2009 Station Fire

In 2009, California was ravaged by more than 63 wildfires. Of these, the largest and by far most damaging was the Station Fire, which began August 26, 2009 and burned huge tracts of the Angeles National forest in Los Angeles County. While the fire was mostly contained by September 19th, it burned on for nearly another month. With the help of some rain, the fire ultimately was contained October 16th, 2009 and the Station Fire officially ended (“Station Fire”). Total damages were relatively low, 209 buildings were destroyed, another 57 were damaged and 22 injuries were reported (“Station Fire Update Sept. 27, 2009”).

The suspected cause of the fire was arson and in the end its containment cost $93 million and took two firefighter’s lives (“Station Fire Update Sept. 15, 2009” and “Firefighters Honor 2 Comrades Killed in ‘Station Fire’”). The largest fire in modern Los Angeles history, and one of the largest ever in California, the 2009 Station Fire had a huge impact on the region (“Station Fire is the Largest in L.A. County’s Modern History”). I personally visited the burned area to participate in tree planting and restoration and it was clear that the damaged areas have a long road to recovery. In order to better understand this devastating fire, I used geospatial analysis to examine the fire.

Using data provided by Mark Greninger in Station Fire Perimeters, I was able to construct a reference map for the fire. To aid in the visualization, I also used National Elevation Dataset data from the USGS as well as U.S. Highways, U.S. Major Parks, and the California State (Generalized) .shp files from ESRI and the UCLA GIS Map Share. The reference map I created shows the spread of the fire over several days, from August 29 to September 2, 2009. The fire was large on the 29th, but it is notable for the dramatic expansion the 30th. The Station Fire continued to grow, albeit at a diminishing rate, through the 2nd, when the dataset ends. The fire spread throughout the Angeles National Forest, initially to the Northwest and Southeast. On August 30th, however, the fire rapidly shifted to the North, expanding greatly, and in the days following the expansion pushed outward in both East and West.

One of the features of this reference map that initially caught my attention was the direction of the spread of the fire. It seemed to be bound by the highways, spreading into the Angeles National. That made me wonder, did the fire ever impact populated areas, the nearby towns and cities? To investigate further, I decided to construct a thematic map overlaying the spread of the Station Fire onto a choropleth map of the population density of Los Angeles County by census block. My hypothesis was that the fire spread to the least populated regions, leaving the more densely populated areas unharmed.

When I completed the thematic map, it confirmed my hypothesis. The majority of the fire took in regions belong to the lowest class of population density. If the fire had spread South or Southwest, where the population was more concentrated, the damage and cost of containment, as well as the number of injuries, would likely have been much higher. Within the scope of this spatial analysis it is impossible to say what caused this fire spread pattern. One possibility is that firefighters intentionally prevented the fires from reaching populated areas, allowing it to spread into the forest. It would be interesting in the future if information about firefighting strategies and planning were made available, in order to provide more explanation as to why this pattern of spreading occurred. Whatever the cause, the key face that was revealed by the thematic map was that the fire burned only in sparsely populated areas and did not spread to the densely populated communities near by.

This lab was very interesting for me. I assembled these maps mostly by myself: while I gathered all the data online I was ultimately responsible for organizing, analyzing, visualizing and presenting the data. It was a relatively long process, as I made many mistakes along the way, but I now feel I have a clear understanding of how to perform simple spatial analysis using GIS. Spatial analysis is a powerful tool: maps and geospatial visualizations can unlock insights from seemingly inscrutable data. With this lab I had a chance to put everything I learned this quarter into use and hopefully provide some new and insightful analysis about the to the 2009 Station Fire.

Bibliography

Bloomekatz, Ari B. "Station Fire Is Largest in L.A. County's Modern History." L.A. Now. Los Angeles Times, 2 Sept. 2009. Web. 05 June 2012.

ESRI. California State (Generalized). UCLA GIS Map Share, 06 June 2010. SHP.

ESRI. U.S. Census Block Groups. UCLA GIS Map Share, 06 June 2010. SHP.

ESRI. US Highways. UCLA GIS Map Share, 30 June 2010. SHP.

ESRI. US Major Parks. UCLA GIS Map Share, 6 June 2010. SHP.

"Firefighters Honor 2 Comrades Killed in 'Station Fire'" www.ktla.com. KTLA, 4 Sept. 2009. Web. 05 June 2012.

Greninger, Mark. Station Fire Perimeters. Los Angeles County Enterprise GIS, 2 Sept. 2009. SHP.

