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GIS AND SPATIAL DECISION MAKING

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Spatial decision making problems are multi-facetted challenges. Not only they often involve numerous technical requirements, but may also contain economical, social, environmental and political dimensions that may have conflicting values. Solutions for these problems involve highly complex spatial data analysis processes and frequently require advanced means to address physical suitability conditions while considering the multiple socio-economic variables. Geographic information systems (GIS), Multicriteria Decision Making techniques (MCDM), and Expert Systems (ES) are the most common tools employed to solve these problems. However, each suffers from serious shortcomings. The need for combining the strengths of these techniques has prompted researchers to seek integration of GIS, MCDM and ES. A variety of strategies can be used for integrating GIS and these tools. These strategies range from loose coupling techniques to the recent advanced techniques of software interoperability. In this chapter the complexity of the spatial decision making is highlighted. Both traditional and advanced techniques for software systems integration are presented.
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In: Geographic Information Systems ISBN: 978-1-61209-925-5
Editor: Christopher J. Dawsen © 2011 Nova Science Publishers, Inc.
Chapter 3
GIS AND SPATIAL DECISION MAKING
Khalid A. Eldrandaly*,PhD, GISP
Faculty of Computers and Informatics, Zagazig University, Egypt
ABSTRACT
Spatial decision making problems are multi-facetted challenges. Not only they often
involve numerous technical requirements, but may also contain economical, social,
environmental and political dimensions that may have conflicting values. Solutions for
these problems involve highly complex spatial data analysis processes and frequently
require advanced means to address physical suitability conditions while considering the
multiple socio-economic variables. Geographic information systems (GIS), Multicriteria
Decision Making techniques (MCDM), and Expert Systems (ES) are the most common
tools employed to solve these problems. However, each suffers from serious
shortcomings. The need for combining the strengths of these techniques has prompted
researchers to seek integration of GIS, MCDM and ES. A variety of strategies can be
used for integrating GIS and these tools. These strategies range from loose coupling
techniques to the recent advanced techniques of software interoperability. In this chapter
the complexity of the spatial decision making is highlighted. Both traditional and
advanced techniques for software systems integration are presented.
Keywords: GIS, Spatial Decision Making, Expert Systems, MCDM, Systems Integration
Techniques, Interoperability.
1. INTRODUCTION
Spatial decision making is a routine activity that is common to individuals and to
organizations. People make decisions influenced by location when they choose a store to
shop, a route to drive, or a neighborhood for a place to live, to name but a few. Organizations
are not much different in this respect. They take into account the realities of spatial
*E-mail: khalid_eldrandaly@yahoo.com
Khalid Eldrandaly
2
organization when selecting a site, choosing a land development strategy, allocating resources
for public health, and managing infrastructures for transportation or public utilities
(Jankowski and Nyerges 2001).
Spatial decision making is a highly complex process of choosing among alternatives to
attain an objective or a set of objectives under constraints. It can be a structured process
involving problems with standard solution procedures, or an unstructured process consisting
of problems with no clear-cut solution procedures, or even semi-structured problems for
which combinations of standard procedures and individual judgments have to be used to find
a solution.
All these processes generally involve voluminous spatial and aspatial information,
structured and unstructured knowledge, and human valuation and judgment (Leung 1997).
Spatial decision-making problems are multi-facetted challenges. Not only do they often
involve numerous technical requirements, but they may also contain economical, social,
environmental and political dimensions that could have conflicting objectives. Malczewski
(1999) defined the main characteristics of spatial decision problems as follows:
1) A large number of decision alternatives.
2) The outcomes or consequences of the decision alternatives are spatially variable.
3) Each alternative is evaluated on the basis of multiple criteria.
4) Some of the criteria may be qualitative while others may be quantitative.
5) There are typically more than one decision maker (or interest group) involved in the
decision-making process.
6) The decision makers have different preferences with respect to the relative
importance of evaluation criteria and decision consequences.
7) The decisions are often surrounded by uncertainty.
Solving this complex type of decision problems usually requires an intelligent and
integrative use of information, domain specific knowledge and effective means of
communication (Leung 1997). Geographic information systems (GIS), multicriteria decision
making techniques (MCDM), and Expert Systems (ES) are the most common tools employed
to solve these problems. However, each suffers from serious shortcomings. GIS is a great tool
for handling physical suitability analysis. However, it has limited capabilities of incorporating
the decision maker’s preferences into the problem solving process. MCDM is the proper tool
for analyzing decision problems and evaluating alternatives based on a decision maker’s
values and preferences.
However, it lacks the capability of handling spatial data (e.g., buffering and overlay) that
are crucial to spatial analysis. Also ES, which is capable of addressing heuristic analysis,
lacks the capability of handling spatial data/knowledge. The need for combining the strengths
of these techniques has prompted researchers to seek integration of GIS, MCDM and ES.
Numerous mechanisms enabling interoperability between GIS and theses tools have appeared
over the years.
Examples range from primitive (although widely used) solutions such as simple, loose
coupling to much more sophisticated approaches, such as COM technology. In the following
sections, brief descriptions of GIS, ES, and MCDM are presented, and the different
techniques for integrating these tools are discussed.
GIS and Spatial Decision Making
3
2. GEOGRAPHIC INFORMATION SYSTEMS
2.1. Brief History of GIS
The day-to-day necessity of dealing with space and spatial relationships represents one of
the basic facets of human society. Geographic information systems evolved as a means of
assembling and analyzing diverse spatial data. These systems evolved from centuries of
mapmaking and the compilation of registers. The earliest known maps were drawn on
parchment to show the gold mines at Coptes during the reign (1292- 1225 B.C.) of Rameses
II of Egypt. At a later date, the Greeks acquired cartographic skills and compiled the realistic
maps. The Greek mathematician, astronomer, and geographer Eratosthenes (ca. 276-194
B.C.) laid the foundations of the scientific cartography i.e., the science, art, and technology of
making, using, and studying maps. The Arabs were the leading cartographers of the Middle
Ages. The Arabian geographer Al-Idrisi made a map of the world in 1154. European
cartography degenerated as the Roman Empire fell. Until the nineteenth century, geographical
information was used mostly for trade and exploration by land and sea and for tax collection
and military operations. New needs arose in step with evolving infrastructures, such as roads,
railways, etc., because planning these facilities required information about the terrain beyond
that commonly available. As planning advanced, specialized maps became more common. In
1838, the Irish government compiled a series of maps for the use of railway engineers, which
may be regarded as the first manual geographic information system. By the late 1950s and
early 1960s, secondgeneration computers using transistors became available and the first
computerized geographic information system appeared. The first GIS was the Canada
Geographic Information System (CGIS), designed in the mid 1960s as a computerized map
measuring system. CGIS was developed by Roger Tomlinson and colleagues for Canadian
land inventory. This project pioneered much technology and introduced the term GIS. The
rapid development of powerful computers led to an increasing acceleration in the use of GIS.
