Lab #8: Mapping the Station Fire

(sources: http://egis3.lacounty.gov/eGIS/, http://gis.ats.ucla.edu//Mapshare, http://frap.cdf.ca.gov/data/frapgisdata/select.asp)

On August 29th in 2009 a wildfire known as the California Station Fire burned more than 100,000 arces of land. (http://www.cnn.com) The map above serves as a reference map for the wildfire and specifically outlines the extent of the burned land over time. This is shown with the colors displayed in the legend, using dark red to represent the source or beginning of the fire, and tan to represent the final extents of the fire damage. Since this fire became one of the largest fires California had seen in more than 60 years (http://www.cnn.com) we wish to use geography and GIS to outline possible reasons for its rapid spread and what can be done to prevent such large amounts of damage and destruction for future wildfires. One of the most important aspects of successfully fighting wildfires lies in the coordination and efforts of the local fire departments. For this reason the following maps will analyze how the fire department coverage regions affected the spread of the Station Fire and what factors influenced their zoning.

(sources: http://egis3.lacounty.gov/eGIS/, http://gis.ats.ucla.edu//Mapshare)

The fire threat GIS data seen in the first map most likely has a large impact on where fire departments are located in the area. The fire threat data was made by the California Department of Forestry and Fire Protection and it would be useful to determine what factors contribute to the fire threat level of an area. In the map above, local schools are shown with a one mile buffer analysis around each one. Fire threat is potentially dependent on locations of public buildings and their density in relation to the land-use nearby. As shown on the map, schools are very close together near the south west area. The fire department coverage in this area is very large and not well defined unlike the northern areas. More analysis on this will be given later. The main objective of this map was to show how close the fire came to schools in the area and in some cases entered their buffer zones or encased the school entirely. Notice however that the fire spread very little to the south. These areas seem most well protected and suited to putting out fires.

(sources: http://egis3.lacounty.gov/eGIS/, http://gis.ats.ucla.edu//Mapshare, http://frap.cdf.ca.gov/data/frapgisdata/select.asp)

Assuming that schools were a factor in determining the fire threat level, the map above displays fire threat and school location for comparison. From the map we see that fire threat is indeed most significant in areas with a high density of schools. Notice the large blue area (low fire threat) south of the Station Fire. This land contains no schools and supports the theory that school location helps determine fire threat. Of course there are many other factors that are associated with school location, such as residential housing density. However we can still see that it is highly likely that fire department coverage zones are dependent on school locations as shown by the fire threat level. In this way the spreading of the Station Fire followed a path that led away from schools and into land that had poor or difficult to maintain fire coverage.

(sources: http://egis3.lacounty.gov/eGIS/, http://gis.ats.ucla.edu//Mapshare)

Now that fire department coverage has been linked to school location with a high probability, the area of each zone should be analyzed to determine how to improve fire fighting methods for future wildfires. The map above shows the relative area of each coverage zone. From the data we see that the Station Fire seemed to spread the farthest in large coverage zones (dark green areas). For these areas, only one fire department must take charge of all fires in the zone. For large-scale fires, such as the Station Fire, more than one fire department may work together to stop the spread. However first response fire fighters often determine how the fire spreads and how effectively it will be extinguished. Other smaller coverage regions seem to limit the spreading of the Station Fire much more effectively. Note that other factors might influence how well a coverage region might be protected, such as accessibility of roads and terrain. Even if this is the case, future wildfires might be extinguished more quickly if coverage areas were reduced by adding another fire department or redistributing the work load between local departments in that area.
(sources: http://egis3.lacounty.gov/eGIS/, http://gis.ats.ucla.edu//Mapshare)

For more understanding on how fire department zones are assigned we can look at the highway system around the Station Fire. In areas that had a high density of schools (shown on previous maps) we see that there are many highways for fast travel through the area. This could be a significant factor on how large scale fires are extinguished when extra fire departments are called in to help. As shown, the large region south west of the Station Fire has many highways around the area. However few highways exist north of the Station Fire’s starting location. This factor probably contributed to the large spread north of the fire’s source. The fire department responsible for the northern coverage area may not have had enough neighboring support as there was no easy access to the front lines of the fire. Each factor such as transportation and accessibility of coverage zones should be taken into account when determining how to prevent future wildfires.

All the maps and data given here provide insight on how the Station Fire spread and eventually became so large. From the analysis, there are a few suggestions that could possibly reduce the spread and threat of wildfires for this area in the future. Fire department coverage zones should be adjusted north of the Station Fire since this is where the fire spread most easily. Fire threat is low for this area due to few residential houses, but the smoke and pollution from a large fire should be reason enough to redesign the fire department coverage regions. Other solutions include making the area more accessible to other fire fighters by building more highways in the area. However this would be very expensive and would not be an efficient use of fire protection funds. To make the best decision, the project could be expanded to include many more variables on why the Station Fire spread as far as it did. In this way GIS can help to significantly improve the fire fighting methods of Los Angeles and to control wildfires more efficiently in the future.

Works Cited

“All Station Fire Perimeters (as of September 2, 07:02) – Complete set.” http://egis3.lacounty.gov/eGIS/. Mark Greninger, 2 Sep. 2009. Web. 2 June 2011.

