2.8 Multi-Criteria
Evaluation (MCE) and Weighted Linear Combination (WLC)
Multi-Criteria Evaluation consists of a set of procedures whose
purpose is to facilitate decision making by investigating a number of
alternatives in light of multiple conditions and conflicting objectives (Voogd
1983). It has been used in multiple fields, such as land suitability
evaluation (Janssen and Rietveld 1990; Pereira and Duckstein 1993), urban
planning (Voogd, 1983), and residential quality assessment (Can 1993). In MCE
multiple layers can be scaled, weighted and summed into one stratum
representing levels of suitability for an investigated issue (Eastman et al.
1995, Jankowski 1995). This process can be brought out by experts, interest
groups and/or stakeholders founding their evaluation on the degree of
suitability or the importance of the within variables of the issue considered
(Dodgson et al. 2000, Malczewski 2004).
According to Eastman (2001), three techniques are generally used
to implement MCE. The first option is Boolean Overlay in which the criteria
are reduced to two logical suitability statements. This technique is deemed
lacking of flexibility regarding the number of choices permitted (Mahini and
Gholamalifard 2006). The second procedure, the Weighted Linear Combination
(WLC), offers more flexibility than the Boolean approach. Hopkins (1977)
portrayed WLC as the most common techniques to integrate multi-criteria
evaluation in GIS for land suitability. The last technique is known as Ordered
Weighted Average, which is a stronger extension of the two previous techniques,
and which addresses uncertainty in modeling interaction between various
criteria (Bell et al. 2007).
Another typology of MCE classifies it into two approaches:
concordance-discordance analysis and WLC (Voogd 1983, Carver 1991, and Eastman
et al. 1995). In the concordance-discordance approach each pair of cell is
compared on specific criteria in order to determine which cell outweighs the
other, while WLC is based on multiplying a designated weight to the multiple
factors that are subsequently summed and ranked (Aly et al. 2005). Whereas the
earlier is computational impractical for a large raster dataset, the later is
suitable for solving problems which involves multiple factors with a raster
geodatabase (Aly et al. 2005). The implementation of WLC is made simple within
GIS, using map algebra operations and cartographic modeling (Tomlin 1990). In
addition, it is easy to understand and appealing to decision makers (Massam
1988).
Weights assigned in a WLC process may derive from different
techniques including Analytical Hierarchy Process (AHP) introduced by Saaty
(1977) and based on pair-wise comparison of factors or alternatives; experts
judgment or opinion used in many studies (e.g. Beaton 1986, Dakin and Armstrong
1989, Steptoe and Wardle 1994, Clevenger et al. 2002, Tobias 2004), and public
opinion. AHP presents the inconvenient of being «too data-hungry»,
not intuitive, and inconsistency-prone (Bailey and Grossardt 2006). Wherever
the pair wise comparison is to be achieved on an important quantity of
variables, the process becomes cumbersome and can easily result in
inconsistency related to the scores assigned. Surveying the public to collect
its view about a matter to which it is unfamiliar would result in collecting
erratic and meaningless data. The Expert Opinion Weighting (EOW) is considered
more suited to this study for its flexibility and communication accessibility.
Yet the designation of experts may be cautiously used since no similar study
experience could be accounted to the respondents to the survey, though they all
have working experience in health and environment.
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