MCE: Criteria Development and the Boolean Approach

transcript

Exercise 2-7

Multi-criteria Evaluation a common method for assessing and

aggregating many criteria. Decision will not be based on a single criteria

but a combination.

Types of MCE

Boolean Aggregation

Method

WLC (Weighted

LinearCombination)

OWC(Ordered Weighted Averaging)

Exercise 2-7 Exercise 2-8 Exercise 2-9

Objective of the Exercises

single hypothetical suitability problem. to find the most suitable areas for 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.

Considerations and Requirements.. Developers prime location (areas that best suit their needs

allocated for residential development). environmental groups where new development will or will not

occur. The environmentally-mixed landscape of Westborough includes

many areas that should be preserved as open space and for wildlife.

The town regulations limit land for development.

All these considerations must be incorporated into the decision making process Hence MCE

The goal is to explore potential suitable areas for residential development for the town of

Westborough: areas that best meet the needs of all groups involved.

Considerations

Constraints Factors

LANDCON

WATERCON

MCELANDUSE

TOWNDIST SLOPES

WATERDIST DEVELOPDIST

ROADDIST

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 residential development from those that cannot be considered suitable under any conditions.

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.

STEPS OF THE EXERCISE

a) Path Setup

Change the Data Paths (under the file menu) so the folder Idrisi32 Tutorial / MCE is set as the working folder.

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.

c) Water

Display the image MCEWATER with the Qualitative palette.

d) Water Constraints

Display the image WATERCON with the Qualitative palette.

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.

Display LANDCON, a Boolean image produced from MCELANDUSE Areas suitable 1 unsuitable for residential development 0.

Suitable categoriesCroplandPastureForestOpen Undeveloped

Unsuitable CategoriesOpen DevelopedMultifamily residentialSmall Lots ResidentialMed Lots ResidentialsLarge Lot ResidentialCommercialIndustrialTransportationOpen Water

Examine the images ROADDIST, TOWNDIST, and SLOPES using the IDRISI standard palette.

Display MCELANDUSE with the Qualitative palette.

ROADDIST

TOWNDIST

SLOPES

Examine the images WATERDIST and DEVELOPDIST using the IDRISI standard palette.

WATERDIST

DEVELOPDIST

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 resultant land allocation choices.

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 developed

previously must be reduced to Boolean constraints.

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 categoriesCroplandPastureForestOpen Undeveloped

Unsuitable CategoriesOpen DevelopedMultifamily residentialSmall Lots ResidentialMed Lots ResidentialsLarge Lot ResidentialCommercialIndustrialTransportationOpen Water

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 Categoriesequal to or beyond 400 meters

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.

Slope Factor

relatively low slopes make housing and road construction less expensive,

Suitable categoriesslope less than 15%

Unsuitable Categoriesequal to or greater than 15%

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 from any water body or wetland.

Unsuitable CategoriesWithin 100m of water body or wetland

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.

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 to

produce a single image of suitability. equivalent to a logical AND operation

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 by identifying the images as parts of a collection

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 using

Collection 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 box

is MCEBOOLGROUP.MCEBOOL. Choose the Qualitative palette and click OK to display

MCEBOOL.

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 the final image and in the eight criterion images.

Assessing the Boolean Approach

a value of 1 in the final suitability image is only possible where all 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 the

factors/ constraints. (maximum relationship). If any one of the factor show unsuitability and all other show

suitable, 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 residential development and no less suitable locations are

identified.

Thanks ….

Questions / Queries ? ? ?

MCE: Criteria Development and the Boolean Approach

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