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MCE:Criter ia Development and
the Boolean Approach
Exerc ise 2-7
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MCE
Multi-criteria Evaluation
a common method for assessing and
aggregating many criteria.
Decision will not be based on a single criteriabut a combination.
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Types of MCE
MCE
Boolean
Aggregation
Method
WLC
(Weighted
Linear
Combination)
OWC
(Ordered
Weighted
Averaging)
Exercise 2-7 Exercise 2-8 Exercise 2-9
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Objective of the Exercises
single hypothetical suitability problem.
to find the most suitable areasfor residential
development in the town of Westborough,
Massachusetts, USA. The town is located very near two large
metropolitan areas and is a prime location for
residential (semi-rural) development.
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Considerations and Requirements..
Developersprime location (areas that best suit their needsallocated for residential development).
environmental groupswhere new development will or will notoccur. The environmentally-mixed landscape of Westborough includes
many areas that should be preserved as open space and forwildlife.
The town regulationslimit land for development.
All these considerations must be incorporated into the decisionmaking processHence MCE
The goal is to explore potential suitable areasfor residential development for the town of
Westborough: areas that best meet the needs ofall groups involved.
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Considerations
Constraints Factors
LANDCON
WATERCON
MCELANDUSE
TOWNDIST SLOPES
WATERDIST DEVELOPDIST
ROADDIST
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Constraints
YES / NO ? TRUE / FASLE 1 / 0
Constraints are those Boolean criteria that constrain
(i.e., limit) our analysis to particular geographic
regions.
Constraints are always Boolean images.
In this Example: The constraints differentiate areas
that we can consider suitable for residentialdevelopment from those that cannot be considered
suitable under any conditions.
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Factors
Factors are criteria that define some degree
of suitability for all geographic regions.
They define areas or alternatives in terms of
a continuous measure of suitability. Individual factor scores may either enhance
(with high scores) or detract from (with low
scores) the overall suitability of an alternative.
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MCE:Criter ia Development and
the Boolean Approach
STEPS OF THE EXERCISE
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a) Path Setup
Change the Data Paths (under the file menu)
so the folder Idrisi32 Tutorial / MCE is set as
the working folder.
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b) Display of the Image
Display the image MCELANDUSE using the
user-defined MCELANDUSE palette.
Choose to display the legend and title map
components. Use Add Layer to add the streams layer
MCESTREAMS with the user-defined BLUE
symbol file
Add roads layer MCEROADS with the
uniform black symbol file.
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c) Water
Display the image MCEWATER with the Qualitative palette.
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d) Water Constraints
Display the image WATERCON with the Qualitative palette.
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e) Land use Contraints
Observe MCELANDUSE.
Clearly some of these categories will be
unavailable for residential development.
Areas that are already developed,
water bodies,
and large transportation corridors
cannot be considered suitable to any degree.
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f)
Display LANDCON, a Boolean image
produced from MCELANDUSE
Areas suitable1
unsuitable for residential development0.
Suitable categoriesCropland
PastureForest
Open Undeveloped
Unsuitable CategoriesOpen Developed
Multifamily residential
Small Lots ResidentialMed Lots Residentials
Large Lot Residential
Commercial
Industrial
Transportation
Open Water
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g)
Examine the images ROADDIST,
TOWNDIST, and SLOPES using the IDRISI
standard palette.
Display MCELANDUSE with the Qualitativepalette.
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ROADDIST
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TOWNDIST
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SLOPES
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h)
Examine the images WATERDIST and
DEVELOPDIST using the IDRISI standard
palette.
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WATERDIST
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DEVELOPDIST
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Boolean Approach for MCE.
All criteria (constraints and factors) will be
standardized to Boolean values (0 and 1).
method of aggregation will be Boolean
intersection (multiplication of criteria). This is the most common & Simplest GIS
method of MCE.
Severely limit analysis and constrain resultantland allocation choices.
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Boolean Standardization of Factors
"crisp" or "hard" decision
how continuous data (e.g., slopes) can be
effectively reduced to Boolean values.
All of the continuous factors developedpreviously must be reduced to Boolean
constraints.
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Landuse Factor
Of the four landuse types available for development,
forested and open undeveloped lands are the least
expensive and will be considered equally suitable by
developers, while all other land will be considered
completely unsuitable.
Suitable categoriesCropland
PastureForest
Open Undeveloped
Unsuitable CategoriesOpen Developed
Multifamily residential
Small Lots ResidentialMed Lots Residentials
Large Lot Residential
Commercial
Industrial
Transportation
Open Water
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Distance from Roads Factor
To keep costs of development down, areas
closer to roads are considered more suitable
than those that are distant.
Suitable categoriesareas less than 400 meters
from any road
Unsuitable Categories
equal to or beyond 400 meters
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Distance to Town Center Factor
Homes built close to the town center will yield
higher revenue for developers.
Distance from the town center is a function of
travel time on area roads.
Suitable categoriesless than 10
minutes or 400 GCEs of the
town center
Unsuitable Categories400 GCEs or beyond are not suitable.
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Slope Factor
relatively low slopes make housing and road
construction less expensive,
Suitable categoriesslope less than 15%
Unsuitable Categoriesequal to or greater than 15%
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Distance from Water Factor
local groundwater is at risk from septic system
pollution and runoff, environmentalists have pointed
out that
areas further from water bodies and wetlands are
more suitable than those that are nearby.
protected by a 50 meter buffer through constraints.
Suitable categoriesat least 100 meters fromany water body or wetland.
Unsuitable CategoriesWithin 100m of water body or wetland
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IDRISI Steps for Boolean Images
i) Display a Boolean image called LANDBOOL.
j) Display a Boolean image called ROADBOOL.
k) Display a Boolean image called TOWNBOOL.
l) Display a Boolean image called SLOPEBOOL.m) Display a Boolean image called WATERBOOL.
n) Display a Boolean image called DEVELOPBOOL.
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Boolean Aggregation of Factors and
Constraints
all of our factors have been transformed into
Boolean images (i.e., reduced to constraints).
most typical Boolean aggregation procedure.
all eight images are multiplied together toproduce a single image of suitability.
equivalent to a logical AND operation
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In assessing the results of an MCE analysis,
it is very helpful to compare the resultant
image to the original criteria images.
This is most easily accomplished byidentifying the images as parts of a collection
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o)
Open the Display Launcher dialog box and invoke the pick list.
MCEBOOLGROUP in the list with a small plus sign next to it.
This is an image group file that has already been created usingCollection Editor.
Click on the plus sign to see a list of the files that are in the
group. Choose MCEBOOL and press OK.
Note that the file name shown in the Display Launcher input boxis MCEBOOLGROUP.MCEBOOL.
Choose the Qualitative palette and click OK to display
MCEBOOL.
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p)
Use the feature properties query tool (from its
toolbar icon or Composer button) and explore
the MCEBOOLGROUP collection.
Click on the image to check the values in thefinal image and in the eight criterion images.
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Assessing the Boolean Approach
a value of 1 in the final suitability image is only possible whereall eight criteria also have a value of 1,
a value of 0 is the result if even one criterion has a value of 0.
i.e. the suitable places must have the suitability criteria of all thefactors/ constraints. (maximum relationship).
If any one of the factor show unsuitability and all other showsuitable, the location is still unsuitable.
they do not trade off.
In terms of risk, it is very conservative.
no tradeoff and risk aversion
The result is the best location possible for residentialdevelopment and no less suitable locations are
identified.
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MCE:Criter ia Development and
the Boolean Approach
Thanks .
Quest ions / Queries ? ? ?