the example illustrated below, for example, analysts at Penn State’s Environmental Resources Research Institute estimated the agricultural pollution potential of every major watershed in the state by overlaying watershed boundaries, the slope of the terrain (calculated from USGS DEMs), soil types (from U.S. Soil Conservation Service data), land use patterns (from the USGS LULC data), and animal loading (livestock wastes estimated from the U.S. Census Bureau’s Census of Agriculture).

Diagram illustrating the map overlay process used to evaluate potential agricultural pollution by watershed in Pennsylvania.

As illustrated below, map overlay can be implemented in either vector or raster systems. In the vector case, often referred to as polygon overlay, the intersection of two or more data layers produces new features (polygons). Attributes (symbolized as colors in the illustration) of intersecting polygons are combined. The raster implementation (known as grid overlay) combines attributes within grid cells that align exactly. Misaligned grids must be resampled to common formats.

Map overlay is a procedure for combining the attributes of intersecting features that are represented in two or more georegistered data layers.

Polygon and grid overlay procedures produce useful information only if they are performed on data layers that are properly georegistered. Data layers must be referenced to the same coordinate system (e.g., the same UTM and SPC zones), the same map projection (if any), and the same datum (horizontal and vertical, based upon the same reference ellipsoid). Furthermore, locations must be specified with coordinates that share the same unit of measure.

9.7. Pennsylvania Case Study

In response to the LLRW Policy Act, Pennsylvania entered into an “Appalachian Compact” with the states of Delaware, Maryland, and West Virginia to share the costs of siting, building, and operating a LLRW storage facility. Together, these states generated about 10 percent of the total volume of LLRW then produced in the U.S. Pennsylvania, which generated about 70 percent of the total produced by the Appalachian Compact, agreed to host the disposal site.

In 1990, the Pennsylvania Department of Environmental Protection commissioned Chem-Nuclear Systems Incorporated (CNSI) to identify three potentially suitable sites to accommodate two to three truckloads of LLRW per day for 30 years. CNSI, the operator of the Barnwell South Carolina site, would also operate the Pennsylvania site for profit.

Sketch of the proposed Pennsylvania LLRW disposal facility (Pennsylvania Department of Environmental Protection, 1998).

CNSI’s plan called for storing LLRW in 55-gallon drums encased in concrete, buried in clay, surrounded by a polyethylene membrane. The disposal facilities, along with support and administration buildings and a visitors center, would occupy about 50 acres in the center of a 500-acre site. (Can you imagine a family outing to the Visitors Center of a LLRW disposal facility?) The remaining 450 acres would be reserved for a 500 to 1000 foot wide buffer zone.

The three stage siting process agreed to by CNSI and the Pennsylvania Department of Environmental Protection corresponded to three scales of analysis: statewide, regional, and local. All three stages relied on vector geographic data integrated within a GIS.

9.8. Vector Approach

CNSI and its subcontractors adopted a vector approach for its GIS-based site selection process. When the process began in 1990, far less geographic data was available in digital form than it is today. Most of the necessary data was available only as paper maps, which had to be converted to digital form. In one of its interim reports, CNSI described two digitizing procedures used, “digitizing” and “scanning.” Here’s how it described “digitizing:”

One aspect overlooked in the CNSI description is that operators must encode the attributes of features as well as their locations. Some of you know all too well that tablet digitizing (illustrated in the photo below left) is an extraordinarily tedious task, so onerous that even student interns resent it. One wag here at Penn State suggested that the acronym “GIS” actually stands for “Getting it (the data) In Stinks.” You can substitute your own “S” word if you wish.

Vector digitizing with a tablet (left); raster digitizing with a drum scanner (right) (USGS).

Compared to the drudgery of tablet digitizing, electronically scanning paper maps seems simple and efficient. Here’s how CNSI describes it:

I hope you noticed that CNSI’s description glosses over the distinction between raster and vector data. If scanning is really as easy as they suggest, why would anyone ever tablet-digitize anything? In fact, it is not quite so simple to “select the appropriate feature boundaries” from a raster file, which is analogous to a remotely sensed image. The scanned maps had to be transformed from pixels to vector features using a semi-automated procedure called raster to vector conversion, otherwise known as “vectorization.” Time-consuming manual editing is required to eliminate unwanted features (like vectorized text), correct digital features that were erroneously attached or combined, and to identify the features by encoding their attributes in a database.

