Title: Examples of MCA
1Examples of MCA
- Dating
- Best place to live or retire
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4Recent MESM projects with MCA
- Prioritize invasive species and treatment
locations in Santa Monica Mtns. - Prioritize sites
- Ventura Foothills, Blue oak woodland, Santa Clara
River, Goleta Brownfields, Valley oak restoration - Map stress index of watersheds
- Chiapas, Los Padres
- Campus Climate Neutral 1 policy ranking
5What is Multicriteria Analysis?
- Decision analysis
- A set of systematic procedures for analyzing
complex decision problems (multiple, conflicting
and incommensurate objectives) - The purpose of any GIS-based decision analysis
is to provide insights and understanding, rather
than to prescribe a correct solution. Often
the process of attempting to structure the
decision problem is more useful in achieving
these aims than the numeric output of the
GIS-based modeling. (Malczewski 2000)
6Types of decisions
- Sites
- Pick best alternative (site)
- IHOP, landfill
- Identify set of good alternatives
- Top 10 beaches or destinations
- Set of graduate schools to apply to
- Rank all alternatives (i.e., a map)
- Regional vulnerability, sustainability index
- Set of Sites
- Pick best region or area
- Areas for agriculture, corridors
7Elements of multicriteria analysis
- Goal(s)
- Decision maker(s)
- Preferences (weights)
- Attitude toward risk
- Evaluation criteria
- Objectives (desired state)
- Attributes (measure performance in relation to
objectives) - Alternatives
- Outcomes of alternative by criteria
- Decision rules
- Sensitivity analysis
8Decision matrix
Source Malczewski 1999
Rank 1
Rank 2
Rank m
9Hierarchy of criteria
- Many to many relationship possible
Source Malczewski 1999
10Criterion map scales
Source Malczewski 1999
11Point allocation
Source McCoy et al. 2003
12Why weight?
- To express the importance (to the decision maker)
of each criterion in relation to each other - But also dependent on the range of criteria
values - Why not just ask decision maker for their
weights? - What if decision maker cannot weight criteria?
13AHP pairwise comparison
- Calculate weights and consistency ratio from
comparison matrix
14Simplified version of AHP
Source Strager and Rosenberger 2005
15Goal Identify high priority lands for protecting
terrestrial biodiversity in California
Source Regan et al. in press
16Basketball analogy of risk
- Goal assemble a championship caliber team for a
given budget - Alternatives
- Kobe Bryant with four mediocre players
- Five good (but not great) players
- What if Kobe got hurt?
- What if the other four players (or even one of
them) was terrible? - In an environmental context
- map analysis was based on erroneous data or
things change - weakest link
17Choice of decision rule How to aggregate
criteria?
Source Moffett and Sarkar 2006
18Integration matrix
Source WWF
19Boolean logic
Source Jiang and Eastman 2000
20Weighted Linear Combination
- Compensatory tends to average
- Assumptions
- Linearity desirability of additional attribute
unit is constant for any level of attribute - Additivity no interaction (correlation) between
attributes - Tends to be ad hoc with little theoretical support
21Ideal point or compromise programming
- Orders a set of alternatives on the basis of
their distance from an ideal point in
multicriteria space - Can also consider the maximum distance from
negative ideal (risk-averse) - Compromise method prefers closest to ideal and
farthest from negative ideal
22Concordance/Discordance Analysis (Outranking)
- Based on pairwise comparison of alternatives
- Concordance is all criteria for which A is not
worse than B (but not how much better) - Discordance is all criteria for which A is worse
than B (but not how much better)
23VisualizationQuantiles
24VisualizationRadar plots
25VisualizationConsumers Reports
26Highlights
- When to use MCA instead of statistics or process
models? - Rich literature in multicriteria analysis
- Dont reinvent the wheel or make a wobbly wheel
- Start from objectives, not data
- Select method that is consistent with
- Assumptions (ranking outcomes, criteria, and
alternatives, risk) - Type of decision (best alternative, good
alternatives, or rank all alternatives) - Criteria are usually facts, weights are social
values