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International Journal of Geographical
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GIS‐based multicriteria decision
analysis: a survey of the literature
Jacek Malczewski
a
a
Department of Geography , University of Western Ontario ,
London , Ontario N6A 5C2 , Canada
Published online: 20 Feb 2007.
To cite this article: Jacek Malczewski (2006) GIS‐based multicriteria decision analysis: a survey
of the literature, International Journal of Geographical Information Science, 20:7, 703-726, DOI:
10.1080/13658810600661508
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Review Article
GIS-based multicriteria decision analysis: a survey of the literature
JACEK MALCZEWSKI*
Department of Geography, University of Western Ontario, London, Ontario N6A 5C2,
Canada
(Received 2 September 2005; in final form 14 February 2006 )
The integration of GIS and multicriteria decision analysis has attracted
significant interest over the last 15 years or so. This paper surveys the GISbased
multicriteria decision analysis (GIS-MCDA) approaches using a literature
review and classification of articles from 1990 to 2004. An electronic search
indicated that over 300 articles appeared in refereed journals. The paper provides
taxonomy of those articles and identifies trends and developments in GIS-
MCDA.
Keywords: GIS; Multicriteria decision analysis
1. Introduction
Spatial decision problems typically involve a large set of feasible alternatives and
multiple, conflicting and incommensurate evaluation criteria. The alternatives are
often evaluated by a number of individuals (decision-makers, managers, stakeholders,
interest groups). The individuals are typically characterized by unique
preferences with respect to the relative importance of criteria on the basis of which
the alternatives are evaluated. Accordingly, many spatial decision problems give rise
to the GIS-based multicriteria decision analysis (GIS-MCDA). These two distinctive
areas of research, GIS and MCDA, can benefit from each other (Laaribi et al. 1996,
Malczewski 1999, Thill 1999, Chakhar and Martel 2003). On the one hand, GIS
techniques and procedures have an important role to play in analyzing decision
problems. Indeed, GIS is often recognized ‘as a decision support system involving
the integration of spatially referenced data in a problem solving environment’
(Cowen 1988). On the other hand, MCDA provides a rich collection of techniques
and procedures for structuring decision problems, and designing, evaluating and
prioritizing alternative decisions. At the most rudimentary level, GIS-MCDA can be
thought of as a process that transforms and combines geographical data and value
judgments (the decision-maker’s preferences) to obtain information for decision
making. It is in the context of the synergetic capabilities of GIS and MCDA that one
can see the benefit for advancing theoretical and applied research on GIS-MCDA.
There is now a well-established body of literature on GIS-MCDA (e.g. Diamond
and Wright 1988, Janssen and Rietveld 1990, Carver 1991, Church et al. 1992, Banai
1993, Pereira and Duckstein 1993, Eastman et al. 1995, Heywood et al. 1995,
Jankowski 1995, Laaribi et al. 1996, Malczewski 1999, Thill 1999, Laaribi 2000,
Chakhar and Martel 2003, Feick and Hall 2004). The main aim of the paper is to
*Corresponding author. Email: jmalczew@uwo.ca
International Journal of Geographical Information Science
Vol. 20, No. 7, August 2006, 703–726
International Journal of Geographical Information Science
ISSN 1365-8816 print/ISSN 1362-3087 online # 2006 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/13658810600661508
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survey and classify the GIS-MCDA articles published between 1990 and 2004. The
remainder of this paper is organized into four sections. The methods used for
surveying the GIS-MCDA literature are discussed in the next section. Section 3
provides a general description of the GIS-MCDA database. The articles are
classified according to the various perspectives on GIS-MCDA in section 4. Finally,
section 5 presents concluding remarks.
2. Literature survey methods
The search for relevant publications was performed using several Web-based
scientific search engines, electronic libraries, and databases (see Appendix 1). The
search was limited to articles published in refereed journals in the period between 1
January 1990 and 31 December 2004. It was done using a Boolean search containing
the following terms: GIS and multicriteria (or multiobjective or multiattribute).
Initially, any article containing the search terms was considered as a potential
candidate for including into the database of the GIS-MCDA publications.
To supplement the automated search, a manual search was also done. The manual
procedure involved searching the reference sections of the papers identified by the
automated search. Any relevant references within those papers were followed up on.
Each article was reviewed by the author. Inclusion criteria for the review were any
theoretical or applied work concerning an integration of the two methodologies or
any presentation of a computer-based system integrating the GIS and MCDA
methods. Papers identified in the search, but that were clearly irrelevant, were
omitted from further consideration, leaving 319 items that were reviewed
thoroughly. A complete list of the papers is available at http://publish.uwo.ca/
,jmalczew/gis-mcda.htm.
3. The GIS-MCDM literature in 1990–2004
Figure 1 shows the development of GIS-MCDA in terms of the number of refereed
articles published in 1990–2004 and the accumulation of those articles. The
development of GIS-MCDA has been modest in the first half of the 1990s. Of the
319 articles, there were only 26 papers (or 8.2% of the total) published between 1990
and 1995. While Carver (1991) and Langevin et al. (1991) are the earliest papers
included in this survey, one should acknowledge that there had been GIS-MCDA
studies reported prior to 1991 in referred journals (e.g. Diamond and Wright 1988),
book chapters (e.g. Janssen and Rietveld 1990), and conference proceedings (e.g.
Moreno and Seigel 1988). It is interesting to note that while Carver (1991) is one of
the most widely cited GIS papers (see Fisher 2001), the work by Langevin et al.
(1991) remains largely unknown to the GIS community. The efforts to integrate GIS
and MCDA in the late 1980s and early 1990s can be associated with the
proliferation stage of the GIS development (see Waters 1998, Malczewski 2004). The
proliferation phase was characterized by the development of the user-oriented GIS
technology, which has stimulated a wide range of GIS applications including the
GIS-based approaches for tackling spatial decision problems.
