Lab 8: LA County Station Fire

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Campgrounds in LA County at Risk from the
Station Fire of August/September 2009

The infamous Station Fire occurred in Los Angeles County during late August and early September of 2009.  The fire was the biggest in LA County’s modern history and the tenth largest in the history of California, spreading to over 160,000 acres and destroying nearly one hundred homes.  Starting on August 26, the fire grew rapidly but for the most part without the help of strong winds, before finally being completely contained on October 16.  The cost of the wildfire is estimated at around $90 million, taking the lives of two firefighters in the process, and is now believed to be a case of arson. 

According to the set of perimeters we have spanning from August 29^th to September 2^nd, the Station Fire first appears as a concentrated almost circular region starting out in the San Gabriel Mountains.  It initially grew east and west, before rapidly spreading northwest in a rectangular offshoot, the whole fire shaped like a rough capital “L” early on the 30^th of August.  From there, it exploded east, more than doubling in size by the 31^st.  The fire continued to grow east and west during the first two days of September, turning into a rough sideways oval shape with two branches of fire pushing out further east, until finally looking almost like a lobster claw with the pincers facing east by September 2^nd.  

I have included a state map of California with Los Angeles County highlighted in yellow and the total affected area by September 2^nd in bright red to show the breadth of the fire on a state-wide scale, and give a better idea of where exactly the fire and the county are located.  Next to the state map is a reference map showing the spread of the fire in semi-transparent layers overlaid on top of the hillshade of the region to show how the fire grew in respect to the local topography.  It appears that the fire was contained on the southernmost side by the San Gabriel Mountains, as it could not climb over the tops of the mountains. Instead the fire spread horizontally to the east and west as well as north away from band of mountains.

My thematic map looks at the campgrounds in LA County that were at risk from the Station Fire.  Starting on August 29^th, there were only two campgrounds in the fires grasp, the Oakwilde Trail Camp and the Switzer Camp.  On the 30^th, twelve more campgrounds were inside the fire’s boundary, and fourteen more on the 31^st.  September 1^st and 2^nd saw ten and eight additional campgrounds within the fire’s range, bringing the total for the five days to 46 campsites at risk.  These campsites would have needed to be evacuated, as most of them were consumed and destroyed by the Station Fire.  The Angeles Crest Highway (part of California State Route 2) is the biggest highway near most of the campsites in question, and would have had to been utilized for evacuation purposes before the road itself was engulfed by the fire. 

The maps I have included in my report provide insight into the location and spread of the Station Fire, as well as its devastating effect on the campsites of the region.  In addition to the possible structural damage sustained by established campsites, the surrounding forests were incinerated and it will be years before the land recovers enough to support camping again.

Bibliography:

CNN. "Investigation under way into cause of Station fire." CNN U.S.. N.p., 1 Sept. 2009. Web. 9 June 2011. <http://articles.cnn.com/2009-09-01/us/california.wildfires_1_angeles-national-forest-fire-officials-fire-chief-mike-dietrich?_s=PM:US>.

InciWeb. "InciWeb the Incident Information System: Station Fire News Release." InciWeb the Incident Information System: Current Incidents. N.p., 27 Sept. 2009. Web. 9 June 2011. <http://inciweb.org/incident/article/9640/>.

InciWeb. "InciWeb the Incident Information System: Station Fire." InciWeb the Incident Information System: Current Incidents. N.p., 10 Nov. 2009. Web. 9 June 2011. <http://inciweb.org/incident/1856/>.

KTLA News. "Report: Number of Firefighters Reduced Before Station Fire." KTLA.com. N.p., 2 Oct. 2009. Web. 9 June 2011. <www.ktla.com/news/landing/ktla-angeles-fire,0,5292469.story>.

LA Times. "Station fire is largest in L.A. County's modern history." Los Angeles Times. N.p., 2 Sept. 2009. Web. 9 June 2011. <http://latimesblogs.latimes.com/lanow/2009/09/station-fire-is-largest-in-la-county-history.html>.

"Station Fire." California Department of Forestry and Fire Protection. N.p., 16 Oct. 2009. Web. 9 June 2011. <cdfdata.fire.ca.gov/incidents/incidents_details_info?incident_id=377>

Lab 7: Mapping Census 2000 with ArcGIS

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The first map shows counties in the contiguous United States color-coded by the number of people living in them in year 2000.  The darkest counties have the most people, while the lightest counties have the least.  These values are calculated by counting how many people live in each county; then the counties are sorted into ranges of numbers of people to be represented by different shades of color.  The gradient color ramp, from dark purple to light blue, is great for this kind of data because it easily shows at first glance where the counties with the most people are compares to the ones with the least.