"Station Fire." InciWeb. United States Forest Service, 11 Nov. 2009. Web. 05 June 2012.

"Station Fire Update Sept. 15, 2009." InciWeb. United States Forest Service, 15 Sept. 2009. Web. 05 June 2012.

"Station Fire Update Sept. 27, 2009." InciWeb. United States Forest Service, 27 Sept. 2009. Web. 05 June 2012.

USGS. National Elevation Dataset. USGS Seamless Data Warehouse, 28 Dec. 2010. ARCGRID.

Week 8: Census Maps

The first map I produced examines the distribution of African Americans throughout the United States by county. As the map shows, counties with a significant percentage african american are located primarily in the South, with the exception of isolated counties in the Midwest and North. This distribution is likely due to the historical presence of african americans in the South, first as slaves and later as inhabitants. Another interesting feature of this map is the relatively high concentrations of African Americans in certain counties, above 80% in some places. This is not seen in any other racial group outside of caucasian, neither Asian or “other races” ever reached above 50% concentration. This is a surprising and intriguing aspect of african american population dispersal within the United States.

The next map I made focused of the percentage of people of Asian decent by county in the United States. This map had quite a different distribution than African Americans, with populations primarily concentrated in the west coast, specifically California and Washington, with some outlying counties in urban centers in Texas and the East Coast. This data seems to intuitively make sense, as the west coast is much closer to Asia and immigrants would be likely to come through there. In addition, many immigrants come over to work in high tech industries, which are primarily based in the San Francisco-Bay Area. The fact that the highest proportion of Asians occurs in the South Bay, where silicon valley is located, seems to confirm this.

The final map showed “other race” population. The is population is primarily latino, but due to the phrasing of the census it is referred to as “some other race.” The population is distributed along the West Coast, with its highest concentrations in southern states. This makes sense, as most latino immigrants would be coming from Central and South American and would therefore enter the United States through the southern border. One unique feature of this population distribution is its presence in rural farming areas such as California’s central valley and eastern Washington. This is likely due to the presence of latino immigrants as farm workers in these ares. The one surprising aspect of this map, which I would like to look into more, is the high concentration of this population in central New Mexico, where neither of the populations I examined previously had been.

Overall this census map series was very interesting to produce. While I had some guesses about what the distributions of these populations would be, it was still informative to see the information displayed visually. The census is a very powerful tool due to the sheer volume of data it has. This data makes a wide variety of analyses possible, and GIS’s compatibility with databases makes it simple to create output such as the maps I made. The only adjustment I really had to make was deciding to map based on percentages instead of absolute numbers, to control for the differing population sizes of different counties. These maps are the tip of the information available for analysis from the census, and it is fortunate that the US government makes the data available for free. Overall, my experience with using GIS and census data to create these maps was a very informative and relatively simple one, and I look forward to visualizing and analyzing more data.

Week 7: Elevation and Raster

The region I selected for this lab is the San Francisco Bay Area in Northern California. San Francisco is at the center of the map at the very edge of the peninsula. The map also shows Napa and Sonoma counties to the North, Monterey at the very southern edge, and Sacramento in the North East. The decimal degree range of the area is from 36.527° to 38.829° North and -121.247° to -123.315° West. The Datum for these maps comes from the North American Datum of 1983. The following maps show elevation, slope and aspect value.

Finally, I made a 3d model of the elevation of the bay area.

Week 6: Map Projections

Map Projections show both the wide variety of uses of geographic data and the potential problems with analyzing that data. At first glance it seems odd that there would be such a wide discrepancy in the distance between Kabul and Washington DC along the maps. Although the great elliptic, which measures the root that would actually be traveled by a person on the earth, remains constant, the planar distance has a 4,000 mile variance, with an error of over 3,000 miles in some maps. It is surprising that map projection choice can have such a large effect.

However, when we look closer it begins to become clear why this happens. When going from a 3 dimensional globe to a 2 dimensional map, certain features will necessarily be lost in translation. Since we have a wide variety of uses for maps it begins to make sense. In some situations maintaining equal area will be important, and we will use the Eckert II, while in others shape will be most valuable and we will use a Stereographic projection. Since we were focusing on measuring distance in this lab, it is therefore unsurprising that some maps are better than others. Different tools are required for different situations, and the wide variety of projections makes it possible to take the same geographic data and use it to answer a wide variety of questions about various attributes.

