Week 10: GIS Applications

From August 29 to September 2, 2009, one of the largest fires the Los Angeles County has seen in years claimed the lives of two firefighters, burned more than 100,000 acres and 80 residences, and in total threatened more than 12,500 homes (CNN, L.A. Times, KABC). While the 2009 Station Fire originated from a sudden, unpredictable and unnatural cause — the arsonist was never found, according to KABC — the fire was likely so severe and so wide in its scope because natural and human-made conditions were setting the stage for such a destructive fire. In this report I will use GIS overlay analysis and draw upon scientific literature to see what might have led to the immense severity of this fire and what we can do to minimize damage done to the area in future fires.

Here is a reference map showing the Los Angeles County and the perimeters of the Station Fire as it spread over time. The map also shows the boundaries of the areas that each Los Angeles County fire station is responsible for protecting in the case of a fire.

Here is a map of past fires preceding the Station Fire from 2000-2009 and also of the Los Angeles National Forest, which is shaded in green. Although the National Forest indeed only comprises the political boundaries of just one certain ecosystem or fuel source, because the 2009 Station Fire burned almost entirely within the confines of the National Forest, I believe it was suitable to analyze the fuel history of that forest to see what might have led to such a big Station Fire. As we can see in this map, fuel has been allowed to accumulate in the Los Angeles National Forest area in the years leading up to the Station Fire. The Los Angeles National Forest has not seen any significant fires really since 2002, and those fires covered at most just half the area that the Station Fire did. More importantly, almost no fires burned in the Station Fire area in the nine years leading up to the Fire, signaling that this area likely accumulated forest debris and, subsequently, the fuel to supply a disastrous fire like the one that occurred in 2009.

It is also interesting to note that the greatest number of fire stations are in the southeast part of the Los Angeles County, a huge part of the county that has not seen any large fires in ten years. This seems to support the idea that fire suppression efforts are concentrated especially in areas where there are large numbers of population, a phenomenon I will examine further in this report.

An example of fire suppression: using chemical fire retardants to halt the advance of fire. Source: Times of Israel.

One issue connected to this problem of fuel buildup is that of fire suppression. While we must obviously try to protect civilians and their property from being destroyed by any fire, fire suppression over time can have negative effects on Southern California's ecosystems and can even lead to worse future fires. This is because fires are crucial for maintaining Southern California's Mediterranean ecosystems. Southern California's naturally dry chaparral and other shrubs are adapted to fire and depend on low amounts of it to destroy old matter and make way for new growth. Otherwise, dead debris from these plants accumulate and lead to "a gradual increase in dead fuels... (which) is a major cause of increasing flammability" (Conard and Weise). Fire suppression not only involves technology that can be very harmful to the environment, such as tractors, explosives and chemical retardants, but it can contribute to this buildup of fuel by preventing fires from gradually clearing it over time (National Wildlife Coordinating Group, Backer, Jensen and McPherson).

The rest of this report attempts to explain the apparent trend between fire suppression efforts and the location of civilian populations.

Here is a map showing the relation between population and fire station boundaries. This map supports my hypothesis that fire station boundaries — and thus the distribution of fire suppression efforts — are set with resident populations as one of the main priorities. Right below the Station Fire perimeter is an extremely dense band of residents, and this band also coincides with a band of many fire station areas. Thus this map shows that the allocation of fire suppression efforts are determined primarily according to population.

This map shows how resident populations are distributed in the Station Fire area. From the map we can see that many people live right on the edge of the area where the Station Fire occurred, and even in the space that was consumed by the fire. It is important to note that the fire occurred almost entirely within the perimeter of the Los Angeles National Forest, and thus the Station Fire got all of its fuel from that forest. This creates an inherent problem for the people living in the Los Angeles National Forest area and also for the people living right on the border of the forest, since they were all in direct proximity to the Station Fire. Fire suppression efforts were understandably heavily directed towards protecting this border population, but repeated intense fire suppression leads to more fuel buildup and sets the stage for larger fires.

Therefore, because there are significant numbers of people living in this area where fuel buildup is most likely to occur due to the large number of trees and general biomass, it creates a recurring problem of fire suppression and fuel buildup. Fire suppression is likely to be more intense in these areas, preventing the forest from being cleared of debris that can intensify a wildfire.

