lab 5 - spacial analysis

Emilie Barnett

            This week’s lab focused on teaching us how to do various aspect of spatial analysis – which, as I understand it, is one of the most valuable functionalities of GIS. The project was to find the most suitable areas in a fictional county in Montana to build a new landfill. When you build a landfill you have to take multiple factors into account, such as the slope of the elevation (obviously you can’t build a land fill on a steep slope), soil drainage information, distance to streams and rivers, what kind of land it is, and distance to already existing landfills.

            Although the information we used in the lab is made up, this is a very real issue, as we can see in the article about the Kettleman City landfill. Kettleman is possibly being affected by its proximity to California’s largest toxic landfill with increased rates in birth defects, which is serious.

            Anyways, the general idea of what we were doing in the lab, was to perform whatever analysis we wanted (slope, buffers, etc), and then reclassify the results to have only 5 classes, with each class assigned a weight. A one means that all the land that falls into that area is highly unsuitable and 5 means that it is highly suitable. The final analysis was to add up all the individual criteria using their classes and get one last map that shows the same thing (high numbers = good, low numbers = bad) but for all factors involved. Therefore an area with a really high number, like 23 (the highest possible), meant that it was over four kilometers from a stream, an appropriate distance from open landfills, on good land for building landfills, that is also flat, and it has good drainage qualities.

            My final map is weighted according to some fake numbers supplied by the tutorial. This means that instead of just adding up each category, you multiply each one by a decimal that is greater than 0 and less than 1. All the decimals have to add up to one. So for example, my final calculation looked like this: ([Reclass2 of Coverclass] * .3) + ([reclass of sl_dist] *.3) + ([reclass of slope of elev] *.2) + ([Reclass of soildrain] *.1) + ([Reclass of Stream Buffers] *.1))*5, instead of like this: [Reclass2 of Coverclass] + [reclass of sl_dist] + [reclass of slope of elev] + [Reclass of soildrain] + [Reclass of Stream Buffers]. This is just a way to give different categories different weights of importance, because in real life you might care that it is an appropriate distance from a water source a little more than you care if the land it is on is perfectly flat, so that a situation similar to that in Kettleman city doesn’t occur.

            In all of the maps I am displaying, the darker the color, the more suitable that area is, so in the final map you can easily tell which areas correspond to the best land.

            This lab was pretty cool and interesting, because we are learning how to do analysis that is extremely valuable and applicable to real life situations. This is what people who work in GIS actually have to do for their jobs.