Lab 8

         This week's lab allowed me to implement GIS in my analysis of the LA station fire of 2009. I created the reference map out of a map of LA county. I then added a DEM that represented elevation as well as data about major highways and the fire perimeter at different times. My thematic map depicts the level of fire threat in the area, with the station fire being in an area with high fire threat. As can be seen from both maps, the fire started out small (yellow outline) and grew incredibly large (black outline). A number of different factors contributed to the huge growth of this fire.

         First of all, weather is a major factor in the spread of wildfires. During the time of the fire, the area was experiencing high temperatures, and very low humidity. This helped to create a very dry environment. Therefore, the grass and vegetation had little to no moisture content, thereby acting as an exceptional fuel source for the fire. Fires also spread faster if their fuel source is more dense or closer together, and this was the case in the Angeles National Forest. According to the USDA, the forest is densely packed with chaparral, pine, and fir. 

         Usually, wind is a big factor in the spread of wildfires, however it did not play a big role in the station fire. Moderate winds were present during the fire, but the driving force of the spread of the fire was elevation, primarily slope. Steepness of slope increases the spread of fires due to things such as heat rising due to convection, fire being closer to fuel sources, and wind currents pushing flames uphill. And, according to the DEM in the reference map, one can see that the wildfire is in an area of high, varying elevation. So, it is clear that slope and elevation played a major role in this fire. 

         The fire itself grew greatly and caused a great deal of damage. In only one night the fire was able to grow over 40,000 acres, doubling in size. In addition, over 12,000 homes were destroyed and over 6,000 people were evacuated. It was extremely difficult for firefighters to contain the fire due to the elements described above. The fact that wind was not a major force in this fire caught many firefighters   off-guard. Overall, it took 52 days for the fire to be fully contained, and costed roughly $95.2 million. 

         This lab really summed up arc GIS. It shows in which circumstances it can be useful and how it can be used to represent data. The thematic and reference maps create a great visualization of the station fire. From both maps you can see the extent of the fire in relation to the county of LA, and observe how much the fire perimeter grew. Also, you can see some of the other elements that contributed to the fire, particularly slope and elevation. All of the elements that contributed to the spread of this fire are represented in the thematic map of fire threat, and we can now understand why the fire area had such a high fire threat. This ability of arc GIS to take data and create a snapshot of a real-world phenomena is truly its biggest potential.     

Bibliography 

"About the Forest." Angeles National Forest. USDA Forest Service, n.d. Web. 13 Dec. 2012.

Bonsor, Kevin. "How Wildfires Work." HowStuffWorks. Discovery, n.d. Web. 13 Dec. 2012. <http://science.howstuffworks.com/nature/natural-disasters/wildfire.htm>.

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. 13 Dec. 2012. <http://articles.latimes.com/2009/aug/31/local/me-fire31>.

Lloyd, Jonathan. "52 Days Later, Station Fire Is Contained." NBC Southern California. NBCUniversal, 19 Oct. 2009. Web. 13 Dec. 2012.

"Ministry of Forests and Range, Wildfire Management Branch." Fire Behaviour. British Columbia Forest Service, Wildfire Management Branch, n.d. Web. 13 Dec. 2012.

