Next, fuzzy layers need to be derived from the data collected. This would provide a suitability value that cannot be obtained from the constraint layer. While constraint factors show where Cheetahs can and can't live it fails to show the better place to live. All fuzzy layers were derived from layers that were previously distance layers. In the analysis it is crucial that the distance from potential disturbances measured. The further away from the disturbances the more suitable the site. The exceptions are the parks close fuzzy and parks fuzzy layer. These particular layers were developed in order to provide two opposite circumstances. There isn't only one possibility when analyzing. Here is where parks fuzzy define distance from parks as being positive. Parks close fuzzy is a layer were the closer the area to a park the higher the suitability. There were two major analysis made where these were the differing factors.

Once the layers were prepared for the Multi-Criteria Evaluation they had to be weighted. Not all factors are as important as the next. Using the Weighted Linear Combination did this. Some factors impact landscape and animals at different rates as well, in different ways. An example from this project is deciding weather or not overgrazing is more important than population density. It seems obvious that there can be overgrazing without high population densities. On the other hand high population densities will most likely cause overgrazing to occur. Therefore, population density would be weighted with a higher number than overgrazing because of its higher relative importance. The Pairwise Table determines these weight values. The Relative importance of the suitability factors use in the MCEWLC analysis is assessed. The weights are given in the interactive cartographic model.

This MCEWLC shows the suitability of the land for for conservation of Cheetahs. The darker green represents a higher suitability based on the criteria outlined in the cartographic model.