Lab 6 - Fire Hazard Analysis

In this lab we had to create a fire hazard map for the area around which the station fire broke out last year. The first step was to download all the necessary data from the internet: a data elevation model of the LA county area, land cover/fuel information, and the station fire perimeter. I got the DEM from the streamless website, the land cover info from the forestry website, and the fire perimeter from our old lab in gis 7. Once I had all that I could start with my analysis.
First i created a hillshade layer out of the DEM, not really because i needed it for the analysis, but because it makes the final result look better, and more visually clear. Then I created a slope model out of the DEM. The slope model is necessary, because the steepness of a slope affects how at-risk an area is for fires. The next step was to reclassify the slope categories, so that i didn't have a million unique classes confusing the picture. I decided that five was a good number since it matches with the number of classes for fuel cover.
Next i did a similar procedure on the fuel cover. I reclassified the data according to the standards provided in the tutorial. I looked at the metadata provided along with the land data, which explained what type of land cover each class corresponded to. then i referenced the tutorial to see what new classification they should get.
The final step was to do some raster addition to combine the two reclassified layers into one. The final slope/fuel layer is the one that is really important, because by adding up the data value of each pixel (according to what classes they were in the two base layers), you get a final set of class numbers that are easily identifiable as high or low risk. And when you add in the fire perimeter, you can easily see why the station fire broke out where it did: That area is extremely high risk!
Thus finally it was just a simple step of giving everything appropriate symbology and laying it out in an attractive, professional manner.

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.

Written stuff for post

Analysis part:

I agree somewhat with the city ordinace, because as my map shows, there are still plenty of dispensaries in LA outside the 1000 ft buffer around schools, parks and libraries. I think that people have the right to decide if they want their kids to be constantly exposed to weed. And it is true that weed dispensaries have been popping up all over the place. they are everywhere! For my project, i couldn't even geocode all of them in LA, i had to limit myself to 86!
The issue of weed is a very sensitive one, and very relevent to us here in California, the "weed capital" of the country. I personally don't see anything wrong with weed, and in fact believe that in general it is a lot safer than alcohol. However, there are many who are adamantly opposed to weed, as it is illegal, and think that it attracts criminals, etc. These people should be able to send their kids to the park and to school without worrying they will be overexposed.
However, perhaps 1000 ft is a little extreme? These weed dispensaries are very discreet (there are some in Westwood, and they usually have shades on the windows, and don't smell like weed from the outside), and the weed industry is a very profitable one for the state of California. therefore i would propose limiting the buffer to 500, so as not to force more dispensaries to close.

My map is a closeup of the area where the highest concentration of weed dispensaries are, west/downtown LA.

Lab 4 - Reference Map of Iraq