Weighted suitability analysis allows one to answer questions that are impacted by many factors and assign varying weights to each of the factors. It ranks locations based on the suitability rather than giving only a suitable/not suitable result. The black bear (Ursus americanus) frequents the Smokey Mountains National Park, and we can use the weighted overlay to determine the best location for the bears to live if they become a nuisance and need to be relocated.
Suitability analysis or site selection can be performed via GIS analysis and determines the best locations for something dependent on applied criteria.Weighted suitability analysis allows one to answer questions that are impacted by many factors and assign varying weights to each of the factors. The result gives more information than binary analysis, as it ranks locations based on the suitability rather than giving only a suitable/not suitable result. We need to identify the problem (bear relocation), and break it down into smaller submodels. Then we determine which layers are important for determining bear habitat. Layers can be vector or raster data. For this problem, stream, trail, and road data were line vectors, vegetation was a polygon vector, and elevation was a raster dataset. Transformations that can happen to analyze data include changing vector data to raster data, calculating distances to features, and/or determining slope, aspect, etc. The last step, before performing the weighted overlay, is to reclassify each layer. This gives the layers a common scale (i.e. 1 to 5, 1 to 9, etc.) that will be preserved in the final overlay. Table 1 shows the selection factors, and the reclassification criteria on a 1 to 3 scale. Finally, the weight of each layer is assigned, the layers are combined in a weighted overlay, and the results are analyzed. See Fig. 1 for a graphical depiction of the model used to solve the bear exercise. The Procedure-Log can be found here.
Application & Reflection:
Problem description: Our federal farm loan was rejected, and we need to find a new location to purchase a farm. We can use a weighted overlay to do this.
Data needed: We can look up the soil type (NRCS), distance to population centers, land cover and land use, distance to major roads, and distance to the nearby farmers market.
Analysis procedures: We can give the parameters that we value most heavily (distance to population and nearby farmers markets) a higher weighting than other important parameters, the parameters we do not value as great (soil type, land cover), can be less of an influence on where we search for land.