Lab 7

         The first map shows the percent Black population in the United States. The data is broken up into five intervals, with the lighter colors corresponding to lower concentrations of black people and the darker colors corresponding to a higher concentrations of black people. According to the map, the area with the highest concentration of black people is the south, in such states as Alabama, Mississippi, Louisiana, Virginia, and more. In most other areas of the United States, the Black population is relatively low, as can be seen by the lights yellow shading in most of the country. However, urban areas usually show a spike in Black population.
         The second map is a map depicting the Asian population in the United States. The legend is also broken up into five intervals with the darkening blue shades representing larger Asian populations. From the map, the darkest areas are on the west coast, particularly in California and Washington. In those states there are several counties achieving the 21-47% Asian population bracket, the highest bracket. Overall, the Asian population seems to be larger in urban areas. In California, the darkest shades of blue occur in the Bay Area and Los Angeles areas and in Washington they occur in the Seattle region. Outside the west coast this still holds true, for there are pockets of higher Asian concentration in big cities, especially in the northeast, New York city region.
          The third map depicts the population of other races in the United States. Once again, the map legend is divided into five brackets with darker red meaning higher other race population. According to this map the areas of the United States with the highest population of other races are the west and the southwest. Some major states that seem to have particularly high other race populations are Texas, New Mexico, California, and Washington. Other than these areas of the U.S., most of the rest of the country falls in the first two brackets, meaning there is low other race populations. However, there still are spikes in other race populations in urban areas as can be seen in places like Florida, Illinois, and New York.
          This lab on census map series was fairly interesting. I found it interesting how I could take census data listed in excel and bring that census data to life with GIS. It truly does give a better representation of the data compared to when it was just listed in excel. I also liked how I could choose the number of breaks and percentage brackets to be able to represent the data in a specific way. In this way, I really have a lot of control when it comes to how the maps will be presented.
          Although I liked that fact that I have a lot of control in how the census maps were presented, I can see how being able to alter these features can also skew the meaning of data. By simply changing the number of breaks and percentages, a map could misinform and give false impressions to a population. Even so, this census map series was a good experience that enabled me to further explore GIS's capabilities. However, while creating these maps I did run into some errors, which followed me throughout the lab. And sometimes, these errors are difficult to pinpoint and fix. This is what I see as a potential downfall of GIS, that little mistakes here and there can really affect the outcome of your map.

Lab 6

 

           The area I chose to complete this week's lab was an aeriel view of part of Mount Shasta. The area consisted mainly of raising elevations from almost all sides of the map and two main peaks near the center of the map, as depicted in blue in the hillshade image. The part of the mountain I chose is fairly high in elevation. According to the hillshade image the highest point is roughly 4,300 ft high while the lowest point is roughly 2,000 feet high. The 3-D image of the mountain gives a great representation of the high elevation and the peaks present in the area. The extent information of the area I selected listed from top to bottom is 41.4211111107 and 41.3758333329. From left to right respectively, the extent information is -122.261111112 and -122.167777779. For spatial reference, the datum used in the area I chose is the North American datum of 1983.

 

 

 

Lab 5

 

        This week's lab revolved around projecting the Earth on a 2-D layout. Three main types of map projections are equal-area, equidistant, and conformal. Since the Earth is spherical and 3-D in shape, it is impossible to create a perfect 2-D projection of it; distortions are sure to occur. However, different map projections have varying degrees and types of distortions. Despite this, map projections are very significant in that they allow us to have a convenient representation of the world that is portable and easier to view. The map projection one chooses to use depends on what the map's purpose will be for. 

        The first picture above is of a type of map projection know as equal-area projections. This map projection preserves area relationships of the world. This is good because when one looks at an equal-area map, the sizes of everything in relation to each other are the way they are in real life. In this sense, it is a very accurate map projection. However, this comes at the expense of distorted distances and shapes as you move outwards from the center of the map. 

        The third picture depicts conformal map projections. These map projections maintain angles and local shapes. Because of this, conformal maps are useful in things such as navigation. However, preserving shapes and angles come at the expense of distorted relative areas and distances. For example, in the Mercator map, it appears as though Greenland is almost bigger than Africa, which is definitely not the case in real life. The same goes for the Stereographic map, which shows Australia as disporportionatley large.  

         The second picture is of equidistant map projections. Equidistant map projections are sort of a middle ground between conformal and equal-area projections. Here, distances between places are preserved, making it very useful in areas where accurate distances are needed. However, because of preserved distances, both area and shape are a bit distorted in this projection. This can be seen in the two-point equidistant map where North America seems to almost be lying on its side. Because of this, this map is not useful for things such as navigation. 