In either the vector or raster case, if the coordinate system, projection, and datums of the original paper map were not well defined, the content of the map first had to be redrawn, by hand, onto another map whose characteristics are known.

9.9. Stage One: Statewide Screening

CNSI considered several geological, hydrological, surface and subsurface land use criteria in the first stage of its LLRW siting process. [View a table that lists all the Stage One criteria.] CNSI’s GIS subcontractors created separate digital map layers for every criterion. Sources and procedures used to create three of the map layers are discussed briefly below.

Areas underlain by limestone and other carbonate rocks were digitized from the Pennsylvania Geological Survey’s Geologic Map of Pennsylvania. (Chem-Nuclear Systems, 1991).

One of the geological criteria considered was carbonate lithology. Limestone and other carbonate rocks are permeable. Permeable bedrock increases the likelihood of ground water contamination in the event of a LLRW leak. Areas with carbonate rock outcrops were therefore disqualified during the first stage of the screening process. Boundaries of disqualified areas were digitized from the 1:250,000-scale Geologic Map of Pennsylvania (1980). What concerns would you have about data quality given a 1:250,000-scale source map?

Coastal flood plains were digitized from 100-year flood contours compiled from FEMA Flood Insurance Rate Maps onto USGS topographic maps. (Chem-Nuclear Systems, 1991).

Analysts needed to make sure that the LLRW disposal facility would never be inundated with water in the event of a coastal flood, or a rise in sea level. To determine disqualified areas, CNSI’s subcontractors relied upon the Federal Emergency Management Agency’s Flood Insurance Rate Maps (FIRMs). The maps were not available in digital form at the time, and did not include complete metadata. According to the CNSI interim report, “[t]he 100-year flood plains shown on maps obtained from FEMA … were transferred to USGS 7.5-minute quad sheet maps. The 100-year flood plain boundaries were digitized into the GIS from the 7.5-minute quad sheet maps.” (Chem Nuclear Systems, 1991, p. 11) Why would the contractors go to the trouble of redrawing the floodplain boundaries onto topographic maps prior to digitizing?

“Exceptional value watersheds” were delineated on topographic maps, then digitized. (Chem-Nuclear Systems, 1991).

Areas designated as “exceptional value watersheds” were also disqualified during Stage One. Pennsylvania legislation protected 96 streams. Twenty-nine additional streams were added during the site screening process. “The watersheds were delineated on county [1:50,000 or 1:100,000-scale topographic] maps by following the appropriate contour lines. Once delineated, the EV stream and its associated watershed were digitized into the GIS.” (Chem Nuclear Systems, 1991, p. 12) What digital data sets could have been used to delineate the watersheds automatically, had the data been available?

After all the Stage One maps were digitized, georegistered, and overlayed, approximately 23 percent of the state’s land area was disqualified.

9.10. Stage Two: Regional Screening

CNSI considered additional disqualification criteria during the second, “regional” stage of the LLRW siting process. [View a table that lists all the Stage Two criteria.] Some of the Stage Two criteria had already been considered during Stage One, but were now reassessed in light of more detailed data compiled from larger-scale sources. In its interim report, CNSI had this to say about the composite disqualification map shown below:

Composite map showing approximately 46 per cent of the state disqualified as a result of Stages One and Two of the LLRW site selection process. (Chem-Nuclear Systems, 1993).

CNSI added this disclaimer:

As I mentioned back in Chapter 2, CNSI representatives took some heat about the map scale problem in public hearings. Residents took little solace in the assertion that the data in the GIS were more truthful than the data depicted on the map.

11. Stage Three: Local Disqualification

Many more criteria were considered in Stage Three. [View a table that lists all the Stage Three criteria.] At the completion of the third stage, roughly 75 percent of the state’s land area had been disqualified.

One of the new criteria introduced in Stage Three was slope. Analysts were concerned that precipitation runoff, which increases as slope increases, might increase the risk of surface water contamination should the LLRW facility spring a leak. CNSI’s interim report (1994a) states that “[t]he disposal unit area which constitutes approximately 50 acres … may not be located where there are slopes greater than 15 percent as mapped on U.S. Geological Survey (USGS) 7.5-minute quadrangles utilizing a scale of 1:24,000 …” (p. 9).

