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Top1. Introduction
The ultimate goal of geographic information systems (GIS) is to provide support for spatial decision-making. For the last two or three decades there has been a growing interest in the subject of integrating multi-criteria decision analysis (MCDA) and GIS (Malczewski, 2007). The need to address the issue of applying the established concepts of multi-criteria decision making to spatial problems, and adapting them with respect to the nature and the format of GIS data, has resulted in a whole new interdisciplinary field of study, commonly referred to as GIS-based multi-criteria decision analysis (GIS-MCDA). There is a vibrant community within the field conducting research related to a number of application areas, such as environment, transportation, urban planning, waste management, hydrology, agriculture, forestry etc.
Malczewski & Rinner (2015) define GIS-MCDA as “a collection of methods and tools for transforming and combining geographic data and preferences (value judgments) to obtain information for decision making.” Greene et al. (2011) point out that, while virtually all multi-criteria decision-making (MCDM) methods can be applied to spatial problems, not all MCDM methods are suitable for all spatial problems. A list of factors which describe a given decision problem and which influence the choice of a method, presented in Greene et al. (2011), includes problem types (choice, ranking, sorting), number of decision makers, number of objectives, number of alternatives, uncertainty, risk tolerance, existence of constraints, decision phase, measurement scales and units, experience and computational capacity, among others.
Malczewski (2007) presents a survey of GIS-MCDA approaches where he classifies articles from 1990 to 2004 according to which decision making methods were used. From the classification scheme, it became obvious that the use of methods in GIS-MCDM studies has been limited to only a few approaches such as weighted summation, ideal/reference point, AHP and outranking methods, despite a considerable number of alternative decision-making methods being proposed in the MCDA literature. It is worth reminding that the survey was conducted nearly ten years ago, but our analysis of the representative recent case studies shows that the trend has not changed significantly. We performed a search on articles listed in SCOPUS and published after 2009, whereby we used the search term “GIS” combined with “MCDM”, “MCDA”, “multiple criteria decision” and “multi-criteria decision”. Out of 533 articles, we analysed the ten most cited GIS case studies from the list with respect to the used method. The result of this analysis, presented in Table 1, shows that the majority of research within GIS-MCDM still focuses on a limited number of methods, most notably Analytic Hierarchy Process (AHP). AHP has been used in GIS-MCDM both in discrete choice models, where the number of alternatives is relatively small, and in continuous choice models, where decision problems are usually modelled using raster layers where each raster cell is an alternative, thus making pairwise comparison of the alternatives practically impossible. In the former case, AHP is used to aggregate the priority on all levels - between the criteria with respect to the main objective, between sub-criteria (if any) with respect to the parent criterion, and between the alternatives with respect to each criterion. In the latter case, AHP is only used to derive the weights of the criteria, i.e. the weights associated with attribute map layers (Malczewski, 2007). Some compensatory aggregation method is then used to obtain the score for each alternative in the set. Combining a weighting method, most commonly AHP, and an aggregation method is by far the most common approach in GIS decision making. Weighted linear combination (WLC) and boolean overlay operations are the most often deployed aggregation methods, as they are most intuitive and most straight-forward (Malczewski, 2004). In WLC, a total score for each alternative is obtained by multiplying the weight assigned to each criterion by the scaled value given to the alternative on that criterion, then summing the products over all attributes. Another popular aggregation method is ordered weighting averaging (OWA) that uses a fuzzy approach based on Yagers work on ordered weighted aggregation operator (see Yager, 1988; Jiang & Eastman, 2000).