Lab 7: Boolean Multi-criteria Evaluation

1.

In the context of MCE: Boolean is an approach for finding suitable areas based on criteria that states where something can or can’t belong.

2.

Multiplication is used more in MCE because when making a suitability map with constraints, we can mask out the areas that are constrained, and retain the suitable area from both images that are being aggregated. Multiplication is good finding suitable areas that meet all the criteria, addition is good for finding suitable areas that meets at least one of the criteria.

3.

a)

ROADDIST: meters distance from roads

TOWNDIST: Grid Cell Equivalents (GCE) from the town center

SLOPES: percent of slope gradients

These are not comparable.

b)

Categorical data can be considered continuous if an operation is performed on the data. By assigning suitability scores to the categories we can make the data be continuous.

c)

WATERDIST: Meters distance from water (Open Water, Streams, Wetlands)

DEVELOPDIST: Meters distance from developed areas

Yes these are comparable.

d)

In order to meet all the criteria for MCEBOOL:

Have to be on land-use type classified as forested and open undeveloped land

Have to be within 400 meters from any road

Have to be within 400 GCE (10 minutes) of the town center

Have to be on a slope gradient of less than 15 %

Have to be 100 meters away from any water body or wetland

Have to be less than 300 meters from developed land

By opening up the MCEBOOLGROUP file we can see the criteria that was met to make the final MCEBOOL image.

If the data isn’t stored as a group file there is no way to see which criteria was met.

e)

No, there is no way to see which areas with a value of 1 are more suitable. To narrow the suitable area for development we can make the constraints more restricting. We can also add more Boolean criteria to find areas that are more suitable.

f)

BOOLOR is an aggregated Boolean image calculated using the OR function, which requires that at least one criteria be met. By weighting the factors and aggregating them with a weighted linear average (or WLC) we can create a suitability image that lies between the 2 extremes of risk.

BONUS: What is the concept of ‘tradeoff’ and ‘risk’ in Boolean MCE analysis?

For performing MCE using Boolean, it is important to note that tradeoffs between different suitable areas are only set by the parameters which decide if something belongs or doesn’t belong in that area. The degree of how much something belongs in one location verses another would require some type of weighting on the parameters.

It is also important to note that when multiplying images the amount of risk is little and when adding images the amount of risk is high. When adding images the risk is high because only 1 criteria needs to be met and so it is hard to compare 2 different areas that are both considered suitable.

4.