an application of expert systems in urban planning: site selection and analysis

Comput., Environ. and Urban Systems, Vol. 13, pp. 243-254, 1989 0198-9715/89 $3.00 + .OO Printed in the USA. All rights reserved. Copyright 0 1989 Pergamon Press plc

AN APPLICATION OF EXPERT SYSTEMS IN URBAN PLANNING: SITE SELECTION AND ANALYSIS

Sang- Yun Han T. John Kim

University of Illinois at Urbana-Champaign

ABSTRACT. The purpose of this paper is to explain the workings and development process of a prototype Expert System for Site Analysis and Selection (ESSAS). It also discusses the implications of applying expert system techniques to site evaluation tasks. ESSAS is a prototype expert system with 240 decision rules in it and is designed to solve a portion of the problem undertaken to suggest that the approach is viable and a full system is achievable. To have practical values for practicing planners, however, an extensive field test will be needed. With an extensive field test, ESSAS may become an efficient and practical decision aid device for planners.

Urban planning is a multidimensional and multidisciplinary activity embracing so- cial, economic, political, anthropological, and technical factors. As the body of knowledge and the scope of urban activities grow, computers have been increasingly utilized in this field, mostly for the numerical analysis of urban problems.

Solutions for urban and regional planning problems, however, frequently require not only numerical analysis but also heuristic analysis, which in most cases depends on the planners’ intuitive judgment. Conventional programming techniques, which mostly deal with numerical analyses of data, lack the capability of incorporating heuristic or qualitative knowledge of planners into problem solving. Expert systems which permit the use of heuristic knowledge or rules of thumb of human experts through computer programs may provide a more effective way of supporting site evaluation tasks of planners.

To examine the usefulness of an expert system approach to the site evaluation problem, the objectives of this paper are two-fold: (1) to explain the functions and structure of Expert System for Site Analysis and Selection (ESSAS) developed for use by the master planners of the Army, and (2) to discuss the implications of applying expert systems to site evaluation problems.1

BACKGROUND OF BUILDING ESSAS

The master planners of the Army are responsible for planning future building proj- ects and renovations needed by the people who live and work at the installation, that is,

Requests for reprints should be sent to T. John Kim, Department of Urban and Regional Planning, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801.

243

244 S.-Y. Han and T. J. Kim

a military city for people who serve in the Armed Forces, their families, and civilian employees. Many different types of buildings and support facilities must be planned and built for use by the people living in the installation. Without being supported by effective computer systems, the task of a master planner, who must use heuristic knowledge and numerical routines in evaluating a potential site, is difficult since installations are often the size of large cities. Furthermore, installations contain many different facilities and infrastructures with a limited amount of land and other resources available for construction [see Kim, Han, & Stumpf (1987) for detailed information].

Each type of facility requires certain site conditions to ensure a long economic life and minimal adverse impacts. Environmental concerns as well as water, energy sup- plies, sewage and other requirements generated by additional people and equipment are important considerations. Remote sites may require a large capital investment to con- nect buildings to existing utilities and road networks. Zoning considerations and rela- tionships to existing buildings are important as well. The Army has many noisy or hazardous activities, such as weapons training ranges, which must be carefully separat- ed from other uses. Each installation has well-defined “Safety Zones” which restrict land uses to assure compatibility.

Master planners are responsible for most site selection tasks which occur in the military setting. They are familiar with the general land use plan for the installation, but may be unable to determine the suitable site without first collecting a substantial amount of data and analyzing them. Types of data include environmental data, cost of infrastructure, surrounding land use, accessibilities, soil conditions and other construction related data.

Previous attempts to develop operations research type models to support the evaluation tasks of the Army because the major concern of the master planners is not to select a site which minimizes adverse environmental impacts or construction costs or both. Rather, their concern is to select a site which avoids environmental, construction, and other critical problems as well as abides by numerous Army regulations and rules associated with site development.

Expert systems may be regarded as a more relevant approach to the site evaluation tasks of the Army because the major concern of the master planners is not to select a site which minimizes adverse environmental impacts or construction costs or both. Rather, their concern is to select a site which avoids environmental, construction, and other critical problems as well as abides by numerous Army regulations and rules associated with site development.