Lab 4

 

        My experience with ArcMap these past two weeks have been interesting, yet a little bit annoying. On one hand, the outcomes from using GIS correctly are very rewarding. You get to collect data and numbers and mash them up together to create an informative, visual representation of that data. I love the fact that you can layer data, which means you can switch between different layers by turning them on/off depending on what you want to visualize. There's no need for two or three maps, the layers make it so that you only need one. 

       On the other hand, GIS can get annoying. It is very technical by nature, so anyone attempting to use it without previous experience will most likely run in to some troubles. In fact, just trying to complete this project I realized that if you make a mistake it is difficult to go back and correct that mistake. In addition, it is even difficult to pinpoint where you made the mistake. The program is also very tedious, with lots of steps to complete a representation. All in all, GIS is a time-consuming and technical program. 

       Despite this pitfall, GIS has incredible potential to influence people. Just in this project I was able to see the population density and noise levels in a geographic region. Experts using GIS can use it to represent a huge variety of events and phenomenon. For example, it can be used to show rates of a certain disease in the United States of America. This brings the disease to the attention of the public in a stronger way than pure statistics would. When people can visualize things they are more affected by it. However, GIS's power to influence people is also one of its biggest pitfalls. GIS maps depend on data and statistics. So, if the data happens to be off, it could give the people a false representation and mislead them. 

       From this lab I learned both how rewarding and tedious GIS can be. Seeing data come to life in a visual representation is amazing, but it does require some expertise. And despite GIS's pitfalls of being tedious and possibly misleading, the pros do exceed the cons in my opinion.

Lab 3

View San Francisco Attractions in a larger map

Neogeography refers to maps used and created by regular people based on topics of their choice. Websites and online toolkits such as google maps have enabled common people to turn to neogeography easily. This has led to the creation of countless maps that range from a number of varying topics, all depending on the creator's intentions and interests. One could make a map listing their favorite restaurants in town or one detailing their European vacation. More often than not, these maps are interactive, including pictures, links, videos, or even sounds.
        Due to the unprofessional nature of neogeography, where ordinary people are capable of making their own maps, the information retrieved from such maps may not be entirely reliable. Ordinary people can add whatever they want to their maps and are not responsible for false information. Neogeography maps offer more opinions rather than hard facts. However, despite this pitfall, neogeography is a fun way to share one's views and thoughts in a simple, interactive, and personal way.

Lab 2

1. Beverly Hills Quadrangle
2. Adjacent quadrangles are Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, and Inglewood.
3. 1995
4. North American Datum of 1927
5. 1: 24,000
6a. 1 cm = 240 meters, 240*5 = 1200 meters
  b. 1 in = 2000 ft, 5*2000 = 10,000, 5280 = 1 mile, 10,000/5280 = 1.89 miles
  c. 1 mi = 5280 ft, 2000 ft = 1in, 5280/2000 = 2.64 inches
  d. 1 km = 1000 m, 240 m = 1 cm, 3(1000/240) = 12.5 centimeters
7. 20 feet
8a. (34° 04' 22"), (118° 26' 15")  or  (34.072778, 118.4375)
  b. (34° 00' 37"), (118° 30' 09")  or  (34.010278, 118.5025)
  c. (34° 07' 11"), (118° 24' 22")   or  (34.119722, 118.406111)
9a. 560 feet or 170.688 meters
  b. 140 feet or 42.672 meters
  c. 800 feet or 243.84 meters
10. Zone 11
11. 3,763,000 N and 3,761, 500 E
12. 1 in = 2000 ft, 2000 ft = 609.6 m, 609.6*609.6 = 371612.16 m sq.
13. Elevation points moving from west to east are: 340 ft, 600 ft, 620 ft, 640 ft, 520 ft, 520 ft, 440 ft, 380 ft, 340 ft, 295 ft, 250 ft, 190 ft, 155 ft, and 160 ft.

Campus elevation points at 366,000 E and 520 feet and at 367,000 E and 440 feet

14. 14°
15. South
16.