Background
Advanced Remote Sensing
Lecture-5
Multi Criteria Evaluation contd.
Background contd.
• Multicriteria evaluation be organized with a view to produce a
single synthetic conclusion at the end of the evaluation or, on
the contrary, with a view to producing conclusions adapted to
the preferences and priorities of several different partners.
• Multicriteria analysis is a tool for comparison in which several
points of view are taken into account, and therefore is
particularly useful during the formulation of a judgment on
complex problems. The analysis can be used with
contradictory judgment criteria (for example, comparing jobs
with the environment) or when a choice between the criteria
is difficult.
• Multicriteria analysis appeared in the 1960s as a decisionmaking tool. It is used to make a comparative assessment of
alternative projects or heterogeneous measures. With this
technique, several criteria can be taken into account
simultaneously in a complex situation. The method is
designed to help decision-makers to integrate the different
options, reflecting the opinions of the actors concerned, into a
prospective or retrospective framework. Participation of the
decision-makers in the process is a central part of the
approach. The results are usually directed at providing
operational advice or recommendations for future activities.
Land is a scarce resource
Essential to make best possible use
Identifying suitability for:
a) agriculture
b) forestry
c) recreation
d) housing
e) etc.
MCE can provide solutions
»Non-monetary decision making tool
»Developed for complex problems
GIS Approaches
Polygon overlay (Boolean logic)
Cartographic modelling
Example uses:
a) nuclear waste disposal site location
b) highway routing
c) land suitability mapping
d) solid waste management planning
e) habitat mapping
f) etc.
Decision
Decision is a choice between alternatives
 Decision frame: the set of all possible alternative
 Candidate set: the set of all locations / pixels that are
being considered
 Decision set: the areas assigned to a decision (one
alternative)
Criterion
Decision rule
Criterion is some basis for a decision.
Two main classes are:
Decision rule is the procedure by which criteria are
combined to make a decision.
Can be:
 Factor: enhances or detracts from the suitability of a land
use alternative
 Constraint: limits the alternatives
 Can be a continuum from crisp decision rules (constraints)
to fuzzy decision rules (factors)
Goal or target: some characteristic that the solution
must possess (a positive constraint)
 Functions: numerical, exact decision rules
 Heuristics: approximate procedures for finding solutions
that are ‘good enough’
Objective: the measure by which the decision rule
operates
Evaluation: the actual process of applying the decision
rule
Kinds of Evaluations
Basic MCE theory
 Single-criterion evaluation
 Multi-criteria evaluation: to meet one objective,
several criteria must be considered
 Multi-objective evaluations:
 “Investigate a number of choice possibilities in the
light of multiple criteria and conflicting objectives”
(Voogd, 1983)
 Generate rankings of choice alternatives
Two basic methodologies:
– Complementary objectives: non-conflicting objectives
(e.g., forest and residential land)
– Conflicting objectives: both cannot exist at the same
place, same time (e.g., reserve forest and timber licenses)
– Boolean overlays (polygon-based methods)
– Weighted linear combinations (WLC) (raster-based
methods)
MCE Techniques
Principles of MCE
 Many techniques (decision rules)
 Most developed for evaluating small problem sets
(few criteria, limited candidate sets)
 Some are suitable for large (GIS) matrices
layers = criteria
cells or polygons = choice alternatives
 Incorporation of levels of importance (weights –
WLC methods)
 Incorporation of constraints (binary maps)
Methodology
 Determine criteria (factors / constraints) to be
included
 Standardization (normalization) of factors / criterion
scores
 Determining the weights for each factor
 Evaluation using MCE algorithms
 Sensitivity analysis of results
Determine the criteria to be included
Factor normalization
 Oversimplification of the decision problem could
lead to too few criteria being used
 Using a large number of criteria reduces the