Thus, the critical part of developing ESSAS was a review of the Army regulations, field manuals, and design guides as well as in-depth interviews with the master planners. Specifically, its development required encoding site selection rules and the expertise of master planners into a knowledge base. To make the project more manageable, ESSAS is currently configured to evaluate the suitability of sites for administrative buildings only. Extensions of this initial knowledge base are now under consideration.

TASKS IN DEVELOPING ESSAS

The tasks involved in developing ESSAS are discussed in this section. The specific tasks are presented as five steps: (1) identification of important aspects of the problem, (2) conceptualization of the problem, (3) formalization of knowledge, (4) implementation of knowledge using production rules, and (5) testing of the system.

An Application of Expert Systems 245

identification of the Problem

In this phase, the type and scope of the problem, the required resources, and the goals and objectives are identified and then compared to the available resources and the interests of the researchers. The initial problem considered is to model the decision process for site selection for Army training ranges. Since this problem is too broad and requires access to confidential reports on the specification of Army equipment, the scope and complexity of the problem must be narrowed down into a more manageable size. The final subject selected is to model the decision process of the master planner in a specific military installation, and to capture the various expertise needed in selecting a site for administrative building construction. The required data are identified and a point of contact is made with a master planner in Fort Bragg, North Carolina.

Conceptualization of the Problem

Concepts, relations, and control mechanisms necessary to describe problem solving in site analysis and selection are decided during the conceptualization of the problem. Subtasks, strategies, and constraints related to this problem-solving task are also exam- ined. In order to make data gathering of site characteristics more feasible, the scope of the problem is narrowed down further between four sites of new construction. Determi- nation of the actual site selection process is accomplished at this stage after consulting several experts and many references.

Another major task involved in this stage is to incorporate Army regulations and guidelines applicable to installation planning for site evaluation and selection. These references include important topics such as environmental issues, construction and safety issues, compatible land uses, building, parking and open space requirements, and utility requirements. The site analysis and selection factors are cross-checked with available expertise in public domain literature.

The expert system building tool, Personal Consultant Plus, a product of Texas Instru- ments, Inc., is chosen at this stage. It utilizes rule-based knowledge representation methods and backward chaining inference mechanisms and runs on IBM compatible microcomputers.

Formalization of Know/edge

Formalization involves expressing the key concepts of the problem and relations in some structured way, within a framework of the chosen expert system building tool. It is determined that the expertise in site analysis and selection can be expressed in the form of IF-THEN rules, and the large knowledge base can be effectively organized into subareas using FRAMES of Personal Consultant Plus. Frames serve a general organiza- tional purpose by defining problem areas in the knowledge base. The criteria for site analysis are structured into four subframes: Environmental, Construction, Safety, and Local Land Use. Categorization of the site evaluation factors specified in Army regulations and manuals into these four subframes requires much effort.

Implementation of Know/edge

During implementation, the formalized knowledge is encoded into a computer program using IF-THEN rules, parameters, and frames. Parameters are specific facts or


pieces of information, and rules are logical statements written in IF-THEN format. Rules describe the logical relationship between parameter values. The data structure, inference rules, and control strategies are implemented using Personal Consultant Plus interfaced with Lotus l-2-3 for data base files and Autocad for graphic representation of data.

Testing

Testing involves evaluating the performance and utility of the prototype system and revising it as necessary. The system is tested to check if it makes conclusions that experts generally agree to consider items in the order that the expert prefers, and if the system’s explanations are adequate for describing how conclusions are reached.

KNOWLEDGE REPRESENTATION AND INFERENCE MECHANISM OF ESSAS

Like the majority of expert systems developed today, the knowledge representation method used in ESSAS is rule-based. A rule, in a generic form, looks like this:

IF<Premise 1 >and/or<Premise 2> . . . , THEN <Inference I> and/or < Inference 2 > . . . .

For some examples used in the system:

IF <the distance of the potential site to endangered species habitat is less than 1000 FT> , OR < the area of wetlands is more than 20% of total land area > , THEN <the environmental suitability level of the site is very poor > .

IF <the surrounding land use of the site is more than 50% industrial > , OR <the distance of the site to AIRPORT SAFETY ZONE is less than 1000 FT > , THEN <the local land-use suitability level of the site is prohibitive > .

In addition to the rules, the program has access to factual data. The system looks at the factual data such as distance of the site to major utilities, expected capacity of the building, and characteristics of the potential site. Personal Consultant Plus uses parameters to store information or facts in the knowledge base. In this system, there are three ways to obtain factual data by assigning values to a parameter:

1. Provided by the User: The system can ask the user for the parameter value. For example, the user is asked to provide his or her preferred density of building and geographic location of a potential site.