From 1995 to 2000 there has been a substantial acceleration in the number of the
GIS-MCDA articles published in refereed journals with the total number rising
from 9 in 1995 to 40 in 2000. Over the last five years the volume of refereed
publications on GIS-MCDA has continued to grow very rapidly. Of the 319 articles,
almost 70% were published in the last five years. The rapid increase of the volume of
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the GIS-MCDA research can be attributed to a number of factors. First, a wider
recognition of decision analysis and support as an essential element of GIScience
(see the National Center for Geographic Information and Analysis (NCGIA)
initiatives on ‘Spatial Decision Support Systems (SDSS)’, ‘Collaborative Spatial
Decision Making’, ‘GIS and Society’, and the Varenius project on ‘Empowerment,
Marginalisation and Public Participation GIS’ at http://www.ncgia.ucsb.edu/
ncgia.html). Second, the availability of low-cost and easy-to-use MCDA software
such as Expert Choice (Expert Choice Inc.1993) and mathematical programming
solvers (e.g. LINDO Systems Inc. 1994). Third, the availability of MCDA modules
in such systems as IDRISI (Eastman et al. 1993) and SPANS (TYDAC Research
Inc. 1996), and TNT-GIS (MicroImages Inc. 2001). In particular, the availability of
a fully fledged decision support module in IDRISI has been instrumental for
stimulating applied research in GIS-MCDA (e.g. Pereira and Duckstein 1993,
Aguilar-Manjarrez and Ross 1995, Malczewski 1996, Brookes 1997, Tkach and
Simonovic 1997, Giupponi et al. 1999, Jiang and Eastman 2000, Kyem 2001, 2004).
The diffusion of the GIS-MCDA research is indicated by the large number and
diversity of refereed journals serving as outlets for the GIS-MCDM articles. Over
the years, the articles have appeared in 135 different journals. The list of refereed
journals known to have published GIS-MCDA articles gives credence to GISMCDA’s
vitality and acceptance (see table 1). It also shows that GIS-MCDA is a
significant and relevant approach for a wide variety of fields (see section 4.4). The
International Journal of Geographic Information Systems/Science leads with 21
publications (6.6%), followed by Landscape and Urban Planning with 14 publications
(4.4%). Environment and Planning A/B comes up third with 12 (3.8%). Thus, the top
three among 135 journals have published almost 15% of the GIS-MCDA articles.
The top six journals account for approximately 25% of all papers published, while
Figure 1. Total number of GIS-MCDM articles per year for the period 1990–2004.
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the top 20 account for almost 50%. Alternately stated, approximately 5% of the
journals have published more than a quarter of the papers, while about 15% of the
journals account for almost half of all the GIS-MCDA articles.
4. Classifications of the GIS-MCDA articles
A number of approaches to structuring GIS-MCDA have been suggested in the
literature (Jankowski 1995, Malczewski 1999, Herwijnen and Rietveld 1999, Laaribi
2000, Chakhar and Martel 2003). Despite differences between the GIS-MCDA
frameworks, one can identify five generic components of the GIS-based MCDA
procedures: (i) a goal or a set of goals an individual (or a group of individuals)
attempts to achieve along with associated evaluation criteria (objectives and/or
attributes) on the basis of which the decision-maker evaluates alternative courses of
action; (ii) the decision-maker or a group of decision-makers involved in the
decision-making process along with their preferences; (iii) the set of decision
alternatives (or the decision variables); (iv) the set of uncontrollable variables or
states of nature (decision environment); and (v) the set of outcomes or consequences
associated with each alternative-criterion pair.
Given the generic components of GIS-MCDA, two classification schemes for the
GIS-MCDA literature were developed. First, all articles were classified based on the
geo-information (GIS) components of the GIS-MCDA methods. This classification
involved the following considerations: (i) the geographical data models, (ii) the
spatial dimension of the evaluation criteria, and (iii) the spatial definition of decision
alternatives. Second, the articles were classified according to the generic elements of
the MCDA methods. This taxonomy was based on (i) the nature of evaluation
criteria, (ii) the number of individuals involved in the decision-making process, and
Table 1. The list of refereed journals that have published four or more articles on
GIS-MCDA in 1990–2004.
Rank Journal # of articles %
1 International Journal of Geographical Information Systems/Science 21 6.6
2 Landscape and Urban Planning 14 4.4
3 Environment and Planning A/B 12 3.8
4 Journal of Geographic Information and Decision Analysis 11 3.4
5–6 Computers, Environment and Urban Systems 10 3.1
5–6 Journal of Environmental Management 10 3.1
7–8 Environmental Modelling and Software 7 2.2
7–8 Environmental Management 7 2.2
9 Transportation Research Record 6 1.9
10–16 Agricultural Systems 5 1.6
10–16 Annals of the Association of American Geographers 5 1.6
10–16 Ecological Modelling 5 1.6
10–16 Forest Ecology and Management 5 1.6
10–16 Journal of Environmental Engineering 5 1.6
10–16 Photogrammetric Engineering and Remote Sensing 5 1.6
10–16 Transportation Research: Part A/B/C/D 5 1.6
17–20 Conservation Biology 4 1.3
17–20 Decision Support Systems 4 1.3
17–20 European Journal of Operational Research 4 1.3
17–20 Transactions in GIS 4 1.3
Others 170 53.3
Total 319 100
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(iii) the nature of uncertainties. In addition, the articles were classified according to
the extent and direction of the GIS and MCDA integration, and the type of
application domain and decision problem.
4.1 The GIS components
The taxonomy of the geo-information components of the GIS-MCDA approaches
is based on the following dichotomies: (i) the raster versus vector data models, (ii)
explicitly spatial criteria versus implicitly spatial criteria, and (iii) explicitly spatial
alternatives versus implicitly spatial alternatives.
