Lab 1 - Suitability Analysis

This begins the blog section for Application in GIS (GIS5100). Last week was an introduction to the course and a review of everything I should have remembered from last semester. Let's just say GIS work is a lot more complicated with minimal directions but with ArcGIS Help, most things are possible.

The real work started this week with Suitability Analysis. The first challenge was to identify suitable areas for a mountain lion habitat using data provided by the instructor using a Boolean Suitability Model. The elements we used were a land cover raster file, an elevation data raster file, a road vector file, and a river vector file. I identified the suitable habitat areas using a vector method (converting all raster data to vector data) and a raster method (converting all vector data to raster). Both methods reduced all of the data to a basic yes or no option, does the area meet requirements or not? Using the Reclassify Tool in the Spatial Analyst Toolbox all of these yes and no answers were converted to 1's and 0's in the attribute tables for all of the files being analyzed. By the end of the process all I had to do was write one SQL query to sift through all of the data and find the areas that answered yes to every suitability requirement.

Then we took things a step further and rated locations by suitability, not only figuring out what areas were suitable but identifying how suitable they were. The process was very similar to the first part of the lab except in this section all of the data was analyzed in raster form. The difference came with the application of the Weighted Overlay Tool. Using this tool you can sort your data by how important it is, not just if it meets your needs. When assessing the slope of an area everything over 10 degrees slope may meet the suitability requirements but but a slope greater than 16 degrees would be better and a slope greater than 20 degrees would be ideal. You can assign values the data indicating these preferences. Then using the Weighted Overlay tool you can factor in these preferences associated with individual datasets as well the importance of one dataset over another. For example, soil composition may be more important than slope but you also want to consider the preference for different slope values and soil types. For the lab we evaluated five criteria; slope, soil, land cover, and proximity to rivers and roads. The graphic below shows the difference between an equally-weighted analysis and an alternate where slope was deemed the most important factor.

Suitability Analysis for Lab 1