2. Obtained From External Data File: When the system needs factual data, it reads the external data file and sets the value for a parameter. In this system, site characteristic data, such as land area, distance to major utilities, and presence of incompatible land uses in the surrounding area, are obtained from the external data base.

3. Inferred bj the System: The system can generate new data by inferring from the data provided by the user or obtained from the external data base and by using the rules. For example, once the density of building, expected number of occupants, and number of floors in the building are provided by the user, it will


calculate the required land size for building construction using an expert rule encoded in the knowledge base, and then determine whether the land area of potential site exceeds the required land size.

In this expert system, the criterias used to evaluate potential sites are organized into four major areas, as more clearly described in a later section. Thus, the rules and parameters are divided into four groups using subframes of Personal Consultant Plus. The rules and parameters which are relevant to all areas are stored in the main frame, while the rules and parameters relevant only to a specific area (e.g., environmental criteria) are stored in a subframe. This utility of organizing the knowledge base into subframes is quite useful because it enables easy addition of subframes later as the subject area covered by the expert system increases.

Once the knowledge base is built through the use of rules, the system tries to obtain conclusions through its inference engine. The inference engine provided by Personal Consultant Plus uses a backward chaining (or goal-driven) inference method, which focuses on finding a rule to provide a necessary parameter value for confirming a hypothesis.

As an example of inference strategy, consider the following goal and rules:

GOAL: To Find RECOMMENDED-ACTION Rule-l : IF ENVIRONMENTAL-SUITABILITY of the site is ACCEPTABLE,

THEN RECOMMENDED-ACTION is USE-THE-SITE. Rule-2: IF DISTANCE-TO-HABITAT is less than 3000 FT,

THEN ENVIRONMENTAL-SUITABILITY is ACCEPTABLE.

Rule-3: IF SLOPE of the site is less than 8 PERCENT, and DISTANCE-TO-POWER-SOURCE is less than 1000 FT, THEN CONSTRUCTION-SUITABILITY is ACCEPTABLE.

In seeking a value for RECOMMENDED-ACTION, the system scans the knowledge base sequentially for the existence of the parameter, RECOMMENDED-ACTION in the THEN part of a rule. Rule-l above is such a rule. Once it locates Rule-l, the system attempts to prove the premises of this rule. The first premise is “ENVIRONMENTAL- SUITABILITY is ACCEPTABLE.” The system checks its working memory for a value for “ENVIRONMENTAL-SUITABILITY” and finding none, scans the knowledge base seeking a value for that parameter.

Rule-2 in the example contains a value for “ENVIRONMENTAL-SUITABILITY” in its THEN part. Thus, the system again tries to prove IF part of this rule. If Rule-2 succeeds, then it sets value for “ENVIRONMENTAL-SUITABILITY,” otherwise it continues checking other rules which contain the parameter, ENVIRONMENTAL- SUITABILITY, is their THEN parts. The second condition in Rule-l is checked in the same manner.

The system can solve multiple goals, one at a time. The list of goals used in ESSAS includes:

1. AREA-BUILDING: Find the land area required for building construction. 2. AREA-PARKING: Find the land area required for parking lots. 3. AREA-OPEN-SPACE: Find the land area required for open space provision. 4. SURROUNDING-LAND-USE: Provide surrounding land use information for

the site selected by the user in a digitized map form if the site is recommended.


5. CRITICAL-PROBLEM: Find the critical problems expected in using the site. 6. RECOMMENDED-ACTION: Recommend a desired action to the user regarding

the use of site.

FUNCTIONS OF ESSAS: HOW IT WORKS

As summarized in Figure 1, ESSAS initiates consultation by the user’s inputs and then tries to reach conclusions at the end of the user consultation. To reach conclusions, it has several goals to meet, such as: (1) to provide the user with the information inferred by system regarding the use of the potential site, and its suitability in terms of several different criteria, (2) to predict some possible serious problems for potential site use, and (3) to provide recommended system action regarding the use of the potential site.

The following is an explanation of the consultation procedures used in ESSAS:

1. The USER is asked to provide the expected capacity of building in terms of the number of occupants and vehicles, and the preferred density of buildings.