4.1.1 The raster- and vector-based GIS-MCDA. Of the 319 papers, 152 (47.6%)
articles reported the raster-data-based research (e.g. Pereira and Duckstein 1993,
Eastman et al. 1995, Malczewski 1996, Cromley and Hanink 1999, Aerts et al. 2003,
Church et al. 2003) and 150 (47.0%) articles discussed research involving the vectorbased
GIS-MCDA (e.g. Can 1992, Jankowski 1995, Laaribi et al. 1996, Rinner and
Malczewski 2002, Feick and Hall 2004). There were 17 articles which did not
provide any information on the geographical data model. It is important to note
that some of the works reported in the GIS-MCDA articles have been based on a
combination of the raster and vector data models. It was, however, the geographical
data structure used in the multicriteria combination rules (see section 4.2.1) that
provided the bases for classifying the articles according to the geographical data
model. Thus, if the combination rules were performed using the raster data, then the
study was categorized as the raster-based MCDA. Similarly, all papers reporting on
the vector-based multicriteria combination rules were categorized as the vectorbased
multicriteria studies, irrespective of the format of the input data. Although the
majority of the GIS-MCDA research has been based on the layer view of the realworld
represented by the raster or vector data models, an effort has also been made
to use the object-oriented paradigm for integrating GIS and MCDA (e.g. Reitsma
and Carron 1997, Matthews et al. 1999).
4.1.2 Explicitly and implicitly spatial criteria. The raster- and vector-based GISMCDA
approaches are further subdivided into two categories depending on the
nature of criteria. Criterion is a standard of judgment or rule on the basis of which
alternative decisions can be evaluated and ordered according to their desirability
(see section 4.2.1). Explicitly spatial criteria are present in the decision problems that
involve spatial characteristics as criteria. For example, in the context of a site search
problem such site characteristics as size, shape, contiguity, and compactness are
explicitly spatial criteria (Brookes 1997, Church et al. 2003). Many decision
problems involve criteria (objectives) which are implicitly spatial (Herwijnen and
Rietveld 1999). A criterion is said to be implicitly spatial if spatial data are needed to
compute the level of achievement of the criterion. Such criteria (objectives) as the
gross marginal return of agricultural production, equity of income distribution, the
public investment in the conservation reserve program, the costs of solid waste
disposing, etc., can involve spatial attributes such as distance, proximity,
accessibility, elevation, slope, etc. (e.g. MacDonald 1996, Antoine et al. 1997).
It should be noted that these two categories are not mutually exclusive (see
table 2). Indeed, the majority of the studies (almost 70%) involved both explicitly
and implicitly spatial criteria (e.g. Kao and Lin 1996, Antoine et al. 1997, Lin et al.
1997, Seppelt and Voinov 2002, Martin et al. 2003, Wu et al. 2004). Of the 152
raster-based GIS-MCDA articles, 12 (7.9%) and 45 (29.6%) articles have reported
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research that involved explicitly and implicitly spatial criteria, respectively.
Examples of the former category include: Eastman et al. (1995), Brookes (1997),
Cromley and Hanink (1999, 2003), Seppelt and Voinov (2002), and Church et al.
(2003). The articles by Nevo and Garcia (1996), Brakewood and Grasso (2000),
Fuller et al. (2003), Perez et al. (2003) and Store and Jokima¨ki (2003) provide
examples of the raster-based implicitly spatial criteria. Similar classification for the
vector-based GIS-MCDA showed that there were 20 (13.3%) articles reporting the
use of explicitly spatial criteria (e.g. MacDonald 1996, Weigel and Cao 1999) and 12
(8.0%) studies involved implicitly spatial criteria (e.g. Vertinsky et al. 1994, Ka¨chele
and Dabbert 2002).
4.1.3 Explicitly and implicitly spatial alternatives. Decision alternatives can be
defined as alternative courses of action among which the decision-maker must
choose. A spatial decision alternative consists of at least two elements: action (what
to do?) and location (where to do it?). The spatial component of a decision
alternative can be specified explicitly or implicitly. Examples of explicitly spatial
alternatives include: alternative sites for locating facilities (e.g. Aguilar-Manjarrez
and Ross 1995, Kao 1996, Basnet et al. 2001), alternative location-allocation
patterns (e.g. Armstrong et al. 1992, Cova and Church 2000, Malczewski et al.
1997), alternative patterns of land use-suitability (e.g. Eastman et al. 1995, Antoine
et al. 1997, Brookes 1997, Bennett et al. 1999). In many decision situations the
spatial component of an alternative decision is not present explicitly. However, there
may be a spatial implication associated with implementing an alternative decision.
In such a case, the alternative is referred to as an implicitly spatial alternative
(Herwijnen and Rietveld 1999). Spatially distributed impacts can emerge, for
example, through the implementation of a particular solution to minimize flood
risks in which favorable impacts are produced at one location while negative
consequences result at another (Vertinsky et al. 1994, Tkach and Simonovic 1997,
Jumppanen et al. 2003).
Table 3 shows that the articles reporting on the use of explicitly spatial
alternatives accounted for 57 or 37.5 % of all the raster-based GIS-MCDA papers
(e.g. Ross et al. 1993, Kao 1996, Church et al. 2003). The implicitly spatial
alternatives were used in 41 (or 27.0 %) articles on the raster-based GIS-MCDA (e.g.
Jumppanen et al. 2003, Burton and Rosenbaum 2003, Wu et al. 2004). The GISMCDA
database contained 58 articles (38.7%) which have been categorized as the
vector-based GIS-MCDA and explicitly spatial-alternative category (e.g.
MacDonald 1996, Weigel and Cao 1999, Armstrong et al. 2003). There were 49
(32.7%) articles in the vector-based-implicitly spatial-alternative category (e.g.
Vertinsky et al. 1994, Ka¨chele and Dabbert 2002, Morari et al. 2004).
Table 2. Classification of the GIS-MCDA articles according to the GIS data model and
evaluation criterion.
Criteria
Total
Explicitly
spatial
Implicitly
spatial
Explicit/implicitly
spatial
Data model
Raster 12 45 95 152
Vector 20 12 118 150
Unspecified 2 7 8 17
Total 34 64 221 319
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4.2 The multicriteria decision analysis components
Given the generic elements of GIS-MCDA, one can distinguish three dichotomies:
(i) multiobjective decision analysis (MODA) versus multiattribute decision analysis
(MADA), (ii) individual versus group decision-making, and (iii) decisions under
certainty versus decision under uncertainty (that is, the probabilistic and fuzzy
decision-making). This classification schema is shown in figure 2.