However all this variety does have its drawbacks, however. The wide spread of results means that it is harder to trust that what you are measuring conforms to reality. Even with the equidistant map projections there was a large discrepancy in planar distance. With Equidistant Conic the planar distance from Washington DC to Kabul was 6,957 miles but with Plattee Carree it measured 10,098 miles. Since these maps are both supposed to be equidistant this result reinforces that every map projection will give slightly different results for planar measurement, making it hard to determine what the true value is.

For all the drawbacks of geographic projections, they are a valuable tool. While planar measurements may be inaccurate, the Great Elliptic distance is constant among all projections, and is a fairly reliable tool. In addition the different maps are helpful for visualization when examining different phenomenon. For example, if I was trying to look at the relative sizes of the United States and Brazil, the cylindrical equal area map would be very helpful while if I was trying to figure out the distance between two points I could use a Plattee Carree. Different map projections help us use a single geographic data set into a wide variety of maps and make it possible to visualize a wider variety of phenomena.

Larger Version of the Image available Here: http://www.flickr.com/photos/aroch/7219679272/in/photostream

Week 4 Lab: ArcGIS Intro

ArcMap and GIS in general are powerful tools, with great potential but also some large difficulties. One of the main strengths of GIS and ArcMap is the ease of editing. Using hand-drawn maps it is impossible to quickly reconfigure elements but with a click and drag on ArcMap one can move elements and even rearrange orders of layers. Drawing a parallel line is not a difficult and time consuming process, requiring geometry and a steady hand, but is as simple as checking the draw parallel box. GIS makes cartographic actions that would have once required extensive time and skill available to even amateur users.

One of the most fascinating applications of GIS, however, is spatial analysis. This is possible only on a computer and only with advanced software such as ArcMap, but it opens up whole new possibilities for geographic knowledge. With a few clicks I was able to produce a chart displaying the number of each type of parcels within the noise contour for the airport, valuable and relevant information that would have required a lot of time and energy to make without ArcMap. Since charts like the one I made are the ultimate reason why we are performing spatial analysis in the first place, to answer geographic questions, GIS and ArcMap’s ability to answer them directly and easily makes them an invaluable tool.

There are drawbacks to GIS and ArcMap however. One of the main strengths, the amount of manipulations possible, is also a drawback. There are so many tools oftentimes it can be difficult to figure out which one is best to use in a situation. I also had a slight interface problem with the zoom function. Many times it was hard to know if I was zooming in the workspace or the actual map, leading to problems I later had to fix. Finally, the computing requirements also present a large drawbacks for many users. While working in the computer labs I had no problems, but trying to use remote access on the internet slowed down the process considerably. In addition, people who don’t have access to relatively new, powerful computers will be unable to use GIS and software such as ArcMap to their full potential.

Even with these drawbacks, GIS and ArcMap are overall valuable tools which are being used to expand our geographic knowledge. Questions that would have before been time intensive to investigate are able to be easily done. Analysis is possible with built in wizards, and in a few clicks it is possible to create valuable info-graphics or whole new layers. This is best evidenced by this week’s lab, where with only 3 hours of time I was able to use the data to create a report that could be used by policy makers to decide if they want to build the airport extension. That is the ultimate purpose of GIS, and geography in general, to give us better ways to understand our world.

Week 3: A Running Route

Neogeography represents a new and exciting development for the field of geography but it does have its pitfalls. Neogeography is essentially the democratization of mapmaking, made possible by new technology. Before the widespread prevalence of powerful home computing, easy to use software and the internet, the creation and editing of Geographic Information Systems was limited to people who had both the training and hardware to use ARCGIS and similar programs. Now, however, anyone with access to an internet connection can use google maps to create and edit multiple layers of spatial data, effectively creating a GIS. One of the greatest advantages, and also largest challenges, of this neogeography is that it is open to everyone.

The new access to map making allows for a much wider variety of spatial and geographic data to be shared. Groups and individuals who would have never before thought to create GIS and maps now are able to with relative ease. The GIS they create is then available for anyone to access online, increasing the total knowledge available. This open access, however, is the main drawback of neogeography. When everyone can create maps and GIS anonymously, the pressure to have completely accurate results is lessened. In addition, since neogeography involves the participation of mainly amateurs it could lead to a higher rate of mistakes and deliberately false data, which might hurt researchers trying to analyze the data. However, for the most part this democratization of geographic knowledge has been a plus. Neogeography is part of a larger trend where previously hard to attain skill and knowledge is made accessible to the general public through technology. Hopefully, like other previously esoteric skills such as digital photography, filmmaking and music producing, it will make what was once expensive and hard to access easily usable. This will encourage members of the general public to get involved, leading to a wide variety of data rich maps. While it is still to early to speculate where neogeography will lead, I believe one of the ultimate end points could be a worldwide system of geographic information, where previously individual geographic knowledge and information is available for access by all. Neogeography, if it can overcome the problem of accuracy, will be able to bring maps and geography into the 21st Century and give entirely new groups of users access to a wide variety of geographic information.