In the 1970s and 1980s, various systems evolved to replace manual cartographic
computations. Workable production systems became available in the late 1970s. GIS really
began to take off in the early 1980s, when the price of computing hardware had fallen to a
level that could sustain a significant software industry and cost-effective applications. The
market for GIS software continued to grow, computers continued to fall in price, and increase
in power, and the software industry has been growing ever since (Clarke 2001;Bernhardsen
2002; Longley et al. 2005).
2.2. Definitions of GIS
Many definitions of GIS have been suggested over the years in different areas and
disciplines. All GIS definitions recognize that spatial data are unique because geographic
location is an important attribute of activities, policies, strategies, and plans. Following are
some of these definitions:
Ducker (1979) defined GIS as “ a special case of information systems where the database
consists of observations on spatially distributed features, activities or events, which are
Khalid Eldrandaly
4
definable in space as points, lines, or areas. A geographic information system manipulates
data about these points, lines, and areas to retrieve data for ad hoc quires and analysis”.
Star and Estes (1990) defined GIS as “an information system that is designed to work
with data referenced by spatial or geographic coordinates. In other words, a GIS is both a
database system with specific capabilities for spatiallyreferenced data, as well as a set of
operations for working with the data”.
Burrough and McDonnell (1998) defined GIS as “a powerful set of tools for storing and
retrieving at will, transforming and displaying spatial data from the real world for a particular
set of purposes”.
Clarke (2001) defined GIS as “an automated system for the capture, storage, retrieval,
analysis, and display of spatial data”.
Davis (2001) defined GIS as “A computer–based technology and methodology for
collecting, managing, analyzing, modeling, and presenting geographic data for a wide range
of applications.”
Worboys and Duckham ( 2004) defined GIS as “A computer-based information system
that enables capture, modeling, storage, retrieval, sharing, manipulation, analysis, and
presentation of geographically referenced data”.
Whereas Longley et al. (2005) defined GIS as "A special class of information systems
that keep track not only of events, activities, and things, but also of where these events,
activities, and things happen or exist."
2.3. Major Components of GIS
Any functional GIS has six major components as shown in figure 1 (Zeiler 1999; Longley
et al.2005). These components are:
1) People - People are the most important component of a GIS. People must develop the
procedures and define the tasks the GIS will perform. People can often overcome
shortfalls in other components of the GIS, but the opposite is not true. The best
software and computers in the world cannot compensate for incompetence.
2) Data - Data, which are quite critical to GIS, contains both geographic and attribute
data. The availability and accuracy of data affect the results of queries and analysis.
3) Hardware - Hardware is the devices that the user interacts directly in carrying out
GIS operations, such as the computer, digitizer, plotter, etc. Hardware capabilities
affect processing speed, ease of use, and the types of available output.
4) Software - This includes not only GIS software, but also various database, drawing,
statistical, imaging, and other software programs.
5) Procedures - GIS analysis requires well-defined, consistent methods to produce
correct and reproducible results.
6) Network - Network allows rapid communication and sharing digital information. The
internet has proven very popular as a vehicle for delivering GIS applications.
GIS and Spatial Decision Making
5
Figure 1. Basic components of GIS (reproduced with permission, John Wileyand Sons, Ltd. Longley et
al., "Geographic Information Systems and Science", 2nd edition, 2005).
2.4. GIS Data
The ability of GIS to handle and process geographically referenced data distinguishes
GIS from other information systems. Geographically referenced data describe both the
location and characteristics of spatial features on the earth’s surface. GIS therefore involves
two geographic data components: spatial data relate to the geometry of spatial features and
attribute data give the information about the spatial features. A GIS organizes and stores
information about the world as a collection of thematic layers that can be linked by
geography. Each layer contains features having similar attributes, like streets or cities that are
located within the same geographic extent. This simple but extremely powerful and versatile
concept has proven invaluable for solving many real-world problemsfrom tracking delivery
vehicles to recording details of planning applications to modeling global atmospheric
circulation (Bolstad 2002). Data collection is one of the most time-consuming and expensive
GIS activities. There are many diverse sources of geographic data and many methods
available to enter them into a GIS such as digitizing and scanning of maps, image data, direct
data entry using GPS and surveying instruments, and transfer of data from existing sources
(Bernhardsen 2002; Bolstad 2002; Longley et al. 2005).
2.5. GIS Data Models
The real world is far too complex to model in its entirety within any information system,
so only specific areas of interest should be selected for inclusion within a given GIS
application.
Khalid Eldrandaly
6
Figure 2. Vector and Raster Data Models (adapted from Bolstad 2002).
Once a particular application area has been chosen, the next task is to select those
features which are relevant to the application and to capture information about their locations
and characteristics. In order to bring the real world into GIS, one has to make use of
simplified models of the real world. A geographic data model is a set of constructs for
describing and representing selected aspects of the real world in a computer. There are two
basic data models used in GIS; these models are (Zeiler 1999; Davis 2001; Bolstad 2002;
Bernhardsen 2002; Longley et al. 2005):
Vector Data Model: The basis of the vector model is the assumption that the real world
can be divided into clearly defined elements (features) each element consists of an
identifiable object with its own geometry of points, lines, or areas. Vector data represents the
shapes of features precisely and compactly as an ordered set of coordinates with associated
attributes. Points (e.g., wells) are recorded as single coordinate pairs, lines (e.g., roads) as a
series of ordered coordinate pairs, and polygons (e.g., census tracts) as one or more line
segments that close to form a polygon area. Vector models are particularly useful for
representing and storing discrete features such as buildings, pipes, or parcel boundaries.