Slope is change in terrain elevation over a given horizontal distance. It is often expressed as a percentage. A 15 percent slope changes at a rate of 15 feet of elevation for every 100 feet of horizontal distance. Slope can be measured directly on topographic maps. The closer the spacing of elevation contours, the greater the slope. CNSI’s GIS subcontractors were able to identify areas with excessive slope on topographic maps using plastic templates called “land slope indicators” that showed the maximum allowable contour spacing.

Fortunately for the subcontractors, 7.5-minute USGS DEMs were available for 85 percent of the state (they’re all available now). Several algorithms have been developed to calculate slope at each grid point of a DEM. As described in chapter 7, the simplest algorithm calculates slope at a grid point as a function of the elevations of the eight points that surround it to the north, northeast, east, southeast, and so on. CNSI’s subcontractors used GIS software that incorporated such an algorithm to identify all grid points whose slopes were greater than 15 percent. The areas represented by these grid points were then made into a new digital map layer.

9.12. Buffering

Several of the disqualification criteria involve buffer zones. For example, one disqualifying criterion states that “[t]he area within 1/2 mile of an existing important wetland … is disqualified.” Another states that “disposal sites may not be located within 1/2 mile of a well or spring which is used as a public water supply.” (Chem-Nuclear Systems, 1994b). As I mentioned in the chapter 1 (and as you may know from experience), buffering is a GIS procedure by which zones of specified radius or width are defined around selected vector features or raster grid cells.

Like map overlay, buffering has been implemented in both vector and raster systems. The vector implementation involves expanding a selected feature or features, or producing new surrounding features (polygons). The raster implementation accomplishes the same thing, except that buffers consist of sets of pixels rather than discrete features.

Buffer zones (yellow) surround vector and raster representations of a pond and stream.

9.14. Outcomes

To date, neither Pennsylvania nor New York has built a LLRW disposal facility. Both states gave up on their unpopular siting programs shortly after Republicans replaced Democrats in the 1994 gubernatorial elections.

The New York process was derailed when angry residents challenged proposed sites on account of inaccuracies discovered in the state’s GIS data, and because of the state’s failure to make the data accessible for citizen review in accordance with the Freedom of Information Act (Monmonier, 1995).

Pennsylvania’s $37 million siting effort succeeded in disqualifying more than three quarters of the state’s land area, but failed to recommend any qualified 500-acre sites. With the volume of its LLRW decreasing, and the Barnwell South Carolina facility still willing to accept Pennsylvania’s waste shipments, the search was suspended “indefinitely” in 1998.

To fulfill its obligations under the LLRW Policy Act, Pennsylvania has initiated a “Community Partnering Plan” that solicits volunteer communities to host a LLRW disposal facility in return for jobs, construction revenues, shares of revenues generated by user fees, property taxes, scholarships, and other benefits. The plan has this to say about the GIS site selection process that preceded it: “The previous approach had been to impose the state’s will on a municipality by using a screening process based primarily on technical criteria. In contrast, the Community Partnering Plan is voluntary.” (Chem Nuclear Systems, 1996, p. 3)

The New York and Pennsylvania state governments turned to GIS because it offered an impartial and scientific means to locate a facility that nobody wanted in their backyard. Concerned residents criticized the GIS approach as impersonal and technocratic. There is truth to both points of view. Specialists in geographic information need to understand that while GIS can be effective in answering certain well-defined questions, it does not ease the problem of resolving conflicts between private and public interests.

Meanwhile, a Democrat replaced a Republican as governor of South Carolina in 1998. The new governor warned that the Barnwell facility might not continue to accept out-of-state LLRW. “We don’t want to be labeled as the dumping ground for the entire country,” his spokesperson said (Associated Press, 1998).

No volunteer municipality has yet come forward in response to Pennsylvania’s Community Partnering Plan. If the South Carolina facility does stop accepting Pennsylvania’s LLRW shipments, and if no LLRW disposal facility is built within the state’s borders, then nuclear power plants, hospitals, laboratories, and other facilities may be forced to store LLRW on site. It will be interesting to see if the GIS approach to site selection is resumed as a last resort, or if the state will continue to up the ante in its attempts to attract volunteers, in the hope that every municipality has its price. If and when a volunteer community does come forward, detailed geographic data will be produced, integrated, and analyzed to make sure that the proposed site is suitable after all.

