Comments on selecting a geographic information system for ...

Comments on Selecting a Geographic Information System for Environmental Management CURTIS E. WOODCOCK* CHI HO SHAM BARBARA SHAW Department of Geography and Center for Remote Sensing Boston University 675 Commonwealth Avenue Boston, Massachusetts 02215, LISA ABSTRACT / Many organizations in environmental fields stand to benefit from the use of a geographic information system (GIS). Selecting a GIS to implement within an organization can be a difficult task that is often required of people with little experience using a GIS. A framework for evaluating competing GIS considers cost, functionality, ease of use, future stability, development potential, support availability, and

Geographic information systems (GIS) are an emerging technology with great potential to aid work in such diverse fields as environmental management, facilities management, and science. GIS provide a computer-based method of storing, retrieving, analyzing, and displaying spatially organized data that promises to revolutionize the way people think about and use traditional map data. However, like many new technologies, GIS capabilities and limitations are understood by only a few experts. Organizations looking to implement a GIS are often left to select one with little or no prior experience in their use. This is a common problem associated with the adoption of innovative technologies, and often leads to disillusionment with the new technology, not because it is inappropriate, but because uninformed decisions were made in the implementation process or expectations were unrealistic. T h e fact that GIS are useful for such a wide range of applications is one reason that it is so difficult to select a GIS. Different systems are designed for different uses, types of data, and kinds of analyses. In addition to evaluating the hardware and software comprising a GIS, selecting a GIS also involves some introspection to determine the needs and expected uses of a GIS within the organization. We recently made GIS implementation recommenKEY WORDS: Geographic information system; Selection criteria; Cost; Functionality

*Author to whom correspondence should be addressed.

Environmental Management Vol. 14, No. 3, pp, 307-315

maintenance costs. Initial cost involves more than the actual purchase price of hardware and software; it includes the cost of building the data base and training users within the organization. Functionality refers to the depth and breadth of capabilities of a GIS. Issues involved in evaluating functionality include the appropriateness of raster vs vector processing and the ability to add your own software. Ease of use is important, but there is generally a trade-off with functionality. The degree of centralization of use of the GIS within the organization affects requirements for ease of use. GIS are rapidly evolving, and as a result it is important to select a system with high potential for future development. With the proliferation of companies offering GIS it is important to select one that is likely to survive and prosper. Similarly, the ability to find support in the forms of technical help, advice, and possibly even skilled employees can be significant.

dations for the North Atlantic Region (NAR) of the National Park Service (NPS) and thought it might be helpful to make available the considerations and thought processes that went into our recommendations (Sham and others 1988). Taken singly, these considerations are neither surprising nor particularly profound. Basically, they are common sense augmented by a little experience and research. However, it is our hope that a complete treatment of the subject will be helpful to individuals and organizations faced with similar decisions. If we bring to light previously unconsidered issues, we will have helped. Perhaps the biggest service we will provide is for those readers who have already considered all the issues addressed. In this case we can provide some measure of assurance to the reader that they are not forgetting something important. Approaches to quantifying the performance of different GIS for particular uses or applications have been presented (Goodchild and Rizzo 1987, Guptil 1989), as well as an article specifically devoted to the problems of large-scale GIS (Smith and others 1987). This article starts from a more basic consideration of the primary types o f GIS and is designed to help with the problem of determining which kind of GIS makes most sense for a particular organization. T h e approaches of Goodchild and Rizzo (1987) and Cuptil (1989) would be more useful later in the evaluation process when trying to select from a group of very similar GIS. Selecting the appropriate GIS is important, will 9 1990 Springer-Verlag New York Inc.

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have long-term effects on the organization involved, and will, to some extent, determine the level of GIS adoption and use within the organization. As a result, it is a decision that merits considerable attention and caution.