Thus, after analyzing these maps, one can conclude that populations should be discouraged — and possibly even regulated — from living in or near the Los Angeles National Forest area and other areas where there are abundant fuel sources for fires. Having populations there will only lead to more fire suppression, more fuel buildup and more difficult fires for firefighters to deal with. If populations are relocated from that area, then small to moderate fires could be allowed to burn the fuel buildup there and thus create a healthier environment for both plants and people. There would also be a smaller need for more intense fire suppression, since people would be less immediately in danger of being touched by the fires.

Data sources

Los Angeles County enterprise GIS. http://egis3.lacounty.gov/eGIS/tag/shapefiles/.

UCLA's spatial data repository. http://gis.ats.ucla.edu//Mapshare/Default.cfm#.

Bibiliography

Backer, Dana, Sara Jensen and Guy McPherson. “Impacts of fire-suppression activities on natural communities.” Conservation Biology 18, No. 4 (2004): 937-945. Accessed March 18, 2013.

“'Angry fire' roars across 100,000 California acres.” CNN, August 31, 2009.  http://articles.cnn.com/2009-08-31/us/california.wildfires_1_mike-dietrich-firefighters-safety-incident-commander?_s=PM:US.

Conard, Susan and David Weise. “Management of fire regime, fuels, and fire effects in southern California chaparral: Lessons from the past and thoughts for the future.” National Agricultural Library: 342-350. Accessed March 18, 2013.

Friedman, Ron. “Firefighters gain control over Carmel blaze.” The Times of Israel, March 8, 2013. Accessed March 19, 2013. http://www.timesofisrael.com/firefighters-gain-control-over-carmel-blaze/.

Garrison, Jessica, Alexandra Zavis and Joe Mozingo. “Station fire claims 18 homes and two firefighters.” Los Angeles Times, August 31, 2009. Accessed March 18, 2013. http://articles.latimes.com/2009/aug/31/local/me-fire31.

Hernandez, Miriam. “Thursday marks Station Fire 1-year anniversary.” KABC, August 26, 2010. Accessed March 18, 2013.

Wildland Fire Suppression Tactics Reference Guide (National Wildfire Coordinating Group, 1996).

Week 9: Cartography

For this week, I created three maps based on data from the 2000 U.S. census.

Here is a map showing the Asian population percentages for each county. Unfortunately, with all three of my maps, some county polygons failed to calculate properly when I used the GIS calculator to find the population percentages. So, on all three of my maps, there are certain polygons that are left white and blank because the calculations did not go through. I was unable to fix the problem in GIS or locate the attributes that were not calculating properly. Out of all the three maps I made for this lab, it appears on this map that the Asian population seems to have the most varied locations where its population percentage is greatest. While the most concentrated Asian populations are on the west coast in California, some higher percentages are also scattered in various spots on both coasts, and some are scattered in the midwest.

Here is a map showing the black population in the U.S. It is obvious that the population is more concentrated in the South: out of all the three maps I made, this map has the clearest trend. I like how the color scheme worked out for this map's symbology: using a color scale that uses light shades to denote smaller percentages to heavy or bright shades of red for the larger percentages helps viewers more clearly see the large concentrations of the black population in the South.

Here is a map of other population percentages, namely populations that are not any of the U.S.'s major racial or ethnic groups, such as the Caucasian, black or Asian populations. I tried a new kind of  color scheme for the symbology on this map: this time, the darker blue colors indicate a higher percentage, while anywhere that is green will have low percentages from 0 to 2.31 percent. I thought this two-color scheme was appropriate to use for this map, since the range percentages that the green color represents are all pretty much the same, because they are so small. Thus it would not be a poor choice to use one color shade to represent these multiple percentage ranges. This color scheme allows the viewer to see more starkly and clearly where the variation in racial groups are, which are mostly in California and in the southwest. Also, the darker blue shades help denote where the diversity is even greater in these areas. However, I can see how this color scheme could lead to pitfalls, since it might lead the map viewer to make generalized assumptions about the area covered only by the same shade of green.

Considering the actual data involved in the making of these three maps, I'd say the third map of the "other" racial population percentages surprised me the most. I did expect the most diversity in California, but I did not expect as much diversity in Texas, New Mexico and even Colorado. It was unfortunate that a handful of counties were unable to be calculated in each map. But after completing three other exercises using GIS, these maps were not difficult for me to create. The only way I think these maps could be misleading is the fact that some counties are larger than others. Thus, the larger counties that take up more space on the map lead to a greater generalization of that area, while the other smaller counties are more accurate because they attribute a statistic to a smaller area. There could be any amount of variation within the larger counties that are masked because the entire county is covered by one color.