Raster Data Model: In a raster model, the world is represented as a surface that is divided
into a regular grid of cells. The x, y coordinate of at least one corner of the raster are known,
so it can be located in geographic space. Raster models are useful for storing and analyzing
data that is continuous across an area. Each cell contains a value that can represent
membership in a class or a category, a measurement, or an interpreted value. Raster data
includes images and grids. Images, such as an aerial photograph, a satellite image, or a
scanned map, are often used for generating GIS data. Grids represent derived data and are
often used for analysis and modeling. They can be created form sample points or by
converting vector data. The smaller the cell size for the raster layer, the higher the resolution
and the more detailed the map. Both vector and raster data models are shown in figure 2.
2.6. GIS Functions
Any geographic information system should be capable of six fundamental operations in
order to be useful for finding solutions to real-world problems. A GIS should be able to
capture, store, query, analyze, display, and output data (Zeiler 1999, Bolstad 2002).
GIS and Spatial Decision Making
7
Capturing data - Data describing geographic features is contained in a geographic
database. The geographic database is an expensive and long-lived component of a GIS, thus
data entry is an important consideration. A GIS must provide methods for entering geographic
(coordinate) and tabular (attribute) data. The more input methods available, the more versatile
the GIS.
Storing data - There are two basic models used for geographic data storage: vector and
raster. A GIS should be able to store both types of geographic data.
Querying data - A GIS must provide tools for finding specific features based on their
location or attributes. Queries, which are often created as logical statements or expressions,
are used to select features on the map and their records in the database.
Analyzing data - Geographic analysis usually involves more than one geographic dataset
and requires working through a series of steps to reach a final result. A GIS must be able to
analyze the spatial relationships among multiple datasets to answer questions and solve
problems. There are many types of geographic analysis. The two common types of
geographic analysis are described below:
A. Proximity analysis - Proximity analysis uses the distance between features to answer
questions like:
1) How many houses lie within 100 meters of this water main?
2) What is the total number of customers within 10 kilometers of this store?
3) What proportion of the alfalfa crop is within 500 meters of the well?
GIS technology often uses a process called buffering, defining a zone of a specified
distance around features, to determine the proximity relationship between features.
B. Overlay analysis - The integration of different data layers involves a process called
overlay. At its simplest, this could be a visual operation, but analytical operations
require one or more data layers to be joined physically (i.e., combined into one layer
in the database). Overlay analysis could be used to integrate data on soils, slope, and
vegetation or land ownership data with tax assessment data.
Displaying data - A GIS also needs tools for displaying geographic features using a
variety of symbology. For many types of geographic analysis operations, the end result is best
visualized as a map, graph, or report.
Outputting data - Sharing the results of your geographic labor is one of the primary
justifications for spending resources on a GIS. Taking displays created through a GIS (maps,
graphs, and reports) and outputting them into a distributable format is a great way to do this.
The more output options a GIS can offer, the greater the potential for reaching the right
audience with the right information.
2.7. GIS Software
GIS software is constructed on the top of basic computer operating capabilities such as
security, file management, peripheral drivers, printing, and display management to provide a
Khalid Eldrandaly
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controlled environment for geographic information collection, management, analysis, and
interpretation. The GIS software employed in a GIS project has a controlling impact on the
type of studies that can be undertaken and the results that can be obtained. There are also far
reaching implications for user productivity and project costs. Today, there are many types of
GIS software product to choose from and a number of ways to configure implementations.
Longley et al. (2005) classify the main GIS software packages into four main types as
follows:
Desktop GIS software: Desktop GIS software owes its origins to the personal computer
and Microsoft Windows operating system and is considered the mainstream workhorses of
GIS today. It provides personal productivity tools for a wide Varity of users across a broad
cross section of industries. The desktop GIS software category includes a range of options
from simple viewers (such as ESRI ArcReader, Intergraph GeoMedia Viewer and MapInfo
ProViewer) to desktop mapping and GIS software systems (such as Autodesk Map 3D, ESRI
ArcView, Intergraph GeoMedia, and MapInfo Professional), and at the high-end, full-featured
professional editor/analysis systems (such as ESRI ArcGIS ArcInfo, Intergraph GeoMedia
Professional, and GE Smallworld GIS). Desktop GIS software prices typically range from
$1000- $20000 per user.
Server GIS: Server GIS runs on a computer server that can handle concurrent processing
requests from a range of networked clients. Initially, it focused on display and query
applications, but now offers mapping, routing, data publishing, and suitability mapping. Third
generation server GIS offers complete GIS functionality in a multiuser server environment.
Examples of server GIS include AutoDesk MapGuide, ESRI ArcGIS Server, GE Spatial
Application Server, Intergraph GeoMedia Webmap, and MapInfo MapXtreme. The cost of
server GIS products varies from around $5000-25000, for small to medium-sized systems, to
well beyond for large multifunction, and multiuser systems.
Developer GIS: Developer GIS are toolkits of GIS functions (components) that a
reasonably knowledgeable programmer can use to build a specific-purpose GIS application.
They are of interest to developers because such components can be used to create highly
customized and optimized applications that can either stand alone or can be embedded with
other software systems. Examples of component GIS products include Blue Marble
Geographics GeoObjects, ESRI ArcGIS Engine, and MapInfo MapX. Most of the developer
GIS products from mainstream vendors are built on top of Microsoft’s COM and .Net
technology standards, but there are several cross platform choices (e.g., ESRI ArcGIS
Engine) and several Java-based toolkits (e.g., ObjectFX Spatial FX ). The typical cost for a
developer GIS product is $1000 - $5000 for developer kit and $100-500 per deployed
application.
Hand-held GIS: Hand-held GIS are lightweight systems designed for mobile and field
use. A very recent development is the availability of hand-held software on high-end so-
called ‘smartphones’ which can deal with comparatively large amounts of data and
sophisticated applications. These systems usually operate in a mixed connected/disconnected
environment and so can make active use of data and software applications held on the server
and accessed over a wireless telephone network. Examples of Hand-held GIS include
Autodesk OmSite, ESRI ArcPad, and Intergraph Intelliwhere. Costs are typically around
$400-$600.
