There are a number of multi-criteria methods that can be utilized to facilitate individual or group decision-making: 1. Analytic Hierarchy Process (AHP) 2. AHP Combined Method 3. Fuzzy AHP 4. Fuzzy AHP Combined 5. Fuzzy AHP Group 6. Group Evaluation Method 7. Weighted Sum Method (WSM) 8. Weighted Product Method (WPM)

1. MULTI CRITERIA DECISIONMAKINGIN SPATIAL
DATA ANALYSIS
PresentationBy:Preeti kumari
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2. What is multi-criteria analysis?
 Multicriteria analysis is generally defined as “a decision-aid and a mathematical tool allowing
the comparison of different alternatives or scenarios according to many criteria, often
conflicting, in order to guide the decision maker towards a judicious choice”.
 Multi-criteria analysis (MCA) is a technique used to consider many different criteria when
making a decision. MCA gives a logical, well-structured process to follow so different factors
can be clearly identified and prioritised. It allows the alternative solutions being considered to
be ranked in order of suitability
 Spatial multicriteria decision making refers to the application of multicriteria analysis in spatial
context where alternatives, criteria and other elements of the decision problem have explicit
spatial dimensions. Since the late 1980s, multicriteria analysis has been coupled with
geographical information systems (GIS) to enhance spatial multicriteria decision making.

3. WHAT IS IT USED FOR?
 Spatial MCA is used for decisions with a geographical
element, most often in site selection processes where
multiple factors need to be considered.Eg.:
 Site location
 Land use
 Distance to areas of population
 Local area demographics
 Proximity to transport and road infrastructure
 Environmentally sensitive areas

4. WHO USES IT ?...
Spatial MCA can be used by any decision-maker
needing to consider location – for tasks such as
site selection and site management. For example:
 Natural resource managers
 Town planners
 Utilities managers
 Policy makers
 Emergency response managers
 Healthcare managers

5. Basicprinciplesof GIS-based MCDA
MCDA – Multi-Criteria Decision Analysis
Problem
 spatial decision problems typically involve a large set of feasible alternatives & multiple evaluation criteria most of the time, these are
conflicting
alternatives & criteria are often evaluated by a number of individuals (decision-makers, managers, stakeholders, interest groups).
 most of the time, they also have conflicting ideas, preferences, objectives, etc.
many spatial decision problems give rise to the GIS-based MCDA
 to aid in the decision making process
GIS
techniques & procedures have an important role to play in analysing decision problems
 recognized as a decision support system involving the integration of spatially referenced data in a problem solving environment
MCDA
provides a rich collection of techniques & procedures for structuring decision problems, & designing, evaluating & prioritizing
alternative decisions
GIS-MCDA
can be thought of as a process that transforms & combines geographical data & value judgments (the decision-maker’s preferences) to
obtain information for decision making

6. BASIC PRINCIPLES OF GIS-BASED MCDA-DEFINITIONS
 Decision – is a choice between alternatives
i.e. best land use among different land use alternatives
 Criteria
are set of guidelines or requirements used as basis for a decision
Two types: factors & constraints
o A factor is a criterion that enhances or detracts from the suitability of a specific alternative for the
activity under consideration
i.e. distance to road (near = most suitable; far = least suitable)
o A constraint serves to limit the alternatives under consideration; element or feature that represents
limitations or restrictions; area that is not preferred in any way or considered unsuitable.
i.e. protected area, water body, etc. (usually represented by a Boolean mask)
See Eastman et al. (1995)

7. BASIC PRINCIPLESOF GIS-BASEDMCDA-DEFINITIONS
Decision rule: procedure by which criteria are combined to arrive at a particular evaluation.
1) Choice function – provides a mathematical means for comparing alternatives; numerical, exact decision rules
2) Choice heuristic – specifies a procedure to be followed rather than a function
 Objective
the measure by which the decision rule operates (i.e. identify suitable areas for a housing project) in a single
objective multi-criteria evaluation, it is also considered as a ‘goal’
 Suitability
is the characteristic of possessing the preferred attributes or requirements for a specific purpose
 Suitability analysis
is a GIS-based process used to determine the appropriateness of a given area (land resource) for a specific use, i.e.
agriculture, forestry, business, urban development, livelihood projects, etc.
Source: Eastman et al. (1995)

8. BASIC PRINCIPLES OF GIS-BASED MCDA
Multi-Criteria Decision Making/Analysis (MCDM/MCDA)
Multi-Criteria Evaluation
sometimes it is also referred to as multi-attribute
evaluation or Multi-Attribute Decision Analysis
(MADA)
Example: site suitability analysis for housing
development (specific single objective)
Multi-Objective Evaluation
sometimes it is also called as Multi-Objective Decision
Analysis (MODA)
Example: analysis for best land use (forest, agriculture,
residential, etc.) - multiple objectives
1) suitability analysis per land use;
2) Multi-Objective Land Allocation (MOLA)