What is a GIS? A geographic information system (GIS) m a y b e defined as an integrated data-base management system that is capable of the input, storage, retrieval, analysis, output, and display of geographic or spatially indexed data. An operational GIS consists of computer hardware and software that allow use of data layers from a variety of sources such as aerial photography, topographic maps, land-use and zoning maps, satellite images, and field notes. They are distinguished from other data-base management systems (DBMS) by their explicit use of spatial location in the organization of data, the types of analyses performed, and the display of results. Geographic information systems differ from other spatially oriented computer programs, such as computer-aided drafting/design (CAD) systems, primarily as a result of the differences in their intended uses. GIS are designed to allow the analysis of data from multiple sources and to derive new information from existing data (Cowen 1987). Computer-aided drafting (CAD) systems are developed primarily for electronic drafting of objects for engineering drawings and designs. CAD is a displayoriented technology that allows users to manipulate objects in two and three dimensions and to update their drawings and designs electronically with ease and a high level of flexibility. A standard CAD system stores objects of different types (e.g., a floor plan outline, an electrical wiring plan and a plumbing system) as separate data layers. These data layers can then be displayed separately or jointly; for example, different layers can be printed using different colors or line styles. However, data developed for CAD are not structured for spatial analysis. Spatial relationships between various objects are not made explicit (e.g., intersection of lines, inclusion of points within an area, adjacency of areas, etc.). Such relationships are essential for the kinds of spatial analysis common to GIS. Furthermore, it is difficult to link attributes in a data base to specific spatial features in CAD. T h e r e is considerable published literature describing the general structure of a GIS, and the reader is referred to these for a better understanding of typical system components and their function (Burrough 1986, Ducker 1987, Parent and Church 1987, Dangermond 1988, Star and Estes 1990).

Why Use a GIS? Although GIS can conveniently be described as systems that handle map data, such a description is misleading. Indeed, a GIS can easily be used to make maps and organize map data, but the strength of a GIS lies in its analytical capabilities. The ability to derive new information from existing data is the main reason interest in GIS is growing so fast in the fields associated with natural resources, environmental management, and urban studies (Hill and others 1987, Harris 1989). Rather than try to explain how helpful analysis within a GIS can be, we feel more can be accomplished by summarizing a few examples from the literature of projects using GIS. This approach affords the opportunity to illustrate the range of analysis methods, variety of applications, and kinds of data commonly used in a GIS for environmental management.

Impact Analysis Johnson and others (1988) used a GIS to generate numerical descriptors of the spatial characteristics of wetlands for a statistical analysis of their relationship with water quality. This study of 15 watersheds over a 37-year period used digitized aerial photography and soils data to measure the area of wetland lost and to quantify the relationship of environmental variables such as stream order, slope, and soil characteristics to water quality. The GIS was used to measure each variable in each watershed for each year, so data, rather than a map, were the primary output. Principal components analysis of the original 31 variables produced eight components related to wetland areal extent, position within the watershed, type of wetland, and several upland characteristics. These eight principal components were used in a stepwise multiple regression to identify watershed characteristics related to water quality. Results show that changes in wetland extent and wetland position have different relationships with downstream water quality. Furthermore, with the GIS the importance of a particular wetland was evaluated in terms of its role in the functioning of the entire watershed on the basis of its size, position, and type.

Route Planning National Park Service planners used a GIS to find the best route to connect a planned visitor center at Great Basin National Park in Nevada to the local highway. Three major considerations directed the choice of possible paths: the degree of slope; the number of stream crossings; and the road's visibility

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from the visitor center. Elevation data were used to create slope maps that were divided into two categories, slopes between zero to seven degrees and those greater than seven degrees. Elevation data were further processed to identify the viewshed from the visitor center, and the park was reclassed into visible and hidden areas. Each of the classes in these new data layers, as well as those in the streams layer, was assigned a weight so that the undesirable categories-steep slopes, visible areas, and s t r e a m s - - h a d the highest values. Weights from each data layer were summed for each cell resulting in a "cost" map, wherein high values were undesirable (high cost) areas for the road. Finally, the least expensive paths to the highway in terms of the specified criteria were identified and the shortest of these was selected (Waggoner 1988).