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3. GIS, ES, MCDM AND SPATIAL DECISION MAKING
3.1. GIS and Spatial Decision Making
The ultimate aim of GIS is to support spatial decision-making. A GIS system typically
has the following capabilities (Zhu and Healey 1992):
1. Describing spatial problems and their spatial relationships.
2. Storing and managing large quantities of complex and heterogeneous spatial data.
3. Using geographical data models for structuring the available information.
4. Providing spatial data handling and displaying facilities.
Malczewski (1999) analyzed the GIS capabilities for supporting spatial decisions in the
context of Simon’s decision making process framework which divides any decision making
process into three major phases: intelligence (is there a problem or opportunity for change?),
design (what are the alternatives?), and Choice (which alternative is best?). Malczewski
mentioned the following conclusions:
1. Commercially available GIS systems tend to focus on supporting the first phase of
the decision- making process through its ability to integrate, explore, and effectively
present information in a comprehensive form to the decision makers,
2. These available GIS systems have limited capabilities of supporting the design and
choice phases of the decision- making process, and
3. These systems provide a very static modeling environment and thus reduce their
scope as decision support tools- especially in the context of problems involving
collaborative decision-making.
Today, geographic information systems incorporate many state-of-the-art principles such
as relational database management, powerful graphics algorithms, and elementary spatial
operations such as proximity analysis, overlay analysis, interpolation, zoning and network
analysis. However, the lack of analytical modeling functionality and the low level of
intelligence in terms of knowledge representation and processing are widely recognized as
major deficiencies of current systems (Fischer 1994).
3.2. Expert Systems and Spatial Decision Making
Expert systems are fast becoming the leading edge of artificial intelligence (AI)
technology because of the need for such systems in commercial and scientific enterprises and
also because AI technology has evolved to the point where expert systems development has
become well understood and feasible in many domains. An expert system is a computer
program that embodies the expertise of one or more experts in some domain and applies this
knowledge to make useful inferences for the user of the system (Hayes-Roth et al 1983).
Firebaugh (1988) defined expert systems as" a class of computer programs that can advise,
analyze, categorize, communicate, consult, design, diagnose, explain, explore, forecast, form
concepts, identify, interpret, justify, learn, manage, monitor plan, present, retrieve, schedule,
Khalid Eldrandaly
10
test, and tutor. They address problems normally thought to require human specialists for their
solution." Expert systems (ES) perform decision-making tasks by reasoning using domain
specific rules that have been judged by an expert in his domain to be true. They are best
suited for ill-structured problems. The distinctive strength of ES can be summarized as
(Jackson 1990):
1. Handling imprecise data, incomplete and inexact knowledge.
2. Exploiting knowledge at the right time.
3. Explaining and justifying the reasoning that lead to a conclusion.
4. Changing or expanding knowledge relatively easily.
There is ample scope for applying ES technology in decision making processes. For
example, we may use knowledge representation techniques to characterize decision-making
domains, use heuristic methods to generate and evaluate decision options, apply inference and
reasoning to explain and justify decisions, etc.
Spatial decision making with expert systems began in the late of 1980s. Many expert
systems have been developed to solve various site selection problems that are heavily
dependent on human judgment and experience. These systems use symbolic knowledge to
construct human understanding of problems in the area of site selection and evaluation.
Because symbolic knowledge is not well suited to describe the spatial nature of site selection
problems, expert systems lack a mechanism to derive solutions based on spatial knowledge
(or knowledge about positional and topological characteristics) of different sites. Spatial
knowledge is critical to spatial reasoning and decision making in many site selection
applications (Jia 2000). Unfortunately, current expert systems can’t handle spatial knowledge.
They don’t have an appropriate method to encode and represent the spatial nature of
knowledge. Furthermore, they can’t deal with locators, spatial relations, and spatial reference
actions involved in spatial knowledge. Zhu and Healey (1992) asserted that expert systems
technology alone does not adequately support spatial decision making because it has the
following limitations:
1. Spatial decision making requires large volumes of spatial data. These data mainly
reside in GIS and not in ES. ES lack facilities for handling large-scale data sets.
2. Expert systems are concentrated on symbolic reasoning and do not provide good
arithmetic capabilities. Yet, arithmetic operations are required in spatial data
handling.
3. Expert systems lack spatial data handling capabilities such as buffering and overlay
which are unique and important to spatial analysis.
4. Expert systems do not provide facilities for spatial data representation and
visualization.
Table 1. contrasts the strengths and weakness typically observed in expert systems and
GIS .The advantages of integrating a GIS with an expert system have been recognized by a
number of authors (Zhu and Healey 1992, Fischer 1994, Lilburne et al.1996, Moore 2000).
Zhu and Healey (1992) argued that the integration of GIS and ES may avoid some of the
limitations and difficulties existing in each of them and the spatial decision process can be
made more effective within such integrated systems. They also mentioned that a conventional
GIS is very suitable to well-structured spatial problem solving, while the integration of GIS
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11
and ES offers a best approach to solving ill-structured spatial problems and to providing
knowledge of how to use and run the GIS. Lilburne et al. (1996) mentioned that domain
knowledge represented in an expert system together with spatial data found in GIS can
provide a decision support environment in which users are guided by the integrated system
towards useful recommendations. Fischer (1994) asserted that there were no longer any
questions that expert systems would be integral in building the next generation of intelligent
GIS. Moore (2000) has noted that the reason why there is plenty of scope for use of expert
systems in this subject area is that GIS without intelligence have a limited chance to
effectively solve spatial decision support problems in a complex or imprecise environment.