9. METHODSOFMULTI-CRITERIA EVALUATION (MCE)
Steps:
1. Set the goal/define the problem
2. Determine the criteria (factors/constraints)
3. Standardize the factors/criterion scores
4. Determine the weight of each factor
5. Aggregate the criteria
6. Validate/verify the result
1. Set the goal/define the problem
As a general rule, a goal must be:
S– Specific
M– Measurable
A– Attainable
R– Relevant
T– Time-bound
Source: Haugey, D. “SMART Goals”. Project Smart.
www.projectsmart.co.uk/smart-goals.html

10. METHODS OF MULTI-CRITERIA EVALUATION (MCE)
2. Determine the criteria
(factors/constraints)
how much details are needed in the analysis
affects the set of criteria to be used
 i.e. main roads only vs. including minor roads;
no. of houses vs. no. Of residents; etc.
Criteria should be measurable
If not determinable, use proxies
i.e. slope stability can be represented by slope
gradient
3. Standardize the factors/criterion scores
Set the suitability values of the factors to a common scale to make
comparisons possible
It is hard to compare different things (i.e. mango vs. banana)
For example
Elevation (m)
Slope (%)
Convert them to a common range, i.e. 0 – 255
0 = least suitable; 255 = most suitable

11. Fuzzy Membership Functions are used to
standardize the criterion scores.
 Decision-makers have to decide based on their
knowledge & fair judgment which function should
be used for each criterion
Source (Figures): Eastman (1999; 2006
Linear Membership Function
3. Standardize the factors

12. Methods of Multi-Criteria Evaluation (MCE)
4. Determine the weight of each factor
There are several methods
 Ranking
i.e. 3 factors: rank the factors with 1, 2, & 3, where 1 is the least
important while 3 is the most important
Rating
i.e. 3 factors: rate the factors using percentile – Factor 1 with the
lowest percentage as the least important & Factor 3 with the
highest percentage as the most important
Rankings & ratings are usually converted to numerical weights
on a scale 0 to 1 with overall summation of 1 (normalization).
i.e. Factor 1 = 0.17; Factor 2 = 0.33; & Factor 3 = 0.50;
 Pairwise comparison
(Analytical Hierarchy Process (AHP)
A matrix is constructed, where each criterion is compared
with the other criteria, relative to its importance, on a scale
from 1 to 9.
where 1 = equal preference between two factors; 9 = a
particular factor is extremely favored over the other
a weight estimate is calculated & used to derive a
consistency ratio (CR) of the Pairwise comparisons
• If CR > 0.10, then some Pairwise values need to be
reconsidered & the process is repeated until the desired value
of CR < 0.10 is reached.
Like in ranking & rating, AHP weights are also expressed in
numerical weights that sum up to 1.

13. 4.DETERMINE THE WEIGHT OF EACH FACTOR (USING AHP)

14. Methods of Multi-Criteria Evaluation(MCE)
5. Aggregate the criteria
Weighted Linear Combination is the most commonly used
decision rule
Formula: S = Σwixi x Πcj
Where:
S – is the composite suitability score
xi – factor scores (cells)
wi – weights assigned to each factor
cj – constraints (or Boolean factors)
Σ -- sum of weighted factors
Π -- product of constraints (1-suitable, 0-unsuitable)
S = Σwixi x Πcj
Example: Applying it in GIS raster calculator
S =((F1 * 0.67) + (F2 * 0.06) + (F3 * 0.27)) * cons_boolean
Note: F1, F2, F3 & cons_boolean are thematic layers representing the
factors & constraints.
6. Validate/verify the result
 to assess the reliability of the output
Ground truth verification
 i.e. conduct a field survey to verify sample areas
Sensitivity analysis
How do the following affect the result?
altering the set of criteria (plus or minus)
altering the respective weights of the factors
Is the result reasonable?
Does the result reflect reality?
Etc.