2. The SYSTEM calculates the spatial requirements of the site for building, parking, and open space construction, based on the user inputs and ESSAS knowledge on estimating spatial requirements.

3. The USER is asked to select a site for evaluation from a Fort Bragg land-use map provided by the system. This allows ESSAS to reach the external data base for the user selected site and to know the characteristics of that site. All potential sites are prespecified in the map and the external data base maintains site characteristic data for these potential sites.

4. Once the system has all the information it can get from the user and external data base, the SYSTEM starts reasoning to reach conclusions about the goals specified utilizing its knowledge base. First, it evaluates the site using critical rules in the knowledge base. An example of a critical rule is “IF the land area of the site is less than the required land area, THEN reject the site.” If a site under evaluation doesn’t satisfy any of the critical rules, the system immediately reaches a conclu- sion about the use of the site (i.e., DON’T USE THE SITE) and lists the critical problems detected by the system.

5. When a potential sites passes all the critical rules, the SYSTEM enters the first subframe, ENVIRONMENTAL, and evaluates the site using environmental criteria, such as, impacts on wetlands and endangered species habitat. The SYSTEM reports serious environmental problems as well as positive features regarding the use of the site. It also tries to determine an ENVIRONMENTAL SUITABILITY LEVEL of the site with which the system determines overall suitability of the site and the action recommended by the system at the end of consultation. Seven different levels of suitability are used here: EXCELLENT, VERY GOOD, GOOD, ACCEPTABLE, BAD, VERY BAD, and PROHIBITIVE. The system determines the suitability level based on the degree of environmental problems it detects.

6. After it completes solving the goals of the ENVIRONMENTAL frame (i.e., to find environmental problems, and environmental suitability levels), the SYSTEM enters the SAFETY subframe and evaluates the site using safety and health criteria such as noise level, expected hazards from airport, helipad, and muni-


User enters number of employees and autos

System calculates required building, parking, and open space area

I System evaluates potential site using critical rules I

Dropped by critical rule Report result

> and list problems

Passed all critical rules

I \/

I 1

Evaluate the site using ENVIRONMENTAL criteria:

WETLANDS, HABITATS, VEGETATION, RUNOFF, CLIMATE, AND SEWAGE

I

Evaluate the site using SAFETY criteria

NOISE. FIRE; HAZARDS FROM AIRPORT, HELIPAD, AND MUNITIONS

Evaluate the site using CONSTRUCTION criteria:

SLOPE, SOIL, COST OF UTILITIES: WATER,GAS AND HEATING

I

-f- Evaluate the site using LOCAL LAND USE criteria:

ROADS, SURROUNDING LAND USE, ACCESSIBILITY, EXPANSION POSSIBILITY

I

Report serious problems & noticeable positive features of the site

detected by the system

Report recommended action regarding the use of site:

USE THE SITE or DON'T USE THE SITE

with some certainty factor

FIGURE 1. Schematic Structure and Function of ESSAS.


tions storage. Using the knowledge base, it reports safety-related problems as well as attractive features of the site and determines a SAFETY SUITABILITY LEVEL.

7. The SYSTEM continues this process for the rest of subframes (CONSTRUC-

TION and LOCAL LAND-USE), and then returns to the main frame. 8. In the main frame, the SYSTEM reports the area needed for building, parking,

and open space, and retrieves from its data base the land-use map of the site which shows the area within l/z mile radius of the site for user information. Finally the SYSTEM, based on the suitability levels from the four subframes,

determines the action recommended by the system regarding the use of its conclu- sion: USE THE SITE or DON’T USE THE SITE with a determined certainty factor, and then summarizes the suitability of the site in terms of environmental, safety, construction, and local land use criteria. In determining the final action, the system relies on a set of rules which decide the final action based on the suitability levels from four subframes. For examples of such a rule: IF < ENVIRONMENTAL-SUITABILTY is EXCELLENT > AND <not PRO-

HIBITIVE in other criteria > , THEN < USE THE SITE > . IF <any criterion is PROHIBITIVE > , THEN < DON’T USE THE SITE > .

9. The USER can continue evaluating other sites by selecting any site from the land

use map with different user inputs. The USER can ask how the system reaches

certain conclusions by selecting HOW from the menu. The SYSTEM will list the

rules it used to generate rules. For example, when the user asks how the total land area required for building constuction was determined to be l,OOO,OOO square feet, the system will list the rules containing formulas calculating spatial requirements and its reference sources.