3.3. MCDM and Spatial Decision Making
Almost all decision problems involve the simultaneous consideration of several different
objectives that are often in conflict. Multicriteria problems with conflicting objectives have
encountered in several applications, such as facility location. The development of
multicriteria methods is actually relatively recent. Over the past 20 years there has been a
plethora of tools and techniques developed for solving these problems such as Analytic
Hierarchy Process (AHP), Goal Programming, Data Envelopment Analysis, etc. MCDM
techniques are decision support tools designed to analyze decision problems, generate useful
alternative solutions, and evaluate alternatives based on the decision maker’s values and
preferences. The general objective of these methods is to assist the decision-maker in
selecting the best alternative from the number of feasible alternatives under the presence of
multiple choice criteria and diverse criteria priorities (Eldrandaly, 2010). These techniques,
however, assume homogeneity within the study area, which is unrealistic in many spatial
decision making situations such as site selection problems.
Table 1. Comparison of some GIS and expert systems capabilities (adapted from
Lilburne et al.1996)
GIS
Expert Systems
Quantitative and Suited to structured
problems
Qualitative and Suited to unstructured
problems
Use geometric primitives, e.g., point, line,
polygon
Use symbols
Integrate data
Integrate knowledge
Do not easily handle incomplete data
Handles incomplete data and knowledge
Spatially capable
No spatial capability
Cope with large volume of data
Do not cope well with lots of data
No explanation facility
Explanation facility
Can not represent and manage knowledge
Can represent and manage knowledge
No inference or reasoning capabilities
Have inference engines
Algorithmic
Opportunistic
Variety of output maps/graphics
No mapping capability
Can efficiently perform geometrical
operations
Can not efficiently perform geometrical or
arithmetical operations
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Figure 3. Framework for spatial multicriteria decision analysis (adapted from Malczewski ,1999).
It also, lacks the capability of handling spatial data (e.g., buffering and overlay) that are
crucial to spatial analysis. Malczewski (1999) suggested that there is a need for an explicit
representation of geographical dimension in MCDM techniques. The combination of GIS and
MCDM capabilities could effectively solve this problem. Malczewski (1999) has proposed a
framework for spatial multicriteria decision analysis, as shown in figure 3.
4. INTEROPERABILITY AND SYSTEMS INTEGRATION TECHNIQUES
The concept of software interoperability is one of those buzzwords in the computer field
that means different things to different people (Eddon and Eddon 1998). According to
Goodchild et al. (1999) Interoperability means openness in the software industry, because
open publication of internal data structures allows software users to use different software
components from different developers to build their applications. It also means the ability to
exchange data freely between systems, because each system would have knowledge of other
systems’ formats. Interoperability also means commonality in user interaction, as system
designers build interfaces that can be customized to a look and feel similar to the user.
Wegner (1996) defined interoperability as “is the ability of two or more software components
to cooperate despite differences in language, interface, and execution platform”. Interoperable
systems are systems composed from autonomous, locally managed, heterogeneous
components, which are required to cooperate to provide complex services (Finkelstein 1998).
Although, Interoperability has been a basic requirement for modern information systems
environment for over two decades (Sheth 1999), it is a recent research agenda element of
geographic information science. To GIS users, interoperating GIS refers to the ability to
exchange GIS data and functionality free among systems. Such interoperability can be
GIS and Spatial Decision Making
13
achieved at three levels: technical (e.g. compatible data formats between systems), semantic
(e.g. consistent meanings of data across systems), and institutional (e.g. legal and economic
support for data sharing across organizations) (Goodchild et al. 1999). Major efforts in GIS
interoperability are associated with organizations such as Open GIS Consortium and
ISO/TC211 (Feng and Sorokine 2001). The development and deployment of successful
interoperability strategies requires standardization that provides the lingua franca needed for
the exchange and integration of information (Vckovski 1998). In the GIS community, the
Open GIS Consortium has used the well known industry-wide specifications for exchanging
data and functionality, such as COM (Component Object Model) by Microsoft and CORBA
(Common Object Request Broker Architecture) by the Object Management Group, as the
base standards to develop specifications for exchanging GIS data and functionality.
Interoperability is sometimes distinguished from integration, but at other times the two terms
are used almost interchangeably. Dictionary definitions suggest that any significant difference
between them lies in the degree of coupling between the entities. Thus, an integrated system
is sometimes considered to be more tightly coupled than a system composed of interoperable
components. Yet even this distinction suggests that perspective is a key factor in discussing
interoperability. Thus, when looked on from a distance, a system is perceived to be integrated,
but from the perspective of its constituent elements, they are interoperating with each other.
The issue of perspective is recursive, because the interoperable entities themselves may be an
integration of other constituents. Thus, the relationship of the observer to the constituent
makes a difference as to whether the appropriate term is integration or interoperability
(Brownsword et al 2004). We shall not make any further distinction between these terms in
the remainder of this study.
An extensive body of literature published since 1990 documents the need for additional
GIS functionality (Lilburne 1996). To fulfill this need, researchers give considerable interest
in integrating geographical information systems (GIS) with other specialist systems to meet
the requirements of advanced applications. Also, the integration of GIS and other
technologies such as expert systems will lead to new, richer approaches to problem solving
(Abel and Kilby 1994). In the context of a particular problem, systems integration essentially
seeks to fuse capabilities available in the individual systems and to provide some desired level
of usability (Chou and Ding 1992). Abel and Kilby (1994) argued that the available GIS and
modeling systems are complex systems which would be costly to re-implement or to re-
modify and consequently, there is some value in determining the limits of possible integration
using existing systems. They defined the system integration problem as “the problem which
concerned with the coupling of pre-existing systems (the components of the integrated
system) to fuse a desired set of capabilities with some targeted degree of usability of the
integrated system. While the pre-existing systems (components) themselves are to be taken as
not to be modified, systems integration typically involves the design of some specialist
components linkage components to facilitate coupling. Identifying the types of the linkage
components needed is then a core issue in the system integration problem.” Coupling is a
measure of the degree to which functions in one software package can be controlled directly
from another. It refers to the physical and logical connection between software packages in
the system implemented (Malczewski 1999). The degree of interoperability between an expert
system and a GIS will affect the ability of an integrated system to model the complexity of
the real world (Linlburne et al 1996). Numerous mechanisms enabling interoperability
between GIS and ES have appeared over the years. Examples range from simple solutions
Khalid Eldrandaly
14
such as, loose coupling techniques to much more sophisticated approaches, such as
Component Object Model (COM) technology and Ontology.