Habitat Identification and Protection Scepan and others (1987) evaluated areas for release of captive-bred condors in a large region of California with a GIS. T h e environmental variables that characterize previously known condor habitat and the specific activities of the condors in these areas were identified and compared to data on current land cover/land use and the administration of the areas. Lyon and others (1987) also used a GIS for habitat evaluation. Land cover data were grouped one way to reflect the suitability of the overstory for nesting and a second time to reflect the value of the understory for foraging. Measures of the spatial diversity of the cover and seasonal innundation were also included in the evaluation. Each of these data layers was used in each of two models, one that evaluates habitat suitability for each cell within a 3 x 3 moving window, and one that evaluates groups of cells defined by measures of interspersion.

Environmental Monitoring and Change Detection On a regional scale, Dalsted (1988) identified current conditions in an area undergoing desertification using satellite imagery. Current "baseline" conditions for an area of the Sahel in Mauritania were identified with ground surveys that were essentially points on a small-scale base map in a GIS. These conditions were then extrapolated to characterize larger areas using multispectral scanner imagery and high-altitude aerial photography. Resulting maps of these conditions in the larger area included soil (erodibility), forest (density, condition, n u m b e r o f seedlings), and pasture (grazing animal carrying capacity). These measures were chosen with the idea that a series of forest maps

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would reveal long-term change, and the soil and pasture map series would reveal relatively rapid changes in conditions. Comparing such maps from different years would indicate where conditions are deteriorating on a regional scale. Despite some qualms about misregistration and conflicting class definitions, the author felt that overlays of these maps were useful. T h e example given is an overlay of forest seedling density, soil erosion hazard, and browsing, which may indicate the prognosis for desertitication on a regional basis. A GIS can, in some cases, be used to identify baseline conditions by combining data on environmental variables that control or relate to the variable of interest. Wiltshire and others (1986) developed an automated method of determining some basic drainage basin characteristics used in hydrologic modeling. Their specific discussion of use of a GIS involves transiorming a polygonal basin boundary to raster format and then summing the rainfall for each cell in the basin for a measure of the total amount of rainfall contained in the basin. Environmental Modeling A GIS expands the options for analyzing environmental change by providing the capabilities for modeling physical processes in a spatial context. For example, Wadge (1988) proposed the use of a GIS for predicting the occurrence and behavior of natural events. Existing physical models giving the probability of landslides and the paths of gravity flows can be run with the types of data and processing tools available in a G1S. One model assesses slope stability for predicting landslides. Elevation data is used to generate a slope map, land use and soil types are classified from sample plots, rainfall data are interpolated for an isohyet map and converted to an infiltration map, and first-order streams are weighted to serve as a proxy for local pore-water pressures. T h e processed data is used as input to a standard slope stability algorithm to evaluate the area represented by each cell. T h e GIS provides a platform for applying the model in a spatial manner across the landscape.

Raster and Vector Data Models T o understand the overall structure of a GIS, it is important to understand the nature of the data in the system and the way in which it is stored and analyzed. Currently there are two basic data models or data structures commonly used by GIS in the area of environmental management for capturing and storing