FUTURE EXTENSIONS OF ESSAS

The expert system, ESSAS, is a medium-sized research prototype system with 240 rules, designed to solve a portion of the problem undertaken to suggest that the approach is viable and full system development is achievable. To have practical value, an extensive field test is required to produce a more reliable system that better addresses the needs of the end-user.

One possible extension to ESSAS includes embedding some mechanism to resolve conflicting knowledge in the knowledge base. Since the expertise encoded into expert systems is normally obtained from multiple knowledge sources, it is fairly typical that several conflicting or different rules apply to the same problem in the reasoning process. Because conflict resolution strategies are not explicitly specified, currently ESSAS ap- plies the first rule encountered in the reasoning process when conflicts arise.

More advanced ways of resolving conflicts may be employing metarules which are explained as high-level rules about how to use rules or integrating analytical models into expert systems to resolve conflicts in the knowledge obtained from rules of thumb. The first approach is explained by Waterman, Hayes Roth, and Lenat (1983), D’Angelo, Guida, Pighin, and Tasso (1985), and Han (1988), and the second approach is proposed by Han (1988).

Another possible extension includes integrating database management systems into


ESSAS. It has been noted that storing large sets of data in rule format consumes a large computer memory and slows down the processing speed of the system. It is learned from ESSAS that to be a practical tool, expert systems need to be supported by relation- al database systems which effectively manage and process factual data. To save time and resources while increasing the productivity of the system, ESSAS might be reconfigured to use existing data base systems. Since the use of digitized maps can be useful for many planning problems including site selection problems, it will be also worth attempting to develop expert systems which read information directly from digitized maps and incorporate it into the knowledge base. Although ESSAS are currently designed to use maps digitized using Autocad during the consultation process, the interaction between ESSAS and the computer mapping program is limited. Coupling Landtrak or any other geographic information systems package into ESSAS may create more practical expert systems.

IMPLICATIONS OF APPLYING EXPERT SYSTEMS TO SITE EVALUATION PROBLEMS

In urban planning, optimization models and suitability analyses using overlay manipulation techniques have been dominant methods for the site selection and evaluation tasks. Some examples of optimization models used in this area include: Bammi, Bam- mi, and Paton (1976) which is formulated to minimize environmental impacts; Kim (1978) which devises optimal zoning schemes minimizing costs associated with com- modity production, transportation, and export; and Barber (1976) which provides a multiple-objective optimization model minimizing land development costs and energy consumption in transportation and maximizing residential accessibility. On the other hand, the suitability analyses use overlay manipulation techniques which combine a set of different map overlays to produce a composite suitability map as illustrated by McHarg (1971) and Steinitz, Parker, and Jordan (1976).

What is the importance of expert systems when these tools of optimization modeling and overlay analysis are already available? It may be argued that expert systems provide an alternative way of supporting site selection and evaluation tasks in situations: first, where traditional approaches are inadequate, and second, where traditional approaches are insufficient by themselves. To illustrate the first situation, consider the site evaluation problem dealt with by ESSAS. The users of ESSAS, namely, the master planners of the Army do not have clear objectives to be optimized in the site evaluation process. Rather, they want to select a site which complies with the Army regulations, field manuals, and directives from high-ranking officials, and conforms to the planners’ qualitative knowledge on site evaluation.

Obviously, optimization models are inadequate in this situation. In addition, inter- preting the Army regulations regarding site evaluation, for example, is not a matter of numerical manipulation of data of most algorithmic programming, but a matter of symbolic manipulation of data which is well suited for expert system applications.

In a situation where problem-solving objectives and strategies are not fixed, because, for example, the directives from decision makers frequently change and the knowledge of planners are enhanced through their new training and experiences, the expert system approach may be more suitable than the algorithmic programming approach. This argument is based on the distinctive nature of expert systems, that is, the separation of domain-specific knowledge from inference engine which is the reasoning mechanism for applying that knowledge to an instance of the problem. In a traditional algorithmic


programming, knowledge about problem domain which is normally expressed in the form of mathematical equations is mixed with strategies which control the flow of that knowledge using procedural statements, such as DO-WHILE, GOTO, STOP, IF, and ENDIF. While efficient search strategies including depth-first, hill-climbing, and other heuristic search strategies are common interests of both expert systems and algorithmic programming approaches the developing process of any expert systems mostly focuses on the development of knowledge base, leaving the search tasks to the built-in inference engine provided by the expert system development shells.