4.1. Loose (Shallow) Coupling
In this approach GIS and ES support each other to solve specific problems through
sharing data files written in ASCII or other standard file format by the use of file transfer
utilities (Goodchild et al. 1992). Using this approach, the GIS serves as a preprocessor or
postprocessor to the expert system and the expert systems could access to the data stored in
the GIS or produced by the GIS. However, this approach does not provide the ES with the
spatial data handling capabilities of the GIS. At this level of integration, each tool runs
independently, the user interfaces continue to be separated, and there is no need to write extra
software, only the file formats have to be adapted. However, manipulating the exchange files
tends to be cumbersome and error prone (Fedra 1996, Jun 1997). In addition, the approach
may not work if the data sets extracted from the GIS become too large to fit into the ES
database (Zhu and Healey 1992).
The following paragraphs summarize some of the systems developed using this approach.
Kirkby (1996) used loose coupling to integrate a GIS with an expert system to identify
and manage dry land salinization in South Australia. The developed system, Salt Manager, is
a UNIX-based computer software that integrates “off the shelf” commercially available GIS
(ARC/INFO), RDBMS (ORACLE), and ES (Harlequin Lisp works/ Knowledge Works
Environment) Software. The communication between the RDBMS and both the ES and GIS
is conducted via a standard interface file, while the communication between the GIS and ES
is conducted via an ASCII text file.
Jun (1997) designed an expert geographic information system for industrial site selection
by integrating GIS (ARC/INFO 7), expert system (CLIPS), and MCDM (AHP). The software
integration between all the modules is based on loose coupling and is handled by the ASCII
file transfer method.
Yialouris et al. (1997) followed loose coupling strategy to develop EXGIS, an integrated
expert geographical information system for soil suitability and evaluation. EXGIS consists of
two components: GIS (ARC/INFO) and Expert system shell. The expert system shell was
implemented in CLIPPER because the files produced by it (dBase III+ files) can subsequently
be processed by ARC/INFO.
4.2. Tight (Deep) Coupling
Tight (deep) integration means that one system provides a user interface for viewing and
controlling the application, which may be built from several component programs (Pullar and
Springer 2000). That is, tight coupling is to integrate ES with GIS using communication links
in such a way that the GIS appears to the ES as an extension of its own facilities, or vice
versa. One appears as the shell around the other. The system developed by this approach is
called a "tight coupled standalone system". A "tight coupled standalone system" can be either
a merged system with expert systems as a subsystem of GIS Functionality, or an embedded
system, where existing GIS facilities are enhanced with expert system functionality. The
GIS and Spatial Decision Making
15
second type of tight coupling is "expert command languages", where expert system reasoning
is added to GIS macro or command languages (Zhu and Healey 1992). Compared with loose
coupling, tight coupling is considered to be a more effective integration method as the
decision problem can be modeled using generic tools on a single integrated database.
However, the computations will not be optimal. Also, it sometimes causes serious problems
due to the complicated communication between GIS macro language and the user-developed
expert systems. The following paragraphs summarize some of the systems developed using
this approach.
Kristijono (1997) followed a tight coupling strategy to develop a knowledge-based GIS
for landscape suitability. The author used the available tools of ARC/INFO GIS to build his
system entirely within the GIS environment The following ARC/INFO tools were used to
design the system :( 1) ARCEDIT environment, (2) a combination of the logical expressions
and the commands of the ARCEDIT, and (3) the AML (ARC Macro Language) facilities.
The first tool was used to perform as a rule editor, the second tools were used to
transform the IF … THEN form of the production rules, and the third tool was used to control
the whole operation of the entire transformed production rule.
Corner et al (2002) used tight coupling in developing EXPECTOR, a method of
combining data and expert knowledge within a GIS to provide information on the occurrence
of spatially distributed attributes. The method has been implemented as a stand-alone
"Knowledge Editing" module coded in Visual Basic and interfaced with a GIS (ArcView)
both to derive information about the input spatial data and to communicate back the results of
its calculations. Total Probability Rule and Bayes Theorem were used as knowledge
representation mechanism and as the inference engine. The data processing and combination
phases are carried out in ArcView using routines written in Avenue (the ArcView scripting
language).
Yang et al (2006) developed a GIS expert system for modeling distribution of matsutake
mushrooms using tight coupling approach. The system was developed under ENVI-IDL
environment and Bayesian theory was used as the inference engine
4.3. Client/Server
Client/server technology refers to the software that allows a process to receive messages
from another process. These messages request services of the receiving system (the server).
The service might be to perform a specified action or to return some information to the
requesting system (the client). Both processes remain in memory concurrently, avoiding the
loss of performance that occurs when loading a system into memory every time of one of its
functions is required. There is no limit to the number of requests, nor are there any restrictions
on the types of requests that can be made. In client/server integration approach GIS and ES
communicate via a standard protocol such as DDE, OLE or PRC which enables them to send
and receive messages from other concurrently running systems. Functionality is interleaved,
dynamic and relatively full. Data may be transferred or shared and there may be one or two
interfaces (Lilburne 1996). The following paragraphs summarize some of the systems
developed using this approach.
Lilburne et al. (1996) used client/server technology in developing a spatial expert system
shell (SES) that integrates two commercial products: the GIS ARC/INFO v7 and the expert
Khalid Eldrandaly
16
system shell, Smart Elements. ARC/INFO v7 includes some commands which create a
framework for client/server communication with another process. Once a connection has been
initialized, messages can be sent between the processes. Smart Elements is a combination of a
hybrid frame, rule-based expert system called Nexpert Object, and a GUI developer kit called
Open Interface. It has an Application Programming Interface (API) which allows C routines
to access Smart Elements functions. SES was developed on a Solaris SUN Workstation
platform. Both ARC/INFO v7 and Smart Elements use Sun's ONCRPC client/server protocol.
Smart Elements is the client and ARC/INFO is the server. A combination of C and
ARC/INFO's macro language AML is used to develop the client/server interface between
ARC/INFO and Smart Elements.
Jia (2000) developed a conceptual framework for integrating ES and GIS using
Transmission Control Protocol/ Internet Protocol and Remote Procedure Calling
technologies. A software system (called IntelliGIS) implementing the method has been
developed. IntelliGIS was implemented by enhancing CLIPS (a rule and object-based expert
system shell) with ARC/INFO GIS and the ES-GIS interface developed in the research.