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spatial information and phenomena: the raster and vector data models. Although the debate on vector versus raster has been ongoing for two decades, a consensus on the best data structure has not been reached, and both are still commonly used (Peuquet 1984, Maffini 1987). Currently, GIS tend to be either vector or raster oriented, and a fully integrated system with a complete set of vector and raster capabilities is not yet available. Raster and vector GIS are quite different in the way they function and are most useful for different kinds of applications. Understanding whether a raster- or vector-based GIS is most appropriate for your organization is an important step toward selecting a GIS. The raster and the vector data structures each have their strengths and weaknesses in capturing a wide range of spatial phenomena. These strengths and weaknesses are directly associated with the different conceptualization (or model) of space adopted by the two data structures. T h e vector model represents spatial phenomena using a geometry of continuous space to position points (e.g., sites of rare plants), lines (e.g., a trail), and areas or polygons (e.g., land parcels). The raster model partitions space into cells of given dimensions, and spatial phenomena are recorded in the discrete space of each cell. For further discussion, see Peuquet (1984). T h e vector model is deeply rooted in the disciplines of surveying and cartography, which are based on the principles of geometry and trigonometry. The coordinates associated with objects are precise, being derived from a continuously measured coordinate system. A series of linked points represent a line, and connected lines may define a polygon. Using the vector model, it is possible to capture topological relationships of spatial phenomena. Topologic data structures use the same points, lines, and polygons as a conventional vector-based system but place them in a framework based on a set of relationships. For example, drainage basins can be represented by polygons, and in a topological data structure they could be stored to identify nested subbasins within larger basins, providing a new level of information useful for hydrologic studies. T h e vector model is ideal for representing spatial phenomena with definite linear dimensions, such as property boundaries, utility lines, and outlines of buildings. As a result, the vector data structure is commonly used for GIS applications involving facilities management, tax assessment, and transportation and urban planning. The vector model also is well suited for the situation where many attributes are required for the same map units. For example, for a land ownership map, the map units are the individual parcels of

land. However, it may be important to store many attributes for each parcel, such as the owner's name, address, phone number, year it was bought, assessed value, zoning information, current usage, etc. In this situation, a separate file of information containing this kind of textual data is associated with the spatial data defining the parcel. Map units that involve considerable spatial integration for determination of their map category are best suited to the vector model. Consider a land-use map. Within each map unit in the category residential there are many different kinds of land covers, such as streets, houses, lawns, trees, etc. When considering any small area within the map unit, it could be covered by any combination of these land covers. Several different land-use categories may share these same land covers. Urban map units, for example, will also have streets and trees and occasionally lawns, while forest categories obviously will have trees. It may not be apparent which land-use category is most appropriate until the whole map unit is considered. Taken separately, an area of trees in a vacant lot in a residential neighborhood might be called forest. However, if the map unit is the entire block, it would be best characterized as residential. This kind of generalized view of the landscape is handled well within a vector GIS. Since the individual map units are explicitly defined, the scale of the area to which categories are assigned is obvious. The raster model represents spatial phenomena in discrete spatial units (called grid cells, rasters, or pixels) allowing different data layers to be organized for the same locations. T h e ease of data coding, along with the simplicity of programming to perform multiple map overlays, has made the raster model an attractive alternative to the vector model. During the early stages of GIS development, computational and memory limitations of early computers made it difficult to produce high-quality raster output. T h e low level of precision of the data represented in raster format is a major criticism of raster-oriented GIS. However, in situations when natural phenomena are being mapped, boundaries are usually fuzzy and are imposed by interpretation of data. For example, the boundary o f a woodland may be positioned differently on the basis of different criteria used by surveyors and photointerpreters or among different photointerpreters. This type of problem is also applicable to other linear features such as contour lines. In most instances, contour lines are constructed based on spatial interpolation of point information. Therefore, a vector representation of this type of information is an abstraction and not inherently superior to a raster rep-