4.2.1 Multiattribute and multiobjective decision analysis (MADA and
MODA). Criterion is a generic term including both the concept of attribute and
objective. Thus, MCDA is used as the blanket term which includes both
multiobjective and multiattribute decision-making. The multiattribute decision
problems are assumed to have a predetermined, limited number of alternatives.
Solving this type of problem is a selection process as opposed to a design process.
The multiobjective problem is continuous in the sense that the best solution may be
Table 3. Classification of the GIS-MCDA articles according to the GIS data model and
decision alternative.
Decision alternatives
Total
Explicitly
spatial
Implicitly
spatial
Explicit/implicitly
spatial
Data model
Raster 57 41 54 152
Vector 58 49 43 150
Unspecified 8 4 5 17
Total 123 94 102 319
Figure 2. Classification scheme for the GIS-MCDA articles (Note: the number of articles is
given in brackets; *22 articles presented both GIS-MADA and GIS-MODA; these articles
were included into the two categories).
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found anywhere within the region of feasible solutions. Therefore, multiattribute
and multiobjective problems are sometimes referred to as discrete and continuous
decision problems, respectively (Hwang and Yoon 1981, Goicoechea et al. 1982).
Figure 2 shows 341 GIS-MCDA articles (approaches) at the top of the
hierarchical classification. This number is greater then the number of surveyed
articles, because there were 22 studies which combined the GIS-MADA and GISMODA
methods (e.g. Guimara˜es Pereira et al. 1994, Vertinsky et al. 1994, Kao and
Lin 1996, Bojo´rquez-Tapia et al. 2001, Makropoulos et al. 2003). These articles were
included in both the GIS-MADA and GIS-MODA categories. The results of the
survey indicate that a majority of the articles fall into the GIS-MADA category
(figure 2). The GIS-MADA approaches account for almost 70% of the total (e.g.
Banai 1993, Pereira and Duckstein 1993, Jankowski 1995, Eastman et al. 1995,
Malczewski 1996, Feick and Hall 1999, Jun 2000, Kyem 2004). More than 30% of
the approaches fall into the GIS-MODA category (e.g. Xiang 1993, Antoine et al.
1997, Gomes and Lins 2002, Seppelt and Voinov 2002, Aerts and Heuvelink 2002,
Aerts et al. 2003, Xiao et al. 2002, Armstrong et al. 2003, Ka¨chele and Dabbert
2002, Stewart et al. 2004).
In addition to the classification of the articles into the GIS-MADA and GISMODA
categories, it is useful to identify the algorithms or combination (decision)
rules within each of the two groups. A decision rule is a procedure that allows for
selecting one or more alternatives from a set of alternatives available to the decisionmaker
(see Malczewski 1999). Although a considerable number of decision rules
have been proposed in the MCDA literature (e.g. Cohon 1978, Hwang and Yoon
1981, Goicoechea et al. 1982), the use of the combination rules in the GIS-MCDA
studies has been limited to a few well-known approaches such as the weighted
summation, ideal/reference point, and outranking methods (see table 4). The
weighted summation and related procedures have been by far the most popular
GIS-MCDA approaches. These methods were present in 143 articles or 39.3% of the
total (e.g. Eastman et al. 1995, Gumbricht et al. 1996, Pettit and Pullar 1999,
Robinson et al. 2002, Ceballos-Silva and Lo´pez-Blanco 2003, Perez et al. 2003,
Ayalew et al. 2004, Marinoni 2004). The weighted summation has typically been
Table 4. Classification of the GIS-MCDA articles according to the combination rule.
Combination rules # of articles*
%
MADA
Weighted summation/Boolean overlay 143 39.3
Ideal/reference point (TOPSIS, MOLA) 35 9.6
Analytical Hierarchy Process (AHP) 34 9.4
Outranking methods (ELECTRE, PROMETHEE) 17 4.7
Other 30 8.3
Total (GIS-MADA) 259 71.3
MODA
Multi-objectives programming algorithms
(linear-integer programming)
57 15.7
Heuristic search/evolutionary/genetic algorithms 29 8.0
Goal programming/reference point algorithms 9 2.5
Other 9 2.5
Total (GIS-MODA) 104 28.7
Total 363 100.0
Note: *
some articles presented more than one combination rule.
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used in conjunction with the Boolean operations (e.g. Eastman et al. 1995, Pettit and
Pullar 1999, Perez et al. 2003). Many studies have used the weighted summation
approach along with the linear transformation method for normalizing criteria and
the pairwise comparison method for deriving the criterion weights (e.g. Eastman
et al. 1995, Gumbricht 1996, Tseng et al. 2001, Basnet et al. 2001, Vlachopoulou
et al. 2001, Wu et al. 2004, Marinoni 2004). The ordered weighted averaging (OWA)
provides an extension and generalization of the Boolean operations and the
weighted summation procedures. The OWA approach has been presented in Jiang
and Eastman (2000), Rinner and Malczewski (2002), Makropoulos et al. (2003),
Malczewski et al. (2003), and Rashed and Weeks (2003).
The Analytical Hierarchy Process (AHP) is another popular method which is
based on the additive weighting model (e.g. Banai 1993, Wu 1998, Basnet et al. 2001,
Zhu and Dale 2001). The AHP method has been used in two distinctive ways within
the GIS environment. First, it can be employed to derive the weights associated with
attribute map layers. Then, the weights can be combined with the attribute map
layers in a way similar to the weighted additive combination methods. This
approach is of particular importance for problems involving a large number of
alternatives, when it is impossible to perform a pairwise comparison of the
alternatives (Eastman et al. 1993). Second, the AHP principle can be used to
aggregate the priority for all level of the hierarchy structure including the level
representing alternatives. In this case, a relatively small number of alternatives can
be evaluated (Jankowski and Richard 1994).
The primary reason for the popularity of the weighted summation and related
methods is that the approaches are very easy to implement within the GIS
environment using map algebra operations and cartographic modeling. The
methods are also easy-to-understand and intuitively appealing to decision-makers.