View Holmby Park Run in a larger map

Week 2 Lab: Exploring the Beverly Hills Quadrangle

Lab Answers.

1. The name of the quadrangle is the Beverly Hills Quadrangle.
2. The adjacent quadrangles are Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice and Inglewood.
3. The topography for this quadrangle was initially compiled in 1966.
4. The North American Datum of 1927 and 1983 as well as the National Geodetic Data of 1929 were used to create this map.
5. The scale of the map is 1:24,000.
1. 5 centimeters on the map is 1,200 meters on the ground.
2. 5 inches on the map is equivalent to 1.894 miles on the ground.
3. 1 mile on the ground is 2.64 inches on the map.
4. 3 kilometers on the ground is 12.5 centimeters on the map.
7. The Contour interval is 20 feet.
1. The Public Affairs Building is at 34° 4’ 22” N Latitude and -118° 26’ 24” W Longitude (37.073°, -118.44°).
2. The tip of the Santa Monica Pier is at 34° 0’ 25” N Latitude and -118 ° 29’ 56” W Longitude (34.007°, -118.499°).
3. The upper edge of the Franklin Reservoir is at 34° 6’ 11” N Latitude and -118° 24’ 49” W Longitude. (34.103°, -118.414°).
9.  
1. Greystone Mansion is at 580 feet and 177 meters elevation.
2. Woodlawn Cemetery is at 140 feet and 43 meters elevation.
3. Crestwood Hills Park is at approximately 720 feet and 222 meters elevation
10. This map is in UTM zone 11.
11. ³⁷63⁰⁰⁰ meters North and ³62⁰⁰⁰ meters East are the UTM coordinates for the lower left corner of the map.
12. Each cell contains 1,000,000 m², or 1 km².
13. 
    
14. The magnetic declination of the map is 14°.
15. The stream between the 405 and Stone Canyon Reservoir flows south.
16. 
    
    

Map #3: Federal Land Ownership in the United States

The last map I selected was created by David M. Kennedy, Professor of History at Stanford University. It shows the percentage of land owned in each state both numerically and with a smaller, red image image of the state within the actual state. What I find interesting about this map is the heavily skewed distribution. Colorado and the Rocky mountains seem to form a dividing line as far as government land ownership is concerned. While you can read about the differences in historical development and federal involvement in states, it is most striking to see the difference between the eastern and western United States in map form. I thought it was also cool how they included a smaller red image of states indicating the amount of land owned by the United States government, it makes clear just how much area 40% or more can be.

Map #2: Cartogram of the 2008 US Presidential Election

The next map I chose was created by Mark Newman, a Professor of Physics at the Center for the Study of Complex Systems at the University of Michigan. It is an area cartogram which assigns the size of each county based not on physical area, but on population. The counties are then labeled red or blue to indicate how they voted in the 2008 presidential election, blue for Barack Obama and red for John McCain. What I liked about this map was how it dispelled traditional notions about political parties in the United States. People often talk of a conservative, republican “heartland” of the country bordered by the liberal, democratic coasts. However, this map shows the large pockets of democrats within the supposedly conservative center, as well as conservatives in the coastal regions. I thought it was a very interesting way to better display the political beliefs of the actual population, as opposed to counties or states.

Map #1: Losses of the French Army in the Russian Campaign 1812-1813

This map, probably one of the most famous in existence, was created in 1869 by Charles Minard, a French civil engineer who also created a wide variety of maps. This one shows the disastrous result of Napoleon’s 1812-1813 Russian campaign. What I really like about this map is the wide variety of information it conveys. The colors show the direction of the army (brown invading, black retreating) overlaid on a map of Russia. In addition, the width of the line shows the size of the army, and as losses increased over time the line thinned. Numbers along the side of the line show the exact number of troops at certain points. Finally, the bottom of the map contains a temperature readout which shows the temperature at a variety of locations during the retreat. This map does an outstanding job of providing you with a variety of information that allows the viewer to   really understand what was happening as the campaign progressed: the army went deeper into Russia, losing massive amounts of men, and by the time they decided to retreat winter had come and even more men were lost. I found the way that this map told a story through the information it gave very cool, and that is why I chose it.