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resentation; in fact, the sharp and solid boundaries are often misleading. In addition, some of the most useful types of data for environmental analysis exist in raster format. Satellite images, which are being used for an increasing range of environmental applications, are recorded in raster format. T h e electromagnetic sensors that collect images on satellites systematically sample from the landscape. Each sample is a measurement taken over the instantaneous field of view of the sensor, and sampling is designed to produce images composed of measurements covering the entire landscape. Each measurement (or pixel) is then equivalent to an individual raster in a GIS data layer, making satellite images relatively easy to incorporate into a raster-based GIS. For input into a vector-based GIS, the satellite images usually need to be processed to yield units larger than individual pixels. T h e manner in which this generalization is done is neither standard across different GIS nor are the results always appropriate for future processing needs. Another useful kind of data for environmental analyses are digital elevation models (DEM), which are derived from contours on a topographic map, but come in digital form. T h e most commonly available digital elevation models are in raster format, in which an elevation value is stored for each raster (or grid cell). Generally this elevation value is derived from interpolating between contour lines scanned from a topographic map. T h e raw elevation values are valuable for many kinds of environmental analyses, including calculation of line-of-sight, flood plain mapping, wind circulation modeling, and for use as a surrogate for climate variables of precipitation and temperature in areas of high relief. T h e r e are also many kinds of information that can be derived from DEM. Slope angle and aspect (or orientation) are commonly derived from DEM and used for a wide range of environmental applications, such as evaluating landslide hazard and building suitability, or as indicators of ecological conditions in studies of distribution patterns of vegetation (Strahler 1981, Cibula and Nyquist 1987, Fleming 1988). Similarly, the shape of a slope, measured as the degree of concavity or convexity, can be used as an indicator of local water accumulation potential. DEM are also used extensively in modeling surface radiation budgets (Dozier 1980). T h e automated extraction of drainage networks and basin boundaries is one example of the recent expansion of the kinds of data being derived from DEM (Band

1990). For GIS projects relying heavily on remotely sensed imagery and/or digital elevation models, raster-based

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GIS are preferable for the simple reason that their original format matches the data structure of the GIS. Typically the map projection or the size of the pixels of the satellite imagery or DEM do not match the intended use, but they can be rubber-sheeted and resampled as necessary (Jensen 1986). Moreover, some of the newer raster-based GIS do not require all data layers to have the same size pixels (US Army Corps of Engineers 1988). Finally, it is reasonable to ask whether development in computer technologies will favor one or the other data models and whether one model will become dominant over the other. It seems unlikely that one data model will become sufficiently dominant to exclude the other. T h e vector model will probably continue to dominate applications such as automated mapping and facilities management. On the other hand, the raster model will continue to be used in the areas of resource and environmental management and remote sensing. T h e most likely changes will occur in the form of improved methods of integrating the two data models, making their combined use easier. For example, a n u m b e r of projects are being carried out to incorporate unique features of the vector data model in the Geographic Resources Analysis Support System (GRASS), which is a raster-based GIS in the public domain. T h e clear trend is to add the desirable components of vector-based systems to raster-based systems and vice versa.

GIS Evaluation Criteria T h e live primary criteria we fbund useful for evaluating hardware and software components of GIS we re: Cost

Functionality Ease of use Future stability and development potential Support availability and maintenance costs T h e evaluation process became a matter of weighing each of these concerns for each system. It would be nice to think that it was possible to simply provide a rating for each criterion for each GIS and sum the scores for each system to ensure an objective method of determining the best GIS. However, such an approach is unrealistically simple, as it makes assumptions about the relative importance of each category. Instead, we adopted an approach that removed systems from consideration on the basis of being unacceptable for some reason or combination of reasons.

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T h e remaining systems were then evaluated, and a decision for a recommendation was made on the basis of an overall appraisal of each system. One factor involved in the selection of a GIS concerns the relative merits of public domain software relative to commercial systems. T h e r e are advantages and disadvantages to each, and these will be discussed briefly in the following overview of each of the evaluation criteria.

