Abstract

The importance of spatially-related data is inherent to the National Environmental Policy Act (NEPA)-required Environmental Impact Statement (EIS). As such, the EIS provides significant opportunities to apply Geographical Information System (GIS) analysis to assist in completing the EIS. GIS databases are essentially spatially referenced databases that allow one to control the distribution of form over space and through time. Consequentially, the large datasets, spatial orientation requirements, and frequent changes to large EIS undertakings, such as ones required for major highway improvement projects, makes most of the 25 features analyzed in an EIS ideal for GIS analysis. A 51-mile proposed highway improvement project in southeastern West Virginia is discussed to demonstrate the uses, benefits, and limitations of GIS in addressing EIS requirements. Particular attention is given to the methods used to complete the GIS analysis for the EIS land use analysis and the development of two GIS-based archaeological predictive models.

Introduction

NEPA, EIS and GIS

NEPA (1969) was established by the federal government to ensure that all agencies of the federal government prepare an EIS when they undertake major federal actions, or fund or permit lower government actions significantly affecting the environment.

The EIS is used: (1) to ensure that federal agencies carefully consider significant environmental impacts arising from projects under agency jurisdiction; (2) to establish a procedure by which the public is given an opportunity for meaningful participation in the agency's consideration of the proposed action; and (3) to provide a record for legal challenges to the project. The EIS is designed to be a detailed and quantitative investigation which rigorously documents the findings of potential environmental impact of the proposed project and also addresses the public concerns.

The inherent spatial requirements of an EIS (i.e., the need to assess the impact of a proposed project on the environment) provides significant opportunities to apply GIS analysis for completing the EIS project. GIS analysis can greatly enhance the evaluation of EIS-required items. A case study of the use of GIS analysis for land use and archaeological resource (both prehistoric and historic) analyses is presented along with a discussion of the benefits and limitations of the GIS process.

Addressing land use and archaeological resources requires the quantification of the land to be affected by the project. Specifically, land use analysis requires estimating the acreage of various types of land use (e.g., agricultural and urban land) to be impacted. Archaeological resource analysis requires estimating the amount of land of varying potential (i.e., high-probability, moderate-probability, and low-probability) for the occurrence of significant prehistoric and historic archaeological sites.

Project Background

Project Area. The project discussed is a 51-mile section of a two-lane rural highway located in southeastern West Virginia (Figure 1). The study area includes some of the most beautiful and historic resources in West Virginia. It includes White Sulfur Springs, a resort built in 1834 and frequented by presidents and congressman. The study corridor follows the ancient Seneca Indian Trail, used by the Iroquois to control their large area of influence. This natural transportation corridor was also a crossroads for Union and Confederate troops during the Civil War. The result is that the area's resources are often beautiful, stately, and potentially of major historical significance.

Figure 1.Project Location

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Project Scope and Purpose. The project's goal was to improve the present two-lane highway to a needed four-lane highway while not substantially impacting the environmental resources in, and adjacent to, the project area.

GIS Perspective. A team of five consulting groups produced over 1.2 gigabytes of computerized data as part of the initial phase of this investigation. During this initial phase of the project, it was determined that an EIS of this magnitude would most satisfactorily (with respect to time and money) be addressed using a GIS, as compared to analyzing hardcopy maps with planimeters, and other conventional methods of analysis. Consequently, GIS software (ARCINFO and ARCVIEW) was brought into the project to organize, manipulate, analyze, and present these data to meet the rigorous requirements of the EIS evaluation.

Project Analysis

The project area and preliminary design improvements to the highway were provided to the project team to assess which improvements should be recommended to upgrade the highway. The first step was to determine the geographic and environmental data that would be needed to address the requirements of the EIS, and how to gather and assemble the data for processing. The next step was to input, manipulate, analyze, and display the data to meet the requirements of the project. Graphic display of the project was difficult, since the project area was oriented north to south, and measured approximately 51 miles long and only one-mile wide. This type of linear project could not easily be displayed for report size graphics. The project team decided to divide the project into fourths and orient from east to west for report presentation. Therefore, there would be four segments of approximately 13 miles each. This was achieved by CLIPping four equal lengths of the project area and TRANSFORMing them into an east - west projection. Example layouts used as they apply to the land use coverage are shown on Figures 2a and 2b. (Please note: words such as CLIPping and TRANSFORMing identify a specific ARCINFO command and an action performed with these data.)

Figure 2a.Land Use Within Project AreaPress here for additional figure information.	Figure 2b.Land Use Within Project Area (cont.)Press here for additional figure information.

Land Use Analysis

The objective of the analysis was to calculate the area of each type of land use that would be impacted by the highway improvements. The analysis employed the review of aerial photographs and the copying of various land uses observed on photographs to the approximately 200 preliminary design maps (developed at a scale of one-inch equals 200 feet) for the highway improvement project. Land use data transferred to these maps were checked during field investigations to verify the accuracy of the aerial photograph interpretations.