I have more respect for GIS than I did when I first started this course. I used to think it was too difficult for any common, non-GIS-trained person to use, but now I see that as long as somebody gets an initial introduction to how to use ArcMap, they can easily start making maps on their own. It especially helps that, when you place your cursor over each command button in ArcMap, it gives you a description of what that command does. I do wish I knew if ArcMap had more spatial analysis capabilities, since the only ones we had time to learn about in lab were overlay analysis and some raster analysis functions. But overall, GIS is very helpful in going beyond the capabilities of traditional print maps to portray information. It is much more flexible and allows the user to manipulate data using different geographic coordinate systems with precision, helping to eliminate the chance of human error. GIS is in some ways limited, however, whenever there are errors or glitches in the ArcMap program.

Week 8: DEMS in ArcGIS

Here are four maps of the same section of the White Mountain National Forest, a federal forest located in New Hampshire. The area I chose is located between 71.732 degrees and 71.137 degrees west and 44.02 and 44.296 degrees north. The geographic coordinate system used in these maps is the 1983 North American coordinate system. Included are shaded relief, slope, aspect and 3D maps of the White Mountain National Forest. I created these maps using data of digital elevation models from the U.S. Geological Survey's National Map Viewer.

3D

Week 7: Map Projections

For this lab exercise, I created six different map projections of the world, and each gave dramatically different distances for the line between Kabul, Afghanistan and Washington, D.C. The standard true distance is 6,934.48 miles, but the fact that each projection had different distances from this shows that map projections cannot be trusted too easily and whoever uses them must be wary of the kind of distortion involved. It seems peculiar to me how each projection can really only be used for a specific, narrow purpose, since each projection differs in where and how it decides to allow distortion. This means that whoever references a map must be extremely careful in identifying the map's projection and must be aware of where and how the distortion is distributed to avoid making drastic errors in measurements.

 

For the equidistant map projections, I chose the Azimuthal since it is used in the United Nations logo, as well as the equidistant cylindrical. It is true that the equidistant projections are perhaps better for maintaining scale throughout the map, but evidently by looking at the Azimuthal projection and the equidistant cylindrical projection, even they are not the same in terms of distance. For example, the distance between Kabul and Washington, D.C. is three thousand more miles in the Azimuthal projection. Thus it is a disadvantage inherent in the map that distance is only true in relation to the center point, but not to all other points. Also, there is more and more distortion of shape and area going along the latitude lines as you move along the map away from the center of the projection. For example, in the Azimuthal projection, Australia and nearby Pacific Islands are stretched far beyond their original shape because they are far from the center point of the map.

The equal area map projections are best for showing the actual size of the land masses. I personally prefer equal area map projections most because they show how large each land mass is in relation to the other land masses, and thus show how things really are in relation to each other. I prefer the Mollewide projection in particular because it also preserves distance and shape relatively well, and it visually resembles the compromise and trusted Robinson projection. I admire how the Bonne map has the distance between Kabul and Washington, D.C. that is closest to the standard distance, but it has a downside in that it severely distorts the shape of the land masses to the point where even some of them are hard to see, such as Australia.

It was interesting but not surprising that the conformal map projections had the distances between Kabul and Washington, D.C. that was most different from the standard distance. Conformal projections probably have the most distortion out of all the map projection types because they do not attempt to preserve any kind of true measurement. It is understandable why elementary schools like to use conformal projections, like the Mercator, to teach children about the world and what it looks like, but evidently we can see that such projections dramatically distort size. It seems though as if the Mercator projection is preferable over the stereographic projection, however, if you care more about equal area, since the stereographic projection severely distorts the size of all land that is far away from the center point of the map: namely, everything besides Africa and the Middle East. The Mercator is more preferable because it distorts while moving away from a line (the equator) rather than a single point, therefore having severe distortion over a smaller area.

It is lucky, however, that most maps are of a larger scale than world maps and cover less area, thus minimizing the amount of distortion involved. It is mostly only when dealing with world maps do we encounter distortion to worry about.

Week 4: GIS Data Models

Click here if image is too blurry.