“ ‘Angry fire’ roars across 100,000 California acres.” http://www.cnn.com. CNN, 31 Aug.
2009. Web. 7 June 2011.

“Fire Station Jurisdiction Areas.” http://egis3.lacounty.gov/eGIS/. Alfie Blanch, 2011. Web. 2
June 2011.

“Fire Threat.” http://frap.cdf.ca.gov/data/frapgisdata/select.asp. California Department of
Forestry and Fire Protection, 2004. Web. 2 June 2011.

“Los Angeles County.” http://gis.ats.ucla.edu//Mapshare. ESRI, 1 April 2008. Web. 2 June
2011.

“Los Angeles County Highways.” http://gis.ats.ucla.edu//Mapshare. ESRI, 1 April 2008. Web.
2 June 2011.

“U.S. Geographic Names Information System Schools for Los Angeles County.”
http://gis.ats.ucla.edu//Mapshare. ESRI, 1 Oct. 2006. Web. 2 June 2011.

Lab #7: Census 2000

For this lab I created three maps based on the 2000 United States census. Each map shows the distribution of a specific race across all the major counties in the United States. The first map gives the percentage of Black Americans in each county. By trying out different distributions for the coloring scheme I was able to get a nice, even spread over the country. Notice however that the darkest color (dark blue in this case) covers a large percentage interval relative to the other colors. This happens because the distribution tried to spread each color over an equal number of counties. From the map we see that the black population is more dense in the south east part of the United States and least dense in the northern areas, especially in the central regions.

The second map shows the Asian American population percentages for each county in the United States. The distribution is more evenly spread than the black population, but seems to have higher percentages around the coastal regions of the country. Note that the color gradient was accidentally reversed for this map so that darker colors represent lower percentages and lighter colors represent higher percentages. It is conventional to use dark colors for higher values and light color for low values.

The third map simply shows the percentage of other races alone according to the 2000 census. This distribution does not include large races like Caucasian or Hispanic. We see that the percentages are much higher on the western areas of the United States, especially in southern states like Texas. A variety of minority races was used to create this distribution.

Mapping the 2000 census statistics provided useful maps for understanding how certain races tend to live in specific areas of the United States. There are a variety of potential reasons for this and GIS can help support theories that geographers might be exploring. There are also trade-offs when making these maps. One of the most significant trade-offs dealt with the percentage intervals for the color mapping. To make the map appear most evenly distributed it was necessary to map the regions with uneven intervals. Specifically the darkest color usually had a relatively large interval compared to the other colors. This makes the map less informative and biases the information. If the intervals were evenly spread, the aesthetics of the maps would then be hurt. In this way, a good geographic researcher must decide on how to balance useful information with map aesthetics.

Lab #6: DEMs in ArcGIS

In this lab I created four different maps using a digital elevation model (DEM) for a small area outside of Los Angeles. The area was taken from the DEM "ned_03854530" and the extent was from 34.34° and 34.27° top to bottom, -119.23° and -119.17° left to right. The geographic coordinate system used was GCS North American 1983. Using the DEM, I created a shaded relief, slope, aspect, and a 3D map shown above.

Lab #5: Projections in ArcGIS

In this lab, I created six different projections in ArcGIS to explore the different properties of projection techniques and understand how it affects the mapped distance between two cities. The projections above each have at least one of the following properties: equal area, equidistant, or conformal. The equal area property ensures that all land masses have area equal to their true area on Earth. Equidistant instead preserves the distance over the map and will match the true distances on Earth. Conformal maps will preserve the shape of all land masses and should match their true shape on Earth. It is not necessary for each map to have more than one of these properties, but some will have multiple.

The first two maps shown are the equal area projections. I used the cylindrical and Bonne projections to create these maps. Equal area projections are most useful for geographic studies that rely on the area of a certain land mass or body of water. If the study is focused on finding the area of different lakes, then this type of projection would be most effective for the project. Note that this type of projection can distort the shape of the Earth as shown in the Bonne projection. This non-conformal map might be inadequate for some types of studies. However the cylindrical projection preserves the shape and is conformal, giving it an advantage as it more closely represents the true Earth.

The second two maps present an equidistant projection of the Earth. I used the sinusoidal and conic projections to make these maps. They are most useful for measuring precise distances or mapping buffer zones around an object of interest. Note that both maps distort the shape and area of the continents. When measuring the distance between the two cities of interest, I found that their distances differed significantly. I believe the error was due to the path of measurement tool used to measure the cities. For the conic projection, the measurement path curved in an arc over the Earth's surface, crossing over the top of the Earth as if a plane was flying north east. On the other hand, the sinusoidal projection had the measurement use a more straight and direct path. I believe this caused the measurement to be shorter on the sinusoidal projection.

The last two map projections, the Mercator and Miller cylindrical, mapped the Earth while preserving the shape of all land masses and bodies of water. This type of projection has fewer uses for making quantitative measurements. However it is still important for making maps in which the user expects to see familiar shapes of states or countries. It may also be useful for navigation since a guide map should accurately represent the shape of the trail, road, or state. Note that in all of these projections, the differences in distance between the two cities is quite large. However in most cases, map projections are only used over a small portion of Earth and do not need to represent huge areas. Projecting a small area will make these differences much less obvious and errors will be less significant. Overall I have a better understanding of projections and how they can be used to make the most effective map for any specific project.