There is an important implication of separating knowledge base from inference mechanism in ESSAS. Most of all, it is easy to upgrade or modify systems by means of adding, deleting, and changing production rules in the knowledge base without worry- ing about the search mechanism. As previously illustrated, the production rules are the simple IF-THEN rules used to represent the knowledge in ESSAS. The following rule,

IF the proposed use of site is for administrative buildings, THEN the site must be outside the safety zones,

can be easily changed to,

IF the proposed use of site is for administrative buildings, THEN the site must be outside the safety zones and within 1,000 feet of the commanding offices.

The separation of knowledge from inference engine enables the easy modification of expert systems, which in turn enables repetitive applications of ESSAS with minor upgrades to the essentially similar problems.

The experiences from ESSAS indicate that expert systems may also be used to provide a leverage to the suitability analysis which employs traditional overlay manipulation techniques. As pointed out by Hopkins (1977, 1979-1980), the “rules of combination” method, which is based on the knowledge of experts, can be a valid and effective way of combining overlays, because it “state[s] in words the combination of levels of impacts that are determined to be preferred to other combinations (Hopkins, 1979-1980, p. 8)”

Because the process of any site evaluation tasks inevitably deals with several factors and criteria (safety, environmental, construction, and local land use criteria and numerous factors in ESSAS), it is essential for any computer-based system developed for site evaluation purposes to incorporate valid methods for the combination of different factors. The critical deficiencies of some commonly used overlay combination methods include adding ordinal scale numbers or assuming independence between different factors when they are interdependent (Hopkins, 1977).

To some degree, ESSAS implements “rules of combination” in its knowledge base. For example, consider the following two rules:

Rule 1: IF erosion potential is high and wetland is not present in the site, THEN environmental suitability is poor. Rule 2: IF erosion potential is low and wetland is present in the site, THEN environmental suitability is very poor.

These two rules imply that the combination of “high erosion potential” and “no wetland” is preferred to the combination of “low erosion potential” and “existence of


wetland.” This rule of combination is based on the knowledge of experts that preventing the erosion problem is less costly than saving wetlands. Although the use of heuristic rules of combination is not fully utilized in ESSAS, certainly ESSAS sheds light on how different decision factors are effectively combined by using the knowledge of experts and efficiently encoding it into the knowledge base of expert systems.

In summary, there are at least two important implications of applying expert systems in urban planning. First, the use of expert systems broadens the spectrum of planning problems which can be supported by computer-based systems. Expert systems can be useful in a situation where traditional problem-solving approaches are inadequate ei- ther because the problem to be addressed is not an optimization problem or because the problem-solving strategies frequently change. Secondly, expert systems may supplement existing techniques used for site evaluation tasks when the existing techniques are not sufficient by themselves. The use of “rules of combination” in ESSAS is proposed as an example which supplements traditional overlay techniques.

CONCLUDING REMARKS

The process of building an expert system is iterative and involves much trial and error. The prototype expert system ESSAS attempts to synthesize multidisciplinary activities of urban and regional planning into a comprehensive decision-making tool for site analysis and selection, as an alternative to the conventional techniques. It demon- strates that expert systems can be useful tools to professional planners for their site evaluation tasks by performing difficult or tedious tasks including reviewing critical regulations and rules governing environmental, safety and health, construction, and local land use factors.

The purpose of this paper is to provide a basis for speculating on the ways in which expert systems can be effectively used for professional planners. It is hoped that the development of ESSAS will shed light on the issues involved in building a detailed and comprehensive expert system to aid professional planners.

Acknowledgments: The authors would like to thank Dr. Moonja P. Kim, Mr. Jerry Brown, and Ms. Annette Stumpf of the U.S. Army Construction Engineering Research Laboratory (CERL) for their valuable comments on the earlier drafts. Partial support by CERL (DACA 88-86-D-0006-40) is also gratefully acknowledged.

NOTE

1. This paper does not review the expert systems in urban planning. For a general introduction to expert systems, see Goodall (1985) or Waterman (1986), and for the introduction to the use of expert systems in urban planning fields, see Ortolano and Perman (1987).

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an application of expert systems in urban planning: site selection and analysis

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