Lab 1 - Suitability Analysis

This was the first lab of the semester for Applications in GIS, and it was interesting to learn some new things about ArcMap and to use the tools to analyze information in a decision making process. In this lab, we carried out several suitability analyses in vector and raster, and the reading for the week helped out a lot as far as visualizing what we were doing. Before even starting the analysis, we first had to ask what our objective is. What are we trying to accomplish with this analysis? With that, we can decide what source data we need to accomplish that goal. Then we need to prepare the data to be used in suitability analysis. We created buffers if needed (i.e. within 1500 feet of streams or roads), and reclassified the data into a Boolean grid. Once this is done, we combined the criteria using the Union tool (for vector), and we were able to determine which areas met all the criteria for our suitability analysis.

The raster-based procedure isn't too different.We again need to gather our source data and reclassify the data into a Boolean grid. One difference is that instead of the Buffer tool used in the vector-based method, to get distances in the raster-based method we needed to use the Euclidean Distance tool, which I had not used before, but was rather simple to use. The distance from this tool is the shortest distance from the center of that cell to the closest source. To obtain a final output, we need to combine the Boolean rasters. The method used in this lab was to use the Raster Calculator, which uses map algebra to obtain the desired results. It can be a bit tricky, but I really like this tool and hope to work some more with it as we progress forward.

The third part of this lab was really interesting. It involved using weighted overlay to rate suitable locations. We reclassified data (on a 1-5 scale) based on how suitable it was (1 being least suitable, 5 being the most suitable). I think the most important part of the weighted overlay method would be to use the same scale for all data; otherwise the results would make no sense. Once the data was reclassified, we used the Weighted Overlay tool and added the 5 criteria. This tool is where we can change how much importance each criteria has. The first output that is on my map is the output where all 5 criteria are treated as equally important; all are set at 20%. The second output on the map is the output where the slope is treated as most important (40%), land cover and soils are treated the same as the first output (20%), and the distance from streams and roads less important (10%).

When comparing the two, we notice that there are more suitable areas near rivers and roads in the alternative weights scenario, which makes sense as we are not prioritizing that criteria as much in this scenario. Additionally, there is a broader area of less suitable areas in locations with steeper slopes. This lab really shows the difference between equal weights and alternative weights scenarios, and it shows the versatility ArcMap has to solve some more complex problems and to assist with the decision-making process.