When making a decision, multi-criteria analysis (MCA) is a technique that takes into account many different criteria. GIS-Multi Criteria Decision Analysis provides a logical, well-structured process for identifying and prioritizing various factors. It enables the alternative solutions under consideration to be ranked in terms of suitability.
Many of the issues we face today are complex, with many competing interests and solutions. However, groups must be able to collaborate and compromise in order to find the most feasible solution. These problems are frequently of a spatial or geographic nature, and thus using GIS with various frameworks or analyses can aid in decision making.
GIS-Multi Criteria Decision Analysis is one approach used to help decision makers consider multiple criteria. MCDA is used to logically evaluate and compare multiple criteria, which are frequently contradictory, in order to make the best decision possible. This is especially useful when you have a diverse set of stakeholders with competing interests, values, and goals.A MCDA could be used in a field to examine a wide range of problems where multiple viable solutions exist. This has been applied to health care issues such as disease treatment or prioritization, purchasing a new car, tourism activities, processing options, transportation, energy use, risk assessment, land use, and site selection.
A Multi criteria decision analysis could be used in a field to examine a wide range of problems where multiple viable solutions exist. This has been applied to health care issues such as disease treatment or prioritization, purchasing a new car, tourism activities, processing options, transportation, energy use, risk assessment, land use, and site selection.
The integration of GIS and Multi criteria decision analysis(GIS-MCDA)
Many today’s problems are geographical in nature, so combining GIS and MCDA has a significant impact. Spatial problems are frequently characterized by a large number of viable alternatives as well as multiple, conflicting, and incommensurate evaluation criteria. The use of a GIS-MCDA is a process that transforms and combines geographical data and value judgments in order to solve spatial problems. This is accomplished by taking into account geographical data models, the spatial dimension of the evaluation criteria, and decision alternatives when evaluating the criteria.
Vehicle routing, site selection, scenario evaluation, land suitability, transportation scheduling, impact assessment, and location-allocation to a variety of sectors are some examples of Application areas.
What are some steps to do MCDA and GIS ?
in GIS based multi-criteria decision there are Six stages
- Specify your issue, goal, or objective. Try to comprehend and define the problem as thoroughly as possible.
- Establish the criteria and constraints. Using a combination of expert opinions and data from multiple sources. This could be obtained through discussions with experts in the relevant fields, a review of the literature, and an analysis of historical data.
- Convert the values to a relative scale. This enables us to compare each of the criteria and represent the judgments and expert knowledge with meaningful numbers.
- Determine the relative importance of each criterion in relation to the objective and to one another.
- Join the layers/criteria together by combining, synthesising, and aggregating them.
- Analyze and then validate your findings.
Methods/Techniques for Multi criteria
Analytical Hierarchy Process (AHP)
Analytic hierarchy process (AHP), also known as the analytical hierarchy process. one of techniques used in Multi criteria decision making. It is a structured technique based on mathematics and psychology for organizing and analyzing complex decisions. It was created in the 1970s by Thomas L. Saaty; in 1983, Saaty collaborated with Ernest Forman to create Expert Choice software, and AHP has been extensively researched and refined since then. It is an accurate method of quantifying the weights of decision criteria. Through pair-wise comparisons, the experiences of individual experts are used to estimate the relative magnitudes of factors. Using a specially designed questionnaire, each respondent compares the relative importance of each pair of items.
After the criteria have been consolidated and classified within the MCDA, the AHP is used to calculate the relative weights, importance, or value of each factor relevant to the problem at hand. After assigning relative weights, we calculate a priority vector, which gives us the overall relevance modifier value for each factor to use in the GIS calculations.
A Consistency Ratio (CR) is then calculated to determine how consistent the judgements have been in comparison to large samples of purely random judgements. If the CR is greater than 0.1, the judgments should be regarded as untrustworthy.
Main advantages of using AHP
- A more systematic and disciplined approach to assessing suitability that divides the problem into hierarchical criteria.
- A more comprehensive and even in examination of the factors, which may be better understood by examining their lower and more specific forms or indicators.
- Addressed both experts and stakeholders to participate in providing input. This type of framework allows for the incorporation and accommodation of both qualitative and quantitative criteria, as well as the contribution of expert knowledge.
Fuzzy Logic
Decision-makers face a plethora of increasingly complex situations, and they are frequently unsure how to assign evaluation scores as crisp value. As a result, designing an MCDA that incorporates fuzzy set theory can account for this uncertainty. A fuzzy AHP (FAHP) is the incorporation of fuzzy logic nuances into the assessment of AHP criteria.
Fuzzy numbers come in a variety of formats, each with its own nomenclature: sine numbers, bell shapes, polygonal, triangular, trapezoids, and so on.
Fuzzy logic is a computing approach based on “degrees of truth” rather than the traditional “true or false” (1 or 0) Boolean logic on which modern computers are based.
Lotfi Zadeh of the University of California, Berkeley, pioneered the concept of fuzzy logic in the 1960s. Zadeh was working on the problem of computer-assisted natural language understanding. Natural language, like most other activities in life and the universe, is difficult to translate into absolute terms of 0 and 1. Whether everything is ultimately describable in binary terms is a philosophical question worth exploring, but in practice, much of the data we might want to feed a computer is in some state in between, as are the algorithms.
We can muddle two aspects of the GIS-MCDA process:
# Each criterion’s values can be transformed.
# Make the criteria’s weightings or relative importance fuzzy.
Why is fuzzy AHP superior to AHP?
a) AHP is a scientific, pairwise comparison-based MCDM method for selecting the best potential alternatives.
b) Fuzzy AHP is a similar approach to AHP, with the exception that it employs TFNs rather than crips numbers. It eliminates ambiguity and uncertainty in decision-making.