This was the first time I had ever used a software like ArcGIS, and it was extremely helpful to have a thorough and step-by-step explanation on how to use it to create these maps. It was a bit difficult to figure out the glitch that occurred when joining tables to create the population density map, but after using a new copy of the database it worked out eventually.

One pitfall of GIS is that it is not user-friendly for the general public. Even with the step-by-step tutorial, I still did not fully understand exactly what I was doing with ArcGIS for every step. I would not be able to navigate all the commands and make a good map on my own without a tutorial. This software is extremely complex and while it may be a good tool for GIS experts and researchers, it is maybe not easy enough for general users to take advantage of or learn from.

Another pitfall or difficulty that seems to be inherent with GIS is figuring out how to form the data to be imported into software such as ArcGIS in the first place. It was very useful and interesting to make these maps with ArcGIS, but it still took much extra measurement or data processing to make the database for this project in the first place. I feel like I don't have the ability or detailed knowledge to actually make a database on my own, thus I wouldn't really know how to make a map unless I have somebody give me the data first. Thus ArcGIS seems to be limited by who has the extensive and complicated knowledge to make these maps.

Despite this, however, GIS is still an invaluable tool for spatial analysis. The map layering is especially helpful because it can connect different data to help users make informed decisions, such as how much area the noise from a proposed airport expansion would affect. GIS helps users make sure this map layering is precise and accurate and it allows users to add their own variations in the data to see how things might be if they changed certain variables.

Week 3 Lab: Digital Mapping

How to cruise the Central Coast: an interactive digital map

View How to cruise the Central Coast in a larger map

I admire neogeography's ease of use and how it is accessible to anyone with a computer. I only had a few technical difficulties in making this map, mainly with drawing the route efficiently and accurately. It took time to draw the line route that adheres to already existing routes, roads or freeways on the map, since it was difficult to tell the computer which exact route you wanted your own line to follow. It was also a bit difficult figuring how to properly embed or insert photos and videos into the map markers. But other than that it was easy to use and it can definitely help people share information with their peers in a visually attractive way.

However, from this experience with neogeography it seems as if the kind and extent of information you can use or present with neogeography is very limited. It seems as if - at least with GoogleMaps - all you do is make your own routes and then add place markers for places you find interesting. You cannot really do much analysis with neogeography, since it only allows you to create one-layer maps and only shows you where to go or where things are. This kind of neogeography seems more appropriate for social networking purposes or educating about place rather than doing spatial analysis, as you could with GIS. Thus the limits of neogeography seem to lie in the fact that it deals with more superficial information.

Week 2 Lab: Topographic Maps

1. This is a map of the Beverly Hills quadrangle.
2. The adjoining quadrangles are Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice and Inglewood.
3. The quadrangle was created in 1995.
4. The national geodetic vertical datum of 1929 was used to make the map.
5. The scale is 1:24,000.
6. At the above scale:
1. 5 centimeters x 24,000 / 100 = 1,200 meters
2. 5 inches x 24,000 / 63,360 inches (1 mile) = 1.894 miles
3. 1 mile = 63,360 inches / 24,000  = 2.64 inches
4. 3 km = 300,000 cm / 24,000 = 12.5 cm
7. Contour interval = 20 feet
8. Locations of:
1. Public Affairs
1. 34 degrees, 4 minutes and 30 seconds north, 118 degrees 26 minutes and 15 seconds west
2. 34.075 degrees north, 118.44 degrees west
2. Santa Monica Pier
1. 34 degrees, 0 minutes and 36 seconds; 118 degrees, 30 minutes and 0 seconds west
2. 34.028 degrees north, 118.5 degrees west
3. Upper Franklin Canyon Reservoir
1. 34 degrees, 7 minutes and 0 seconds north; 118 degrees, 24 minutes and 30 seconds west
2. 34.1167 degrees north; 118.408 degrees west
9.  Elevations of:
1. Greystone Mansion:
1. 560 feet
2. 170.688 meters
2. Woodlawn Cemetery
1. 140 feet
2. 42.67 meters
3. Crestwood Hills Park
1. 750 feet
2. 228.6 meters
10.  UTM zone 11
11. 61.5 north (61,500 meters), 63 east (63,000 meters)
12. 1,000,000 meters
13. 
    

     14.  14 degrees, 48 minutes and 0 seconds

     15.  It flows from north to south.

     16.  Graphic of UCLA: