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| Two weighted overlay results. |
Discussion
The above map shows the results of two different weighted overlay results. Both maps were derived from the same input data (ranked highway, river, slope, soil, and landcover type) but the overall importance each dataset was weighted differently using the Weighted Overlay tool. The map on the left shows the model results if all criteria are given the same weight (in this case, each criteria weighted 20% out of 100), and the map on the right shows the results where some criteria (such as slope at 40%) are given more weight than others (such as roads at 10%).As you can see, with the variable weight scenario map on the right the resulting classes were not enough to fill all 5 categories. What the category range means is that areas classed as being 1 are least suited (in terms of the overall criteria and their given weights) and areas classed as being 5 are most suited. Under the alternative scenario the best results are average suitable locations only.
Quite a bit of data processing went into the production of the above maps. The above maps show raster data, not vector data - so those layers that were shapefiles were converted using Euclidean Distance, then reclassified to show the desired number of classes/rankings. The raster data was also reclassified the same way.
A benefit of the weighted overlay over simple Boolean analysis is that one can see the shades of gray within the data... although as shown on the map above, it does matter how you initially class and rank your data. The results of a Boolean analysis are very easy to interpret but weighted overlays are a whole other beast - the above results are a bit trickier to understand, and a solid methodology is required to make sense of it all.
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