After checking the data, the information was prepared for scanning into a digital format. To initiate this process, a mylar overlay, approximately two feet by three feet, was placed on top of each design map and the land use shapes originally transferred to the design map were copied onto the mylar sheet. Each land use shape (or polygon) was given an identification number indicating the land use type it represented. Approximately 200 mylar sheets were scanned into computer images at a maximum rate, determined by the limitations of the scanning equipment, of 40 sheets per day.

The scanned images were converted into four GIS land use coverages, one coverage for each quarter of the project area. Table 1, Feature Attribute Data, was created for each type of land use identified on the mylar sheets. This table was joined to the land use coverages using the JOINITEM command with the land use identification number being the common "joining item." Quality control was conducted by overlaying and comparing the original mylar sheets with the hardcopy maps of land use coverages developed from the scanned data.

The next step of the analysis was to CLIP the land use coverage with the proposed highway modifications. The CLIPped coverage was then imported into ARCVIEW for tabular analysis of land use acreage to be potentially impacted. From ARCVIEW, the land use table was EXPORTed into a spreadsheet computer program for calculation of total potential impact acreage for each land use type. This calculation was conducted by sorting the table by land use type, and quantifying the potential impacted areas for each land use.

Figures 2a and 2b illustrate the land use within the project area, and Figure 3 is presented as a close-up of part of the project area to display the detail of the GIS analysis.

Figure 3.Detailed Land Use Impact Analysis

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GIS Benefits. Benefits attained from using a GIS analysis as compared to traditional methods of analysis included:

• increased work quality;
• increased work efficiency and labor-time reduction;
• increased flexibility of analysis for changes to highway design; and
• cost savings.

If the traditional method of analysis was followed for quantifying land use, it would have required the planimetering of approximately 200 hardcopy maps at a scale of one-inch equals 200 feet for the entire project area to delineate land use. The individual areas of each land use (or land use polygons) would then have needed to have been summed and this information would have required a manual quality control review. It is estimated that such a process would have required approximately four times the man-hours.

By using the GIS, and scanning hardcopy maps into the GIS database, significant portions of the manual labor were removed from the data capturing process, and the quality of the final product was increased as less handling of data was required to obtain the final desired information. Equally important was the fact that the data could be easily analyzed and the database could efficiently accommodate changes in the analysis criteria and highway modification design change.

GIS Limitations. A significant amount of time was required to establish communication among the various parties performing the EIS to establish proper GIS data format and required quality control. The rigorous format required by the GIS, although insuring a high-quality analysis, also provided little forgiveness in formatting the information. Establishing ARCINFO polygon topology, that is, establishing data in the proper format for spatial analysis, did require significant time, and had to be fully completed prior to conducting the analyses. Establishing the topology consisted of BUILDing and CLEANing the coverages by closing the land use polygons and assuring each polygon had only one label point and a unique user identification number. Also, converting the Intergraph files used by state highway departments to ARCINFO-compatible files was expensive and increased the size of the data files by several multitudes.

Archaeological Resources Analysis

Archaeological Predictive Models. The nature of historic and prehistoric archaeological analysis requires predictive modeling to identify areas likely to contain important archaeological sites. In conjunction with the background research, the predictive models for archaeological site locations were developed which identify areas of high-probability, moderate-probability, and low-probability for the locations of both prehistoric and historical archaeological sites. High-probability areas were those areas where archaeological sites are most likely to be encountered. Moderate-probability areas were defined as sensitive locations where archaeological sites could occur. Low-probability areas were defined as unlikely locations for the presence of archaeological sites.

Prehistoric Archaeological Predictive Model. The development of the predictive model for prehistoric site locations in the project area involved a review of the results of previous archaeological studies in the project vicinity, informant interviews, analysis of locational data on previously-recorded prehistoric sites in the project vicinity, and comparison of these data to the environmental characteristics of the project area.

Analysis of data on recorded sites in the project vicinity were combined with information from previous research and informant interviews to identify trends in prehistoric site locations. These trends in site location were then used to predict the occurrence of unrecorded prehistoric sites in the affected environment of the project, and establish the parameters for the predictive model. Specific types of data collected included physiographic and topographic setting; associated landforms, drainage, and soils; and the ages of the specific prehistoric components identified.

The predictive model criteria, formulated from trends in prehistoric site locations and used to determine areas of high-probability, moderate-probability, and low-probability of identifying prehistoric archaeological sites, was based on:

• slopes less than 20 percent;
• specific soil types; and
• distances to perennial surface waters.

ARCTIN was used to identify land with slopes less than 20 percent. This slope analysis was further constrained by selecting specific soil types within the land containing 20 percent slopes. To establish the soil coverage, data from 17 maps from the Soil Conservation Service (SCS) were scanned into digital format following the procedure used to establish the land use coverage (previously discussed). The digitally-formatted data were then reviewed, and specific soil types were identified for the model. Constraining the slopes was accomplished by using the RESELECT command in ARC, allowing for the extraction of only the soil type attributes that met the established soil criteria. Only the common land that met the model's slope and soil criteria were combined using the INTERSECT command to produce a single coverage. Once these areas were identified, the distances from streams and rivers were used to determine "high", "moderate", and "low" probability areas. Specifically, areas were BUFFERed such that 0 to 500 feet from surface water were considered high-probability, while areas 500 to 1,000 feet from surface water were considered moderate-probability, and areas greater than 1,000 feet from surface water were considered low-probability. The buffer polygons (i.e., probability areas) were INTERSECTed with the slope/soil criteria coverage to delineate the three probability areas for the project area. The three probability delineations were maintained as separate to allow efficient modification to the model's criteria, and in order to maintain quality control.