However, GIS implementations of the weighted summation procedures are often
used without full understanding of the assumptions underlying this approach. In
addition, the method is often applied without full insight into the meanings of two
critical elements of the weighted summation model: the weights assigned to attribute
maps and the procedures for deriving commensurate attribute maps. Hobbs (1980),
Lai and Hopkins (1989), Heywood et al. (1995) and Malczewski (2000) provide
discussions on some aspects of the incorrect use of the method.
Some of the difficulties associated with the weighted summation and AHP models
can be avoided by using such MADA procedures as the ideal/reference point
methods (Pereira and Duckstein 1993) and the outranking methods (Joerin et al.
2001). According to the survey (see table 4), the ideal/reference point method is the
second most often-used GIS-MADA combination rule (e.g. Carver 1991, Pereira
and Duckstein 1993, Malczewski et al. 1997, Tkach and Simonovic 1997, Lee et al.
2000, Jankowski et al. 2001). Over the years a considerable number of articles have
presented the use of the outranking methods such as ELECTRE and related
procedures (e.g. Carver 1991, Can 1992, Joerin 1995, Villa et al. 1996, Joerin and
Musy 2000, Joerin et al. 2001, Schlaepfer et al. 2002, Morari et al. 2004) and
PROMETHEE (e.g. Laaribi et al. 1996, Martin et al. 2003).
The GIS-MODA articles can be grouped into three main categories depending on
the multiobjective algorithms: (i) multiobjective linear-integer programming, (ii)
goal programming/reference point algorithms, and (iii) heuristic search/evolutionary/genetic
algorithms (table 4). In general, the multiobjective models are often
tackled by converting them to single objective problems and then by solving the
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problem using the standard linear-integer programming methods (e.g. Cohon 1978,
Goicoechea et al. 1982). This approach is the most often used in the GIS-MODA
research (e.g. Bowerman et al. 1995, Kao 1996, Cromley and Hanink 1999, Ka¨chele
and Dabbert 2002, Church et al. 2003). It accounts for 15.7% of all methods
reported in the GIS-MCDA articles (table 4). Another group of GIS-MODA
includes articles in which the goal programming/reference point algorithms are used
(e.g. Antoine et al. 1997, Chang et al. 1997, Grabaum and Meyer 1998, Agrell et al.
2004, Stewart et al. 2004). These traditional approaches to MODA have some
limitations (Malczewski and Ogryczak 1996, Zhou and Civco 1996, Xiao et al. 2002,
Aerts et al. 2003). First, they can be applied in situations when it is possible to
formulate decision problems in terms of mathematical programming models.
Second, the methods are of limited applicability for very large and complex
problems. Third, the methods may fail to find important solutions. Given these
limitations, a number of heuristics approaches have been proposed. Notably, the
evolutionary/genetic algorithms have recently been applied for solving complex
spatial problems (e.g. Guimara˜es Pereira et al. 1994; Zhou and Civco 1996, Balling
et al. 1999, Bennett et al. 1999, Feng and Lin 1999, Matthews et al. 1999, Seppelt
and Voinov 2002, Xiao et al. 2002, Armstrong et al. 2003).
4.2.2 Individual and group decision-making. The GIS-MADA and GIS-MODA
approaches can be further subdivided into two categories depending on the goalpreference
structure of the decision-maker (see figure 2). If there is a single goalpreference
structure, then the problem is referred to as a single decision-maker’s
problem, regardless of the number of individuals actually involved. On the other
hand, if individuals (interest groups) are characterized by different goal-preference
structures, then the problem becomes that of group decision-making. The group
decision-making category includes the participatory decision making approaches
(e.g. Jankowski and Nyerges 2001).
A majority of the GIS-MCDA articles represented the individual decisionmaker’s
approaches (see figure 2 and table 5). These approaches were found in
63.8% of the GIS-MADA articles (e.g. Carver 1991, Banai 1993, Pereira and
Duckstein 1993, Eastman et al. 1995, Jun 2000) and 36.2% of the GIS-MODA
papers (e.g. Church et al. 1992, Xiang 1993, Kao 1996, Antoine et al. 1997, Ka¨chele
and Dabbert 2002, Aerts et al. 2003). The group/participatory approaches were
presented in 70 articles (see figure 2 and table 6). They were found in 63 (90%)
articles on GIS-MADA (e.g. Malczewski 1996, Feick and Hall 1999, 2002,
Jankowski et al. 1997, 2001, Qureshi and Harrison 2001, Kyem 2004). There were
only seven papers in the GIS-MODA-group decision-making category (e.g. Bennett
et al. 1999, Seppelt and Voinov 2002, Bayliss et al. 2003).
Table 5. Classification of GIS-MCDA papers according to the type of multicriteria decision
method for individual decision maker.
Types of multicriteria analysis
Types of uncertainty
TotalDeterministic Probabilistic Fuzzy
Multiattribute 119 (43.9) 17 (6.3) 37 (13.7) 173 (63.8)
Multiobjective 89 (32.8) 5 (1.8) 4 (1.5) 98 (36.2)
Total 208 (76.8) 22 (8.1) 41 (15.1) 271 (100.0)
Note: percentages of the total are given in brackets.
712 J. Malczewski
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4.2.3 Decisions under condition of certainty and uncertainty. Broadly speaking,
decision problems can be categorized into decisions under certainty and decisions
under uncertainty depending on the amount of information (knowledge) about the
decision situation that is available to the decision-maker/analyst. If the decision-maker
has perfect knowledge of the decision environment, then the decision is made under
conditions of certainty (deterministic decision-making). Many real-world decisions
involve some aspects that are unknowable or very difficult to predict. This type of
decision-making is referred to as decisions under conditions of uncertainty. It should
be recognized, however, that uncertainty may come from various sources. To this end,
there are two basic types of uncertainty that may be present in a decision situation: (i)
uncertainty associated with limited information about the decision situation, and (ii)
uncertainty associated with fuzziness (imprecision) concerning the description of the
semantic meaning of the events, phenomena or statements themselves. Consequently
both multiattribute and multiobjective problems under uncertainty can be further
subdivided into: probabilistic (stochastic) and fuzzy decision-making problems
depending on the type of uncertainty involved (see figure 2).