ARC/INFO performs as the GIS server and its Inter-Application Communications (IAC)
function is used for developing the spatial reference engine because the IAC function allows
direct "talk" between CLIPS and ARC/INFO, and it does not require text files for the talk.
The ES-GIS interface used Transmission Control Protocol/Internet Protocol (TCP/IP) and
Remote Procedure Calling technologies to integrate CLIPS and ARC/INFO.
Fedra and Winkelbauer (2002) developed a client/server DSS framework, RealTime
eXPert System (RTXPS), which integrates a forward chaining expert system and a backward
chaining system with simulation models and GIS for environmental and technological risk
assessment. To integrate the various information resources in an operational decision support
system, flexible client-server architecture is used, based on TCP/IP and the http protocol. The
central system, which runs the RTXPS expert system as the overall framework is connected
to a number of conceptual servers that provide high-performance computing and data
acquisition tasks.
4.4. Component Object Model Technology (COM)
To achieve interoperability between the systems, one must proceed with the
decomposition of the software system into small components that are available to other
applications (Bian 2000).
Leading commercial software vendors have adopted component-based software
development (CBSD) approach in their software design. CBSD approach focuses on building
large software systems by integrating previously existing software components as a way to
reduce development costs, improve productivity, and provide controlled systems upgrade in
the face of rapid technology evolution (Brown 2000). This approach, which is also called
componentware, is a further development of the object oriented programming. It adds to the
object oriented programming the concept of a highly reusable components.
In CSBD, the notion of building a system by writing code has been replaced with
building a system by assembling and integrating existing software components (Karlsson
1995). Brown (2000) defined a software component as “an independently deliverable piece of
functionality providing access to its services through interfaces”.
GIS and Spatial Decision Making
17
A component is a reusable piece of software in binary form that can be plugged into other
components from other vendors with relatively little effort (Eddon and Eddon 1998). A rather
small group of objects is joined into the component with a well defined interface. Inside the
component the objects can communicate with each other without any restrictions, but
communication with the outside world is only possible through the component interface. A
component acts like a black-box: the inner structure is hidden and protected from the outside
world by the component interface (Rebolj and Sturm 1999). That is, with COM, applications
interact with each other and with the system through collections of functions calls known as
interfaces as shown in figure 4.(a).
An interface is a strongly typed contract between a software component and a client that
describes the component's functionality to the client without describing the implementation at
all (Eddon and Eddon 1998). Each component can act as both client and server as shown in
figure 4.(b). A server is a component that exposes interfaces and therefore a list of functions
that a client can call (Lewis 1999). The main goal of the COM is to promote interoperability.
COM supports interoperability by defining mechanisms that allow applications to connect
(Eddon and Eddon 1998). COM specifies an object model and programming requirements
that enable COM objects to interact with other COM objects. These objects can be within a
single process, in other processes, or even on remote machines. They can be written in other
languages and may have been developed in very different ways. COM allows these objects to
be reused at a binary level, meaning that third party developers don’t require access to source
code, header files, or object libraries in order to extend the system (Zeiler 2001).
Leading commercial GIS software vendors have adopted component-based software
development (CBSD) approach in their software design and choose COM as the component
technology for their products. For example, ArcGIS Desktop (an integrated suite of
professional GIS application) developed by Environmental systems Research Institute
(ESRI), is based on a common modular component-based library of shared GIS software
components called ArcObjects. ArcObjects includes a wide variety of programmable
components which aggregate comprehensible GIS functionality for developers (Zeiler 2001).
Also, Leading commercial ES software vendors have adopted COM technology in designing
their software. Visual Rule Studio® (an object-oriented COM-compliant expert system
development environment for windows) developed by RuleMachines is an example. Visual
Rule Studio® solves the problem of software interoperability by allowing the developers to
package rules into component reusable objects called RuleSets. By fully utilizing OLE and
COM technologies, RuleSets act as COM automation servers, exposing RuleSet objects in a
natural COM fashion to any COM compatible client.
Client
Requests
Service
COM Object
Interface
Interface
(a)
(b)
Figure 4. COM Architecture (adapted from Lewis 1999).
Khalid Eldrandaly
18
Visual Rule Studio installs as an integral part of MS Visual Basic 6.0, professional or
enterprise editions, and appears within the visual Basic as an ActiveX Designer. RuleSets can
be complied within Visual Basic .EXE, .OCX, or .DLL executables and used in any of the
ways the developers normally use such executables (RuleMachines 2002). The following
paragraphs summarize some of the systems developed using COM technology.
Eldrandaly et al (2003) used COM technology to develop an intelligent GIS-based spatial
decision support system for industrial site selection. A prototype was developed using three
COM-compliant commercially available software packages: Visual Rule Studio®, ArcGIS®
8.2, and Microsoft® Excel 2002. Visual Rule Studio® was used to develop the expert system
component. ArcGIS® provided the GIS platform to manage the spatial data and conduct the
required spatial analysis operations. Microsoft® Excel provided the tools to implement the
AHP component. In addition, Microsoft® Visual Basic® 6.0 was used to provide the shell for
the COM integration and to develop the system’s user interface.
Tsamboulas and Mikroudis (2005) used COM technology in developing a DSS (TRANS-
POL) for evaluating transportation polices and projects. It was developed using four COM-
compliant commercially available software packages: Microsoft Visual Basic, Microsoft
Access, ESRI MapObjects, and Amzi Prolog.
Eldrandaly (2006) developed a COM-based expert system to assist the GIS analysts in
selecting suitable map projection for their application in ArcGIS software package. Visual
Rule Studio® (an object-oriented COM-compliant expert system development environment
for windows) was used to develop the expert system. The COM technology was used for
integrating the expert system with ArcGIS ® 9.0, a COM-complaint GIS software package. Its
built in macro language, Visual Basic for Application (VBA), was used to develop the Map
Projection application that implements the expert system using Automation Technology.