15. Method Advantages Disadvantages Areas of Application
Multi-Attribute Utility
Theory (MAUT)
Takes uncertainty into account; can
incorporate preferences
Needs a lot of input; preferences
need to be precise.
Economics, finance, actuarial, water
management, energy management,
agriculture
Analytic Hierarchy
Process (AHP)
Easy to use; scalable; hierarchy
structure can easily adjust to fit many
sized problems; not data intensive.
Problems due to interdependence
between criteria and alternatives;
can lead to inconsistencies
between judgment and ranking
criteria; rank reversal
Performance-type problems, resource
management, corporate policy and
strategy, public policy, political strategy,
and planning.
Case-Based Reasoning (CBR)
Not data intensive; requires little
maintenance; can improve over time;
can adapt to changes in environment.
Sensitive to inconsistent data;
requires many cases.
Businesses, vehicle insurance, medicine,
and engineering design.
Data Envelopment
Analysis (DEA)
Capable of handling multiple inputs and
outputs; efficiency can be analysed and
quantified
Does not deal with imprecise
data; assumes that all input and
output are exactly known
Economics, medicine, utilities, road
safety, agriculture, retail, and business
problems.
Fuzzy Set Theory Allows for imprecise input; takes
into account insufficient
information
Difficult to develop; can
require numerous simulations
before use
Engineering, economics,
environmental, social, medical, and
management.

16. Method Advantages Disadvantages Areas of Application
Simple Multi-Attribute
Rating Technique
(SMART)
Simple; allows for any type of weight
assignment technique; less effort by
decision makers.
Procedure may not be convenient
considering the framework.
Environmental, construction,
transportation and logistics,
military, manufacturing and
assembly problems
Goal Programming (GP) Capable of handling large-scale
problems; can produce infinite
alternatives.
It’s ability to weight coefficients;
typically needs to be used in
combination with other MCDM
methods to weight coefficients.
Production planning, scheduling,
health care, portfolio selection,
distribution systems, energy
planning, water reservoir
management, scheduling, wildlife
management.
ELECTRE Takes uncertainty and vagueness into
account.
Its process and outcome can be
difficult to explain in layman’s terms;
outranking causes the strengths and
weaknesses of the alternatives to not
be directly identified.
Energy, economics,
environmental, water
management, and transportation
problems.
PROMETHEE Easy to use; does not require
assumption that criteria are
proportionate.
Does not provide a clear method by
which to assign weights.
Environmental, hydrology, water
management, business and
finance, chemistry, logistics and
transportation, manufacturing and
assembly, energy, agriculture.
Simple Additive
Weighting (SAW)
Ability to compensate among criteria;
intuitive to decision makers;
Estimates revealed do not always
reflect the real situation; result
Water management, business, and
financial management.

17. Examples: Applications of GIS-based MCE
1. SUITABILITY ANALYSIS FOR BEST SITE FOR A NEW SCHOOL
Goal
– to produce a map showing the best site for a new school
Criteria
Factors
Distance To Recreational Sites – areas near to recreational facilities are preferred
Distance To Existing Schools – areas away from existing schools are preferred
Slope – areas on flat terrain are preferred
Land Use – agricultural land is most preferred followed by barren land, bush/transitional areas, forest, &
built-up areas.
Constraints
 Water & wetlands
For mor e details, see: ESRI (2007). Using the conceptual model to create a suitability map. ArcGIS Tutorial.
webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=tutorials

18. 2. Suitability analysis for best site for a newschool
Distance to recreational
sites Slope
Distance to existing
schools Land use
Suitability map for best site for a new school
Standardization method:
Continuous data – re-classed (10 classes) & assigned a suitability value per class using a scale of 1 – 10 (10 = most suitable; 1 = least
suitable)
Categorical data (land use) - assigned a suitability value per class using a scale of 1 – 10 (10 = most suitable; 1 = least suitable)
Using Raster Calculator: (Weighted Overlay can also be used)
S =((dist_rec * 0.50) + (dist_school * 0.25) + (slope * 0.125) + (luc * 0.125)) * cons_boolean
For more details, see: ESRI
(2007). Using the
conceptual model to create
a suitability map. ArcGIS
Tutorial.
webhelp.esri.com/arcgisdeskt
op/9.2/index.cfm?TopicNam

19. REFERENCES & SUGGESTED READINGS
 GIS-based Multi-Criteria Decision Analysis (in Natural Resource Management) Ronald C. Estoquem(Student ID Number: 201030243)
D1 – Division of Spatial Information Science
 ESRI (2007). Using the conceptual model to create a suitability map. ArcGIS Tutorial. Accessed January 11,
2011.webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=tutorials
 Haugey, D. “SMART Goals”. Project Smart. Accessed January 11, 2011.www.projectsmart.co.uk/smart-goals.html
 Saaty, T L (1980). The Analytic Hierarchy Process. New York: McGraw Hill.
 Teknomo, K (2006). Analytic Hierarchy Process (AHP) Tutorial . Accessed January 11, 2011.http://people.revoledu.com/kardi/tutorial/ahp/
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