Many analysts deliberately choose to model spatial decisions as occurring under a
condition of certainty because of insufficient data or because the uncertainty is so
remote that it can be disregarded as a factor (see Hwang and Yoon 1981,
Malczewski 1999). Consequently, the majority of the GIS-MCDA articles fall into
the deterministic category. The deterministic approaches were presented in 263
papers or approximately 77% of the total (e.g. Carver 1991, Church et al. 1992,
Jankowski and Richard 1994, Malczewski 1996, Brookes 1997, Aerts et al. 2003,
Marinoni 2004). A vast majority of these articles belonged to the individual
decision-making category (see figure 2 and tables 5 and 6). The articles presenting
the research on the GIS-MCDA under condition of uncertainty accounted for
almost 23% of the total (e.g. Banai 1993, Klungboonkrong and Taylor 1998, Jiang
and Eastman 2000, Noss et al. 2002, Seppelt and Voinov 2002, Rashed and Weeks
2003, Wang et al. 2004). Of the 78 articles on decision problems under condition of
uncertainty, 35.9% fall into the probabilistic decision analysis category (e.g.
Klungboonkrong and Taylor 1998, Noss et al. 2002, Seppelt and Voinov 2002,
Wang et al. 2004) and 64.1% of the articles were found to represent the fuzzy
decision-making (e.g. Banai 1993, Martin et al. 2003, Jiang and Eastman 2000,
Joerin et al. 2001, Makropoulos et al. 2003, Rashed and Weeks 2003).
4.3 Spatial Decision Support Systems (SDSS): integrating GIS and MCDA
The surveyed articles have been classified according to (i) the extent of integration,
and (ii) the direction of integration of GIS and MCDA. Four categories have been
Table 6. Classification of GIS-MCDA papers according to the types of multicriteria decision
methods for group decision-making.
Types of multicriteria analysis
Types of uncertainty
TotalDeterministic Probabilistic Fuzzy
Multiattribute 50 (71.4) 5 (7.2) 8 (11.4) 63 (90.0)
Multiobjective 5 (7.2) 1 (1.4) 1 (1.4) 7 (10.0)
Total 55 (78.6) 6 (8.6) 9 (12.8) 70 (100.0)
Note: percentages of the total are given in brackets.
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identified based on the extent of integration: (i) no integration, (ii) loose coupling,
(iii) tight coupling, and (iv) full integration (Goodchild et al. 1992, Nyerges 1992,
Jankowski 1995, Malczewski 1999, Jun 2000). In the loose-coupling approach, two
systems (GIS and multicriteria modeling system) exchange files such that a system
uses data from the other system as the input data. A tight-coupling strategy is based
on a single data or model manager and a common user interface. Thus, the two
systems share not only the communication files but also a common user interface. A
more complete integration can be achieved by creating user-specified routines using
generic programming languages. The routines can then be added to the existing set
of commands or routines of the GIS package. This coupling strategy is referred to as
a full integration approach.
The articles have also been classified in terms of the direction of integration. Five
categories of articles have been identified: (i) no integration, (ii) one-direction
integration with GIS as principle software, (iii) one-direction integration with
MCDA system as principle software, (iv) bi-directional integration, and (v) dynamic
integration (see (Nyerges 1992, Jun 2000). One-direction integration provides a
mechanism for importing/exporting information via a single flow that originates
either in the GIS or MCDA software. This type of integration can be based on GIS
or MCDA as the principle software. In the bi-directional integration approach the
flow of data/information can originate and end in the GIS and MCDA modules.
While bi-directional integration involves one-time flow of information, dynamic
integration allows for a flexible moving of information back and forth between the
GIS and MCDA modules according to the user’s needs (Jun 2000).
Table 7 shows a cross-classification of the articles according to the extent and
direction of GIS-MCDA integration. It indicates that a considerable number of articles
(26.0% of the total) do not provide any discussion on the integration of the two
technologies (e.g. Balling et al. 1999, Noss et al. 2002). Of the 319 articles, 106 (33.2%)
papers report the loose-coupling approach (e.g. Guimara˜es Pereira et al. 1994,
Jankowski 1995), and 95 (29.8%) articles discuss research involving the tight-coupling
approach (e.g. Bennett et al. 1999, Riedl et al. 2000). There are 35 (11.0%) articles
presenting full integrated decision support systems (e.g. Eastman et al. 1995, Matthews
et al. 1999). At the same time, almost half of the research has used GIS as the principle
Table 7. Classification of the GIS-MCDA articles according to the extent of integration and
the direction of integration of GIS and MCDA.
Extent of integration
No
integration
Loose-
coupling
Tight-
coupling
Full
integration Total
Direction
of
integration
No integration 83 (26.0) 0 (0.0) 0 (0.0) 0 (0.0) 83 (26.0)
One direction: GIS
as principle software
0 (0.0) 99 (31.0) 40 (12.5) 14 (4.4) 153 (48.0)
One direction: MCDA
as principle software
0 (0.0) 7 (2.2) 26 (8.2) 7 (2.2) 40 (12.5)
Bi-directional
integration
0 (0.0) 0 (0.0) 21 (6.6) 8 (2.5) 29 (9.1)
Dynamic
integration
0 (0.0) 0 (0.0) 8 (2.5) 6 (1.9) 14 (4.4)
Total 83 (26.0) 106 (33.2) 95 (29.8) 35 (11.0) 319 (100.0)
Note: the numbers in parentheses are the percentages of the total number of articles.
714 J. Malczewski
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software for integrating MCDA and GIS (e.g. Jun 2000, Malczewski et al. 2003). The
MCDA as the principle software strategy for integrating MCDA and GIS was used in
12.5% of the research (e.g. Antoine et al. 1997, Ka¨chele and Dabbert 2002).
4.4 Application domains and decision problems
One of the most remarkable features of the GIS-MCDA approaches is the wide
range of decision and management situations in which they have been applied over
the last 15 years or so. Table 8 shows a cross-classification of the GIS-MCDA
articles according to the type of decision (and management) problems and
application domain. Major application areas were found to be in environmental
planning/ecology and management (e.g. Pereira and Duckstein 1993, Villa et al.