4.5. Ontology: A Promising Interoperability Approach
Ontologies are expected in various areas as promising tools to improve communication
among people and to achieve interoperability among systems (Lee et al. 2006). Ontology for a
philosopher is the science of beings, of what is, i.e., a particular system of categories that
reflects a specific view of the world. For the Artificial Intelligence (AI) community, Ontology
is an engineering artifact that describes a certain reality with a certain vocabulary, using a set
of assumptions regarding the intended meaning of the vocabulary words (Fonseca et al.
2oo2). Ontology defines the terms and relationships among terms that represent an area of
knowledge. In software engineering, computer-readable Ontologies are growing in
importance for defining basic concepts within a domain. If multiple-domain applications are
developed utilizing a shared Ontology, or if their distinct Ontologies can be related, then the
applications can have a common understanding of data, and semantic interoperability is
enhanced. In addition, Ontologies can be developed that relate information across domains,
opening up new possibilities for interoperability (Carney et al. 2005). The importance of
Ontologies in GIScience has been established over the past decade as scholars have
demonstrated their value in multiple geospatial and reasoning contexts (Schuurman and
Leszczynski 2006). The use of Ontology, translated into an active information system
component, leads to Ontology-Driven Information Systems and, in specific case of GIS, leads
to what is called Ontology-Driven Geographic Information Systems- ODGIS (Fonseca et.
GIS and Spatial Decision Making
19
al.2002). ODGIS are built using software components derived from various Ontologies.
These software components are classes that can be used to develop new applications. Being
Ontology-derived, these classes embed knowledge extracted from Ontologies (Fonseca et al.
2002). Ontologies aim at modeling and structuring domain knowledge and an Ontology
development follows a cycle containing several phases, ranging from the requirements
analysis and initial Ontology design to conceptual refinement, evaluation and evolution as
shown in figure 5 (Linkova et. al. 2005). Software tools are available to accomplish most
aspects of Ontology development. Today's most Ontology languages are based on the XML
syntax such as OWL (Web Ontology Language). OWL (Linkova et. al. 2005) is a product of
W3C (the World Wide Web Consortium) and is presented as an Ontology language for the
semantic web.
It allows representing not only concepts, taxonomies, binary relations, but also
cardinalities, richer type definitions and other characteristics. Most of the Ontology languages
are supported by tools such as the widely used Protégé system which provides OWL support.
Protégé is a free, open-source platform that provides a growing user community with a suite
of tools to construct domain models and knowledge-based applications with Ontologies. At
its core, Protégé implements a rich set of knowledge-modeling structures and actions that
support the creation, visualization, and manipulation of Ontologies in various representation
formats. Protégé can be customized to provide domain-friendly support for creating
knowledge models and entering data.
Further, Protégé can be extended by way of a plug-in architecture and a Java-based
Application Programming Interface (API) for building knowledge-based tools and
applications. The Protégé platform supports two main ways of modeling Ontologies: The
Protégé-Frames editor that enables users to build and populate Ontologies that are frame-
based, in accordance with the Open Knowledge Base Connectivity protocol (OKBC) and The
Protégé-OWL editor that enables users to build Ontologies for the Semantic Web(Protégé
2006). The following paragraphs describe two of the systems developed using Ontology.
Moore et al. (2001) established an ontological basis for geography and environmental
science (feeding into coastal zone management), and GeoComputation from a holistic
viewpoint. This Ontology serves as the foundation for the development of COAMES
(COAstal Management Expert System), which uses the Dempster-Shafer theory of evidence
to model holism. COAMES is an object-oriented expert system, consisting of a user interface,
a database, an object-oriented knowledge base (incorporating both the expert’s factual
knowledge and the process knowledge embodied in models) and most importantly an
inference engine. Within the inference engine are algorithms to calculate belief with
uncertainty through the Dempster-Shafer Theory of Evidence. COAMES achieves
technological holism, as it brings together expert systems and GIS, as well as remotely sensed
data and GPS measurements. Niaraki and Kim (2009) developed a generic ontology-based
architecture using a multi-criteria decision making technique to design a personalized route
planning system.
The objective of their research is to determine an impedance model of road GIS and
Intelligent Transportation Systems (ITS) for a personalized ontology-based route planning
system using a multiple criteria decision making method. The impedance model aims to
distinguish the appropriate user-centric criteria and combine them in order to obtain the
impedance function to be employed in a route finding algorithm.
Khalid Eldrandaly
20
Requirements Analysis
Initial Design
Refinement
Evaluation
Ontology
Evolution
Figure 5. Ontology Lifecycle (adapted from Linkova et. al. 2005).
CONCLUSION
In this study, we have attempted to highlight the complexity of spatial decision making,
system integration problems, and interoperability and to provide an overview of the different
strategies for integrating GIS, MCDM, and Expert Systems. From the above discussion, it is
clear that spatial decision making is a highly complex process and most spatial decision
problems are complex and ill structured. GIS, MCDM, and ES are required for solving these
problems but each of them has its own limitations and drawbacks in dealing with spatial
decision making. The integration of these tools may avoid some of the limitations and
difficulties existing in each of them and provide the decision maker with an efficient tool for
solving these problems. The degree of interoperability between these tools and a GIS will
affect the ability of an integrated system to model the complexity of the real world. Numerous
mechanisms enabling interoperability between GIS and these tools have appeared over the
years. Examples range from simple solutions such as, loose coupling techniques to much
more sophisticated approaches, such as Component Object Model (COM) technology and
Ontology. Although the simple techniques (loose and tight coupling) have achieved
considerable success in integrating GIS and these tools and they are still used now, these
techniques have many drawbacks and limitations. These drawbacks can be eliminated or at
least reduced by applying the recent approaches of software interoperability such to be COM
technology and Ontology. Ontologies are expected as promising tools to achieve and open up
new possibilities for software interoperability.
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... Content, data, and services may all be found in one place. It can be altered to suit the user's preferences a position with the company [2]. Nowadays automated scheduling systems have been widely used by different institutions. ...
... There are cases that a panel/adviser and the secretary were scheduled in two thesis groups that are presenting at the same time; And there are cases where thesis groups are scheduled twice. (2) The manual process of generating overall ratings takes up a lot of time since panels do not submit at the same coming from individual repositories. ...
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