Cost Needless to say, cost is always a critical factor. Since a GIS can cost from as little as a few thousand dollars to as much as several hundred thousand dollars, it is important to begin the selection process by understanding the scale of the system that is reasonable for the organization. In terms of hardware, this means deciding if a micro-, low-end mini-, or high-end minibased system fits your budget and needs. This relatively simple criterion can remove many systems from consideration in either direction; large organizations require computing power and functionality unavailable in micro-based systems; alternatively, most new investors in GIS cannot afford the top of the line. T h e cost factor weighs heavily with respect to the issue of public domain versus commercial systems. Commercial systems are consistently more expensive for the same hardware configurations. T h e reason is that companies support themselves primarily on the basis of the money they recover from their software (they also make money on hardware markups, but this is small relative to the markup of the software). Customers are paying not only for the development of the software, but essentially are supporting all the other departments in the company. T h e net effect is simple. Commercial systems are more expensive than public domain systems. I f cost were the only factor, the commercial versus public domain issue would be easy. T h e initial cost of the hardware and software is not the only factor contributing to cost of a GIS. Building the data base is usually the most expensive aspect of implementing a GIS. While it is still essential to build a data base regardless of the GIS selected, if it is more difficult in one GIS, the additional cost of building the data base may offset any savings associated with the initial purchase of the hardware and software. For example, if an organization is going to make extensive use of digital line graph (DLG) data from the USGS, buying a GIS without programs for reading DLG tapes would result in significantly higher costs for data base assembly. (Training expenses are similar in this regard, but will be discussed in the section Ease of Use.)

Functionality Functionality refers to the capabilities of systems to perform the functions involved in a GIS. Obviously, the more functionality the better. One interesting perspective on functionality concerns the raster versus vector issue. T h e limits of most systems' functionality generally falls in the domain (i.e., raster or vector) that is not the base of the system. In other words, the greatest limitations in functionality of vector-based systems are in their raster-processing capabilities and vice versa. This situation is logical but is important to keep in mind when evaluating systems. Most commercial systems are not designed with environmental management issues in mind. GIS may have started from that perspective, but the major commercial markets are in facilities management, tax assessment, etc. Thus, when selecting a GIS for environmental management, it is important to make sure that the analysis methods required are in the system. This can be particularly relevant for data input capabilities. It is important to be aware of the kinds of data that are likely to be used in your organization. Will DLG data and digital elevation models (DEM) be used primarily, or will there be a need for extensive manual or scan digitizing? Another issue involving functionality concerns the development of new applications programs. Is your organization likely to be content to use the basic tools as provided in existing GIS, or is there likely to be a need to develop new applications programs? I f there is a need to add new programs, it is important to evaluate how easily this can be accomplished within the GIS being considered. Some companies provide tools for programmers for an additional cost. Others will not provide source code at all or will expect to be paid for it, which can be quite expensive. Public domain systems usually come with the source code, although there may not be anyone to call when you are having trouble getting a new program to work. This is another example why a little introspection can help in the selection of an appropriate GIS.

Ease of Use Like functionality, ease of use is an unquestioned good and its importance should not be underestimated. T h e results of two surveys we conducted of Park Service employees involved with GIS underscored the importance of people being able to use the systems available to them. T h e r e are several examples of people who want to use GIS and have access to one, but do not use it effectively because it is difficuh to use and/or poorly documented. T h e issue of ease of use

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also can become an issue of cost. If extensive amounts of training are required before local personnel can do productive work on the system, it can add significantly to the cost of the system. This problem is compounded in the NPS because of the high mobility of employees within the NPS. Training is a commodity that moves with the employees; it does not stay with the GIS. This can lead to the costs associated with training being repeated. Related to this issue is the degree of centralization of use of the GIS within the organization. Will there be a few GIS experts doing all the GIS work in one place, or will a network of systems be installed in offices across the country? There is often some trade-off between ease of use and functionality. Systems that are very easy to use often loose the flexibility and power desired by sophisticated users. However, if dozens of people at dozens of locations need to use the GIS, it may be important for the system to be easy enough to use that it can be learned by reading a manual.