influence of any one criteria
 They should be comprehensive, measurable,
operational, non-redundant, and minimal
 Often proxies must be used since the criteria of
interest may not be determinable (e.g., % slope is
used to represent slope stability)
 A multistep, process that considers the literature,
analytical studies and, possibly opinions
Standardization of the criteria to a common scale
Need to compare apples to apples, not apples to
oranges
Good: 255
255
For example:
Output
Output
Fuzzy Membership Functions
Factor Normalization: example
• Distance from a road (km)
Poor: 0
• Slope (%)
low
• Wind speed
Input
Consider Range (convert all to a common range)
Meaning (which end of the scale = good)
Used to standardize
the criterion scores
0
high
low
Input
fuzzy membership
1.2
1
0.8
0.6
fuzzy membership
0.4
0.2
41
39
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32
33
30
31
28
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26
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T
25
im
um
m
on
th
ly
m
ax
24
0
Cholera Health Risk Prediction in Southern Africa—the relation between
temperature and risk
Graphs of the Fuzzy Memberships within IDRISI
(Based on Eastman 1999)
high
Determine the weights
 A decision is the result of a comparison of one or
more alternatives with respect to one or more
criteria that we consider relevant for the task at
hand. Among the relevant criteria we consider
some as more important and some as less
important; this is equivalent to assigning weights to
the criterion according to their relative importance.
 By normalizing the factors we make the choice of
the weights an clear process.
MCE Algorithms
Analytical hierarchy process
One of the more commonly-used methods to
calculate the weights.
(This is a freely downloadable ArcGIS script)
Example: weighted linear summation
The most commonly used decision rule is the weighted
linear combination
Map 1
Map 2
Map 3
S = ∑wixi x ∏cj
where:
S is the composite suitability score
x – factor scores (cells)
w – weights assigned to each factor
c – constraints (or boolean factors)
∑ -- sum of weighted factors
∏ -- product of constraints (1-suitable, 0-unsuitable)
Standardise
User
weights
Evaluation
matrix
MCE routine
Output
Map 4
Sensitivity analysis
Sensitivity analysis contd.
 Choice for criteria (e.g., why included?)
 Reliability data
 Choice for weighing factors is subjective
 Sensitivity analysis: vary the scores / weights of the
factors to determine the sensitivity of the solution to
minor changes
 Only addresses one of the sources of uncertainty
involved in making a decision (i.e., the validity of the
information used)
 A second source of uncertainty concerns future
events that might lead to differentially preferred
outcomes for a particular decision alternative.
 Decision rule uncertainty should also be considered
(? MCE itself)
Example of MCE: Wind Farm Siting
Preliminary Siting Factors
Spatial Analytical Hierarchy
Process
 Wind farm siting
 Accessibility to roads
• Will the overall solution change if you use other weighing factors?
• How stable is the final conclusion?
• Distance to primary roads
• Distance to secondary roads
• Distance to rural roads
Find the best wind farm sites
based on siting factors
 Accessibility to transmission lines
• Distance to 100K lines
• Distance to 250K lines
• Distance to above250K lines
 Alternatives
Location—infinite
Divide the space into
squares/cells (200m * 200m)
 Wind power (or wind speed)
 Visibility
 Evaluate each cell based on
the siting factors
• Viewshed size
• # of people in viewshed
Appalachian State University
Siting Steps (MCE)
AHP
 Factor generation
• Distance calculation
• Visibility calculation
 Factor standardization (0 – 100)
• Each factor is a map layer
 Factor weights determination by AHP
 Final score
• Weighted combination of factors
 Exclusion areas
Factor Layers
Wind Turbine visibility--Viewshed
(Turbine: 50m; Observer: 1.5m; Visual distance: 20mi)
Wind Turbine Viewshed Size
Visibility Factor—Viewshed Size
Red—505km2
Green--805km2
Blue--365km2
Visibility Factor--# of People in Viewshed
2000 census block data
Final Score Layer
Candidate Sites
Constraints (binary)
Sites
Waste Dumping Site Selection (MCE)
Waste Dumping Site Selection (MCE)