The resulting prehistoric predictive model defined areas with high-probability, moderate-probability, and low-probability for containing unrecorded prehistoric sites in the project site. A section of the project's prehistoric predictive model is illustrated in Figure 4.

Figure 4.Prehistoric Predictive Model

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Historic Archaeological Predictive Model. A historic resources survey of the project area was conducted to identify and evaluate the impacts to significant and potentially significant historic resources (buildings, structures, districts, and objects).

The formulation of the predictive model of historic site location began by gathering historic resources data collected during the background research for this project. All historic resources, including previously-recorded historic archaeological sites, historically mapped sites, historic roads, and previously-recorded standing historic resources, were documented. Newly surveyed historic resources within the project area were also incorporated in the model-building, since they contain important locational and potentially-important subsurface archaeological information.

The locations of historic resources were recorded on Unites States Geological Survey (USGS) 1:24,000-scale topographic maps. These locations were then computer scanned and digitized for incorporation into the GIS database. This produced a historic base map depicting the location of historic buildings, sites, structures, and courses of historic roads.

The criteria for establishing high-probability, moderate-probability, and low-probability in the model included proximity to historic roads, historic standing structures, and historically-mapped sites. These criteria were depicted on the GIS using multiple overlaying queries to identify those areas which met each of the criteria.

Buffer zones highlighting the critical features formed the basis of the predictive model. High-probability areas were defined as those locations within 300 feet of a mapped historic resource. These included, for example, recorded archaeological sites and historic standing structures, and sites appearing on historic maps of the area. Locations of moderate-probability included areas 300 to 600 feet from the mapped historic resources, or within 300 feet of a railroad or historic road. Distances beyond the above-mentioned buffered areas were considered low-probability.

The resulting historic predictive model, a section of which is illustrated in Figure 5, was capable of estimating the location of unrecorded historic archaeological sites present in the project area, and their probable location.

Figure 5.Historic Predictive Model

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GIS Benefits. The National Historic Preservation Act (NHPA) of 1996 requires all federal projects to undergo identification (Phase I), evaluation (Phase II), and mitigation (Phase III) of significant archaeological resources that are listed in, or are potentially eligible for listing, in NRHP. During an EIS, the first step is identification (i.e., Phase I); State Historic Preservation Officers (SHPOs) usually require Phase I testing of all possible highway modifications to identify sites in order to assist in selecting a preferred highway modification that has the least impact to archaeological resources. On large projects, this labor intensive effort of hand-digging shovel test excavations is timely and costly. If sophisticated GIS models are used at this stage, the SHPO can get a feeling for sensitive areas associated with a modification and select a preferred modification without costly, large-scale excavation. The SHPO will then require some limited testing to ground-truth the model and will only require a full Phase I survey on the preferred modification. This reduction of work, restricted to the preferred modification, is where both cost savings and time savings result. Time savings is very important since NEPA has certain time deadlines, milestones, and restrictions. Identifying sensitive areas early in the process through GIS, rather than waiting for traditional field testing, analysis, and reporting to be completed, is the greatest benefit to industry and federal agencies.

Conclusions

The land use and archaeological analysis examples demonstrate the benefits of efficiency and cost-savings which resulted from the use of GIS analysis with the NEPA process. Project feasibility and quality were expanded by utilizing the GIS data management and analysis tools. Limitations of the GIS stemmed from the effort required to establish proper communication among the various consulting groups working on the NEPA project in order to meet the software's rigorous database format. Consequentially, the true project benefits of using GIS software are most easily seen on large EIS projects, which result in extensive amounts of spatial data, numerous spatial orientation requirements, and require significant analysis flexibility. In such large projects, the efficiency of the GIS analysis becomes apparent after the communication protocol is established.

Acknowledgments

This paper is a result of many peoples efforts. Great appreciation is extended to Ben L. Hark, Chief, Environmental Services Section, and Jim Colby, Project Manager, of the West Virginia Division of Highways for their efforts and support on behalf of this paper. Also, special thanks to Diane B. Landers and George T. Reese for their contributions to, and review of, this work; Gay M. Gazaway for her organization of the paper; and Thomas D. Donovan, Jr., for his translation of the work into HTML format. Also, the authors appreciate the technical forethought of Jonathan C. Lothrop and Benjamin Resnick, GAI archaeologists, for their conceptual development of the archaeological models which provided the authors with a significant opportunity to utilize GIS. A final thanks is extended to Lucia Barbato of Esri, for her review of the paper and guidance through the GIS paper development process.

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