This is the first assignment in the Applications in GIS course, and covers suitability analysis. We worked with both vector and raster Boolean suitability modeling, as well as weighted overlay. For the Boolean modeling, we looked to determine the suitable habitat of mountain lions. Criteria set were forest cover, steep slopes, within 2500 feet of a stream, and 2500 feet away from a highway. We accomplished this with the layers as vectors, and with the layers as rasters:
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| Fig 1. Vector result of Boolean suitability modeling for mountain lion habitat. |
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| Fig 2. Raster result of Boolean suitability modeling for mountain lion habitat. |
We also performed a weighted overlay for a separate data set, with suitability based on low slope, agricultural land cover, soil type, more than 1000 feet from a stream and within 1320 feet of a highway. We then compared results of when the layers are equally weighted, as well as when the layers are weighted so that slope has highest weight, while distance from streams and highways of lower weights.
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| Fig 3. Comparison of weighted overlay results when criteria equally weighted vs. unequally weighted. For this analysis, no suitability value of 1 (lowest suitability) was calculated, and therefore is not present on the map. |
I feel like I learned a lot this week performing the analysis. First and foremost, I (re)learned how to to turn-on an extension in ArcMap. I had totally forgotten that this was necessary sometimes and had a slight freak-out, whoops. I did become quite familar with the Reclassify, Euclidean Distance, Raster Calculator and Weighted Overlay tools. The Euclidean Distance tool was used to evaluate the distance from rivers and roads, which was reclassified to the desired levels. The Raster Calculator was implemented to combine the rasters used in the analysis into one single raster (used in the mountain lion analysis). The Weighted Overlay was used to rank each cell's total suitability when each of the five criteria are combined as one. It is definitely interesting to see how the results change when you change the weight (importance) of the criteria. I can see how this type of analysis is highly important and can be utilized in a variety of situations.