1996, Bojo´rquez-Tapia et al. 2001, Qureshi and Harrison 2001, Noss et al. 2002,
Seppelt and Voinov 2002, and Church et al. 2003), transportation (e.g. Church et al.
1992, Jankowski and Richard 1994, Bowerman et al. 1995, Weigel and Cao 1999,
and Jha et al. 2001), urban and regional planning (e.g. Wu 1998, Feng and Lin 1999,
Gomes and Lins 2002, and Ward et al. 2003), waste management (Carver 1991, Kao
1996, Kao and Lin 1996, MacDonald 1996, Charnpratheep et al. 1997, and Lea˜o
et al. 2004), hydrology and water resource (Langevin et al. 1991, Reitsma and
Carron 1997, Tkach and Simonovic 1997, Giupponi et al. 1999, Lee et al. 2000,
Makropoulos et al. 2003, Martin et al. 2003), agriculture (e.g. Matthews et al. 1999,
Ka¨chele and Dabbert 2002, Ceballos-Silva and Lopez-Blanco 2003, and Morari et al.
2004), and forestry (Vertinsky et al. 1994, Kangas et al. 2000, Riedl et al. 2000,
Schlaepfer et al. 2002, Kyem 2004). These applications accounted for 72.4% of the
total. The rest of the GIS-MCDA applications were found in areas such as natural
hazard management (e.g. Rashed and Weeks 2003, Ayalew et al. 2004), recreation
and tourism management (e.g. Feick and Hall 1999, 2004), housing and real estate
(e.g. Can 1992, Johnson 2001), geology and geomorphology (e.g. de Arau´jo and
Macedo 2002, Burton and Rosenbaum (2003), industrial facility management (e.g.
Jun 2000, Vlachopoulou et al. 2001), and cartography (e.g. Huffman and Cromley
2002, Armstrong et al. 2003).
The survey showed that the GIS-MCDA approaches were most often used for
tackling land suitability problems (table 8). Almost 30% of the articles were concerned
with land suitability analysis. This type of analysis was most frequently used in such
application domains as: ecology and environment (e.g. Pereira and Duckstein 1993,
Bojo´rquez-Tapia et al. 2001), forestry (e.g. Riedl et al. 2000, Kyem 2004), agriculture
(e.g. Ceballos-Silva and Lopez-Blanco 2003), waste management (e.g. Carver 1991,
Charnpratheep et al. 1997), and natural hazard management (e.g. Rashed and Weeks
2003, Ayalew et al. 2004). Plan/scenario evaluation problems accounted for 15.4% of
the total (e.g. Kangas et al. 2000, Martin et al. 2003). The types of problems were
especially often tackled in the water resource application domain (e.g. Ka¨chele and
Dabbert 2002, Morari et al. 2004). In addition, the site search/selection problems (e.g.
Aguilar-Manjarrez and Ross 1995, Cova and Church 2000, Rinner and Malczewski
2002) and the resource allocation problems (e.g. Aerts and Heuvelink 2002, Aerts et al.
2003, Stewart et al. 2004) were found in a substantial portion of the GIS-MCDA
articles (approximately 14.5% and 11.0% of the total, respectively).
5. Conclusions
The last 15 years have evidenced remarkable progress in the quantity and quality of
research in integrating GIS and MCDA. The multidisciplinary field of GIS-MCDA
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Table 8. Classification of the GIS-MCDA articles according to the extent of integration and the application domain and decision problem.
Decision/evaluation problems
Land
suitability
Plan/scenario
evaluation
Site
search/
selection
Resources
allocation
Transportation/
vehicle routing/
scheduling
Impact
assessment
Locationallocation
Miscellaneous Total %
Application
domains
Environment/
Ecology
19 8 3 10 0 5 0 10 55 17.2
Transportation 3 2 0 0 13 2 0 9 34 10.7
Urban/Regional
planning
4 8 5 10 1 0 3 6 32 10.0
Waste management 11 2 5 0 7 0 1 0 29 9.1
Hydrology/Water
resource
4 11 4 2 0 1 0 6 28 8.8
Agriculture 8 3 4 7 0 2 0 2 27 8.5
Forestry 12 2 8 3 3 0 0 2 26 8.2
Natural hazard 9 4 0 0 1 0 0 1 15 4.7
Recreation/Tourism 3 2 6 0 0 0 0 3 14 4.4
Housing/Real estate 4 3 2 1 0 0 0 2 12 3.8
Geology/
Geomorphology
3 0 0 0 0 1 0 5 9 2.8
Manufacturing 3 0 4 0 0 0 0 0 7 2.2
Cartography 0 0 0 0 0 0 0 5 5 1.6
Miscellaneous 8 4 5 2 0 0 3 4 26 8.2
Total 91 49 46 35 25 11 7 55 319 100.0
% 28.5 15.4 14.5 11.0 7.8 3.4 2.2 17.2 100.0
716J.Malczewski
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has been widely and strongly adopted within the GIS community. Quite correctly,
the GIS community recognizes the great benefits to be gained by incorporating
MCDA into a suite of GIS capabilities. This survey suggests that GIS-MCDA has
generated a large enough literature allowing it to be considered as an essential
subfield of research in GIScience. GIS-MCDA has been vital in advancing
GIScience in two major areas: spatial decision support and participatory GIS.
The efforts to integrate MCDA into GIS has been instrumental for developing the
paradigm of spatial decision support (Goodchild 1993), in which the geographic
information technology is made available directly to decision-makers for policy or
scenario development (Eastman et al. 1993, Malczewski 1999, Jankowski and
Nyerges 2001, Ascough et al. 2002). The major advantage of incorporating MCDA
techniques into GIS-based procedures is that the decision-makers can insert value
judgments (their preferences with respect to evaluation criteria and/or alternatives)
into GIS-based decision-making procedures, and receive feedback on their
implications for policy evaluation. Such feedback can enhance the decision-maker’s
confidence in the results, consistent with general findings in the decision support
system literature on the importance of feedback (e.g. Limayem and DeSanctis 2000).
