Lab8: Census 2000/2010

This is a map that conveys black population density distributions within the continental United States according to the U.S. Census of 2000. To demonstrate the difference between regions, I used a purple color ramp to show a variety of areas with different black populations. The darker the county, the greater the percent population blacks represented. Though quite evenly distributed, it is pretty apparent that areas in the southeast have greater percentages of black population distribution.
This map is a map that displays Asian population distribution within the continental United States. For this map, I used an orange color ramp to show the spectrum of population distribution. For the most part, the West coast has the greatest density of Asian populated counties and New England has a close second. On the other hand, states in the middle of the country are very sparsely populated by Asians. These characteristics are very realistic, considering the proximity to and immigration from Asian countries.

This last map is one that shows some other ethnicities as reported by the U.S. Census of 2000. For this map, I chose to use a green color ramp. "Some other" can be defined as any ethnicity that is not black, Asian, or non-Hispanic white. The map is greenest in the southwest region, signifying that the southwest is the area that is most heavy populated by "some other" ethnicities. In fact, the greenest states are the ones that lie on the border between the U.S. and Mexico.

This census map series was definitely the most interesting lab that we have done in this class. It was truly eye-opening regarding what made up the rest of the country and reminded me that not every county is as diverse as the ones that I have lived in: San Mateo and Los Angeles. It was also the first map that I have made without a step by step guide as well as the first map where using different intervals for the data was actually relevant. I had to play around with the intervals used to convey the data before I finally settled on one that I thought painted the picture well. It should be noted that while the intervals I used for each map are the same, they are not congruent; natural breaks in one set of data may not be the same in the others. Thus, it would be difficult to yield good objective data by comparing the three maps, since color intensity had a different scale on each map.

This quarter has been a real learning experience. Since I am but a freshman, Geography 7 was not only a class that represented my first delving into GIS, but my first journey into the college experience. Before this class, whenever I saw a thematic map that would require the kind of thinking, data acquisition and data manipulation techniques that I learned in this class, I did not think much of it. I would glance at it and move on quickly. Now, however, I know better. I know now how to gather information from it quickly as well as question its subjectivity, among other things. If I had to choose any one reason why I am glad that I took this class, it is because it showed me that the possibilities of GIS are absolutely endless.

Lab7: Mapping the Station Fire in ArcGIS

Last summer, over 251 square miles of national forest and residential homes were devastated by wildfire in northern Los Angeles. At the time of the inception of the blaze, I was still enjoying my summer vacation. By its end, I was well into the first quarter of my senior year in high school. The fire burned from the 26th of August until the 16th of October before it was successfully put out. This fire, one that would eventually by known as the Station Fire, went on record as the 10^th largest fire in the history of California.

As a resident of northern California, I was not directly impacted by the Station Fire and did not think much of it at first. However, since I had family living in the Glendale area as well as a sister attending UCSD, I was not unaffected. As we sat in the comfort of our own home watching the fire rage in news broadcasts, I learned that wind can have a substantial impact on how and where a wildfire burns. Additionally, the Station Fire was not a wind-driven fire, either. So if wind could influence a wildfire so significantly and the Station Fire was not wind-driven, I wondered, what other factors could influence the spread of a wildfire? I suspected that elevation could play a part in wildfire expansion. So, given the opportunity, I decided to investigate with ArcGIS.

I began my investigation by first plotting the 9 perimeters of the Station Fire. I then procured the Digital Elevation Model for Los Angeles County online and plotted that as the background to the progression of the fire. To reduce the amount of data thrown at the reader, I removed a few intermediate fire perimeters, increased their opacities and turned the DEM into a shaded relief map. To make more clear the direction the fire was heading in, I also plotted the areas of Los Angeles County that are considered “highly populated”. The fire began at the border between the Angeles National Park and a populated region and expanded in all directions but primarily northwards and to the sides. Strangely, on the eastern side, the fire took on a forked path.

What I found was a little disappointing for my investigation. The elevation of the Angeles National Forest as seen on my map was a little too variable to find any major correlation between it and the progression of the Station Fire, rendering my hypothesis inaccurate. There was no prominent connection between the steepness of the hills and the direction the flame expanded – despite the orientation of the hills, the fire grew as it was expected to, independent of elevation.

The Station Fire was 100% contained on the 16^th of October thanks to some rainfall, but its effects were irreversible. In addition to its profound impact on the ecology of the region, two firefighters lost their lives when their car drove off the road in an effort to escape the smoke. With the proper application of GIS (as well as prompt response from the U.S. Forest Service), however, the next fire that threatens Southern California can be contained before it rages out of control. Even though elevation was not a key factor in determining the direction the fire would take, there are virtually limitless factors that could be explored with programs like ArcGIS to limit the damage done by wildfires in the future.

Sources:

1. Gleeson, Gene. "Station Fire teaches fire department lessons." abclocalgo.com. Eyewitness News Los Angeles, 7 Jun. 2010. Web. 16 Nov. 2010.

2. Garrison, Jessica, Alexandra Zavis, and Joe Mozingo. “Station fire claims 18 homes and two firefighters.” Los Angeles Times. Los Angeles Times, 31 Aug. 2009. Web. 16 Nov. 2010.

3. Hall, Elizabeth. "Wildland Fire: Science of Wildfire." National Interagency Fire Center. Web. 16 Nov. 2010. http://www.nifc.gov/preved/comm_guide/wildfire/fire_4.html.

4. The National Map Seamless Server. Web. 16 Nov. 2010. http://seamless.usgs.gov/website/seamless/viewer.htm.

5. Mapshare. Web. 16 Nov. 2010. http://gis.ats.ucla.edu//Mapshare/Default.cfm.

Lab6: DEMs in ArcGIS

For this week's lab, I chose to do a DEM on the greater part of the Hawaiian island of Maui. I thought it would be an interesting choice because Hawaii is very well known for its prominent volcanic activity, a trait that I thought would lead to some interesting terrain. Additionally, I have never been to Hawaii. I was not mistaken - Maui made for a very interesting DEM. This extent goes from 20.9061111112 to 20.597222223 North and from -156.46111111 to -156.008055555 West, which falls under UTM zone 4. The geographic coordinate systems used include the North American GCS of 1983 (GCS_North_American_1983) as well as the North American Datum of 1983 (D_North_American_1983).