Future Stability and Development Potential A main point we would like to make in this article is that GIS are dynamic in nature. The data bases used in a GIS change through time, with a continued need for updating and addition of new data layers. Similarly, GIS technology is relatively new and rapidly changing, with new functions being added to systems on a continuing basis. Thus, it is not enough to evaluate available systems as they exist today. Some effort is required to determine both the stability of the system and its future growth potential. For these issues, the commercial versus public domain systems debate is important. T h e r e has been a proliferation in the number and variety of commercial GIS over the past few years that has seen the industry expand from a handful of companies that offer GIS products to dozens of companies, many devoted exclusively to GIS. One result of this sudden proliferation is that not all these companies will succeed. In fact many of the new companies are likely to go out of business in the next two or three years. This consolidation of the industry may be healthy in the long run, but it can leave victims in its wake. T h e failure of companies can be particularly devastating to its customers when specialized hardware is involved. It is often difficult and expensive, if not impossible, to get specialized computer hardware repaired when the company that produced the hardware goes out of business. The problem of broken hardware that renders a system useless after the failure of a GIS company is obviously a worst-case scenario that is fairly infre-

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quent. Often standard hardware from other companies (IBM, DEC, Sun, etc.) form the basis of GIS systems and hardware repairs can be done by the company that built the equipment. A larger problem than keeping the hardware working in the wake of a company failure is the future development of the software. Since the development of GIS is still progressing, it is important to have access to future software developments. With the failure of companies, new developments are not going to be available. Most commercial companies only provide the "executable" form of their software, not the actual code. Thus, it is impossible in many cases (and very difficult in most others) for customers to modify the software themselves. For the national parks in North Atlantic Region, this is particularly a problem because qualified personnel are not likely to be available to modify GIS software. T h e situation for public domain systems is quite different. Public domain systems tend to be based on standard hardware from large general-purpose computer companies. Thus, hardware repairs are more easily accomplished and are more likely to be available in the future. The more important consideration for public domain systems is the future development of the software. Software is the bread and butter of commercial GIS systems, so as long as the company stays in business software development will occur. Public domain software is usually developed within an agency for their internal needs. While these needs are often representative of others, there is no guarantee of future development. However, source code for public domain systems is usually available, so, even given the situation in which the agency that developed the software is not continuing its development, users of the software are not left completely in the cold.

Support Availability and Maintenance Costs Two factors need to be considered under the general heading of support availability. One concerns the ability to get technical help regarding the functioning of the system. This consideration overlaps slightly with the future stability issue, but more specifically concerns the ability on a day-to-day basis to get help concerning the routine functioning of the GIS. Examples of these kind of questions might be: "How do I print a vector file on the ink-jet plotter?" "How do I initialize the digitizer?" The second factor included in support availability concerns the ability to get help determining how to apply the GIS to the problems of interest to your organization. These questions might be: "How can I determine how many wells are down slope and within 50

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feet o f a road?" " H o w m u c h o f the total osprey habitat in the park will be affected by the proposed new road?" For large organizations, support o f this second kind can be viewed as the availability o f people to be hired to staff laboratories. Some effort to determine which GIS are being used in courses at universities that are training people could prove worthwhile. Maintenance costs are also o f two kinds: money and time. Monetary maintenance costs are closely tied to the original cost o f the hardware. Software updates can also be considered a maintenance cost. T h e often forgotten factor is the time required to maintain the system.

Conclusions T h e issues discussed here arose d u r i n g a study to determine which GIS could be best used in national parks in the N o r t h Atlantic Region. We evaluated several different parks as candidate sites for implementation o f a GIS and many different GIS software packages and feel that the experience gained in this process can be generalized to address questions about GIS acquisition and implementation in many different environments. T h e criteria used to evaluate various GIS are practical and direct: cost, functionality, ease of use, future stability and development potential, and support availability and maintenance. We have focused on an a p p r o a c h that accentuates the differences between public domain and commercial systems, as we feel it is an important consideration for organizations involved in environmental management. Similarly, we have emphasized introspection as an important part o f the process o f selecting a GIS. A clear understanding o f the expected uses and data sources is critical to making a good choice o f a GIS.

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