MCDA provides mechanisms for revealing decision-makers’ preferences, and for
identifying and exploring compromise alternatives. It can help users understand the
results of GIS-based decision-making procedures, including tradeoffs among policy
objectives, and use those results in a systematic, defensible way to develop policy
recommendations.
The GIS-MCDA research has made considerable contribution to the participatory
GIScience (Jankowski and Nyerges 2001). By their nature, the MCDA
approaches integrate multiple views of decision problems. They may improve
communication and understanding among multiple decision-makers and facilitate
ways of building consensus and reaching policy compromises. Consequently, the
GIS-MCDA support systems have the potential to improve collaborative decision
making process by providing a flexible problem-solving environment where those
involved in collaborative tasks can explore, understand, and redefine a decision
problem (Feick and Hall 1999, Jankowski and Nyerges 2001, Kyem 2004). An
integration of MCDA into GIS can support collaborative work by providing a tool
for structuring group decision-making problems and organizing communication in a
group setting. MCDA provides a framework for handling the debate on the
identification of components of a decision problem, organizing the elements into a
hierarchical structure, understanding the relationships between components of the
problem, and stimulating communication among participants.
The survey has revealed several problems, challenges and trends in the GISMCDA
research. They are related to the developments in geographic information
technology and spatial analysis, as well as the developments in the area of
conceptual and operational validation of the use of MCDA in real-world spatial
problems. The development of GIS-MCDA has been paralleled by the evolution of
geographic information technology. GIS systems have evolved from a ‘close’ expertoriented
to an ‘open’ user-oriented technology. This trend has stimulated a
movement in the GIS community towards using the technology to increase the
democratization of the decision-making process via public participation.
Malczewski (2004) suggests that it is in the context of the debate on the
interrelationship between GIS and society (Pickles 1995) that one can see the
potential for advancing the role of GIS-MCDA in the participatory GIScience.
Multicriteria decision analysis 717
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Specifically, GIS-MCDA should be constructed with two perspectives in mind: the
techno-positivist perspective on GIS, and the socio-political, participatory GIS
perspective. It is expected that the trend towards advancing public participatory
approach to GIS-MCDA system design and application development will be of
critical importance for a successful use of the GIS-MCDA approaches in the realworld
decision situations (Jankowski and Nyerges 2001). Ascough et al. (2002)
suggest that the development of GIS-MCDA must absorb new trends in
geographical information technology including the Web-based applications (e.g.
Zhu and Dale 2001, Rinner and Malczewski 2002, Hossack et al. 2004, Sakamoto
and Fukui 2004) and location-based services. In the context of OpenGIS, the GISMCDA
approaches could be made available as services rather than systems (Rinner
2003). Preliminary efforts to integrate MCDA into the location-based services have
also been undertaken (e.g. Rinner and Raubal 2004).
Recent developments in spatial analysis show that geo-computation (computational
intelligence) offers new opportunities for GIS-MCDA (Wu 1998, Zhou and
Civco 1996, Bennett et al. 1999, Xiao et al. 2002). Geo-computational tools can
potentially help in modeling and describing complex systems for inference and
decision-making. An integration of MCDA and geo-computation can enhance the
GIS-MCDA capabilities of handling larger and more diverse spatial data sets.
Another significant trend has been associated with developing map-centeredexploratory
approaches to GIS-MCDA (Armstrong et al. 1992, Jankowski et al.
2001, Andrienko and Andrienko 2003). The main purpose of these approaches is to
provide the decision-maker with insights into the nature of spatial decision problems
not readily obtained by conventional methods (such as tabular displays). The power
of map-centered-exploratory analysis comes from the confidence in the GIS-based
MCDA procedures that grows as decision-makers see the procedures confirm their
understanding of the decision problem at hand.
The GIS-MCDA research has tended to concentrate on the technical questions of
integrating MCDA into GIS. As a consequence, our understanding of the benefits
of such integration is limited by the lack of research on conceptual and operational
validation of the use of MCDA in solving real-world spatial problems. Very little
empirical research has been undertaken to appreciate the dynamics of spatial
decision making (Jankowski and Nyerges 2001). There are also other, more general,
concerns surrounding the use of multicriteria decision methods in GIS that require
careful consideration. In the MCDA community there has been much discussion
focused on the theoretical foundations and operational validation of the MCDA
methods (Bana e Costa et al. 1997). It is argued that some MCDA procedures are
lacking a proper scientific foundation and some methods involve a set of stringent
assumptions, which are difficult to substantiate in real-world situations (e.g. Hobbs
1980, Hwang and Yoon 1981, Bana e Costa et al. 1997). To a large extent, these
problems have been ignored by the GIS-MCDA community. If a primary purpose
of GIS-MCDA is to process and synthesize a large number of value judgments and
spatial data sets, and to examine the implications of those value judgments for
planning and policy-making, then more careful attention must be paid to the
assumptions underlying the multicriteria procedures.
Acknowledgments
This research was supported by the GEOIDE Network (Project: HSS-DSS-17) of
the Canadian Network of Centres of Excellence (Natural Science and Engineering
718 J. Malczewski
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Research Council of Canada, Social Science and Humanities Research Council of
Canada, and Medical Research Council). The author would like to thank
anonymous reviewers for their constructive comments on an earlier version of this
paper.
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Appendix 1
The following Web-based electronic libraries and databases were searched for
relevant articles:
IEEE XploreH (http://ieeexplore.ieee.org/Xplore),
Ingenta (http://www.ingentaconnect.com),
InspecH (http://www.engineeringvillage2.org),
ISI Web of Knowledge2
(http://isi10.isiknowledge.com),
Pion Publications Ltd. (http://www.pion.co.uk),
Project MUSEH (http://muse.jhu.edu),
ProQuestH (http://proquest.umi.com),
ResearchIndex (http://www.researchindex.com),
ScienceDirectH (http://www.sciencedirect.com),
Scirus (http://www.scirus.com),
Scopus2
(http://www.scopus.com/scopus/home.url),
SpringerLink (http://www.springerlink.com),
WorldCatH (http://firstsearch.oclc.org).
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