9.15. Conclusion

Site selection projects like the ones discussed in this chapter require the integration of diverse geographic data. The ability to integrate and analyze data organized in multiple thematic layers is a hallmark of geographic information systems. To contribute to GIS analyses like these, you need to be both a knowledgeable and skillful GIS user. The objective of this text, and the associated Penn State course, has been to help you become more knowledgeable about geographic data.

Knowledgeable users are well versed in the properties of geographic data that need to be taken into account to make data integration possible. Knowledgeable users understand the distinction between vector and raster data, and know something about how features, topological relationships among features, attributes, and time can be represented within the two approaches. Knowledgeable users understand that in order for geographic data to be organized and analyzed as layers, the data must be both orthorectified and georegistered. Knowledgeable users look out for differences in coordinate systems, map projections, and datums that can confound efforts to georegister data layers. Knowledgeable users know that the information needed to register data layers is found in metadata.

Knowledgeable users understand that all geographic data are generalized, and that the level of detail preserved depends upon the scale and resolution at which the data were originally produced. Knowledgeable users are prepared to convince their bosses that small-scale, low resolution data should not be used for large-scale analyses that require high resolution results. Knowledgeable users never forget that the composition of the Earth’s surface is constantly changing, and that unlike fine wine, the quality of geographic data does not improve over time.

Knowledgeable users are familiar with the characteristics of the “framework” data that make up the U.S. National Spatial Data Infrastructure, and and are able to determine whether these data are available for a particular location. Knowledgeable users recognize situations in which existing data are inadequate, and when new data must be produced. They are familiar enough with geographic information technologies such as GPS, aerial imaging, and satellite remote sensing that they can judge which technology is best suited to a particular mapping problem.

And knowledgeable users know what kinds of questions GIS is, and is not, suited to answer.

9.16. Bibliography

Associated Press (1998). South Carolina Says Pennsylvania Waste Not Wanted in State. Centre Daily Times, , November 28, pp. 1A.

Chem-Nuclear Systems, Inc. (1991). Pennsylvania low-level radioactive waste disposal facility site screening interim report, stage one — Statewide disqualification. Harrisburg, PA.

Chem-Nuclear Systems Inc (1993). Pennsylvania low-level radioactive waste disposal facility site screening interim report stage two — Regional disqualification. Harrisburg PA.

Chem-Nuclear Systems, Inc. (1994a). Pennsylvania low-level radioactive waste disposal facility site screening interim report, stage three — local disqualification. Harrisburg PA.

Chem-Nuclear Systems, Inc. (1994b). Site selection manual. S80-PL-007, Rev. 0

Chem-Nuclear Systems Inc. (1996). Community partnering plan: Pennsylvania low-level radioactive waste disposal facility. S80-PL-021, Rev. 0.

Chrisman, N. (1997). Exploring geographic information systems. New York: John Wiley & Sons.

McHarg, I. (1971). Design with nature. New York: Doubleday / Natural History Press.

Mertz, T. (1993). GIS targets agricultural nonpoint pollution. GIS World, April, 41-46.

Monmonier, M. (1995). Drawing the line: Tales of maps and carto-controversy. New York: Henry Holt.

Pennsylvania Department of Environmental Protection. (1998).Proposed model of the PA low-level radioactive waste disposal facility.

U.S. Nuclear Regulatory Commission. (n. d.). Radioactive waste: Production, storage, disposal (Report NUREG/BR-0216).

U.S. Nuclear Regulatory Commission. (2005). Radioactive Waste Statistics. Retrieved May 14, 2006, fromwww.nrc.gov/waste/llw-disposal/statistics.html (expired)

U.S. Nuclear Regulatory Commission. (2011a). Low-Level Waste Disposal Statistics. Retrieved November 30, 2011, fromhttp://www.nrc.gov/waste/llw-disposal/licensing/statistics.html

U.S. Nuclear Regulatory Commission. (2011b). Low-Level Waste Compacts. Retrieved November 30, 2011, from http://www.nrc.gov/waste/llw-disposal/licensing/compacts.html