1. WLC allows ranking of the various factors, showing what is more important (weighed more) than other factors. Boolean MCE only gives a "yes or no" answer to various factors. For example, WLC allows us to rank the different land types so we can give higher importance to suitable or preferred areas and lower importance to unsuitable or non-preferred areas. Boolean MCE would force us to say which land types are suitable and which are not.
2. "Fuzzy standarization of continuous factors" means taking factors of a continuous scale and standarizing it so that every factor is on the same scale. For example, the scale for the distance to town and distance to water are different. By standardizing both scales to a shared scale (0-255), we can now rank and compare the two factors when we produce our final map.
3. Categorical rescaling does not need to be mathamatically rescaled. We simply assign a rank to the various categories. For example, cropland for my cafe analysis is given a rank of 0 while open undeveloped land is given a rank of 255. Continuous rescaling involves using some sort of mathamatical formula that rescales a factor to the standardized scale. For example, the "distance to town" factor has an original scale of 0 to 581.68. We then standardize it and it is rescaled to a 0 to 255 scale.
4. For the dataset used in my project, OWA rescaling would be more appropriate. My various factors, such as density of deaths and distance to the green zone require different weights. While the shortest distance would be preferable, we absolutely have to avoid areas of high deaths density. In this case, distance would be weighted less while deaths density would be weighted more.