The second map (top right) shows counties in the contiguous United States color-coded by the difference from 1990 to 2000 in the number of people per county.  The difference in number of people per county is calculated by taking the number of people in each county in year 2000 and subtracting the number of people in that same county ten years ago in 1990.  If the number is positive, it means there was a net growth in that county's population; conversely, if it is negative it means there was a net loss.  The color ramp, from bright pink to dark green, is good for this kind of data because it shows the contrast very well between counties with the biggest gains or losses, which we are more interested than counties that did not change very much.  The colors help to show a clear migration away from the middle of the country towards the coasts and big cities.

The third map (bottom left) shows counties in the contiguous United States color-coded by the percent change from 1990 to 2000 in the total population of each county.  The percent change in the total population of each county is calculated by subtracting the total population in 1990 from the total population in 2000 in each county, then dividing that sum by the total population in 2000 in each county, and finally multiplying the resulting number by 100 to get your percent change.  The selected color ramp, from orange to purple but with most of the ranges a shade of purple, is great for this kind of data because the states with a negative percent change really stick out and there are far fewer counties that experienced a loss than a gain in population, so the gains are all in shades of purple while the negative percentages are orange.

The fourth and last map shows counties in the contiguous United States color-coded by their population densities in year 2000.  The population density is calculated by dividing the total population in each county in year 2000 by the total area of each county (in square miles).  This calculation gives you the population density in people per square mile for each county.  The selected color ramp from almost white to green to dark navy blue is ideal for this data because the lighter colors naturally indicate space and openness whereas the dark colors are more condensed and represent the counties with the highest population densities.

Lab 6: DEMs in ArcGIS

Shaded Relief Model

 Slope Map

 Aspect Map

3D Image (Angle 1)

 

 3D Image (Angle 2)

 

The area I selected contains the mountains that make up Killington Ski Resort in Killington, Vermont and some of the surrounding terrain.  Growing up in the Boston area, I spent many weekends of my youth skiing at Killington, so the area was particularly interesting for me to look at.  The extent information (in decimal degrees) is:

Top: 43.9711111109

Left: -73.0333333299

Right: -72.481944441

Bottom: 43.5247222219

The geographic coordinate system used was "GCS North American 1983," and the area I selected was located in UTM Zone 18N from the 1983 North American Datum.

Lab 5: Projections in ArcGIS

GCS & Mercator Map Projection

The first depiction of the world shown is drawn from the geographic coordinate system.  GCS is different from a projected map because GCS is a coordinate system based on a three-dimensional model of the Earth not a projection system, so when GCS is put directly on a two-dimensional map distortion occurs.  The second map is a Mercator map projection, which is a cylindrical map projection and is conformal meaning that all angles are preserved but distances and areas are distorted.  In the Mercator map, the world looks like a vertical rectangle with land to the far south and north (e.g. Greenland, Antarctica) especially stretched out vertically.  The measured distance from Washington D.C. to Kabul is significantly longer on the Mercator map projection than on the GCS map, about 10,100mi compared to 7,000mi.

Cylindrical & Bonne Equal Area Map Projections

The cylindrical equal area map projection is a cylindrical map projection that makes the world look like a rectangle on a two-dimensional map.  Everything appears to be stretched out from east to west, and the measured distance from Washington D.C. to Kabul is longer than on the Bonne map projection below, about 10,100mi compared to 6,700mi.  The Bonne equal area map projection is a pseudoconical map projection that makes the world look heart-like in shape.  In both map projections, area is preserved but angles and distances are distorted.

Conic & Cylindrical Equidistant Map Projections

The equidistant conic map projection is a conic map projection in which distances along the meridians are preserved, but angles and areas are distorted.  The equidistant conic map projection makes the world look like a circle with a wedge removed (or a pizza with a slice removed) on a two-dimensional map.  All continents beside Antarctica look small and somewhat compressed, but Antarctica stretches all the way around the map projection in a strip.  The equidistant cylindrical map projection is a cylindrical map projection in which distances along the meridians are also preserved but angles and areas are distorted.  This map projection, however, makes the world look square-like in shape, reminiscent of the Mercator map projection in appearance.  The measured distance from Washington D.C. to Kabul is shorter on the equidistant cylindrical map projection than on the equidistant conic map projection, about 5,100mi compared to 7,000mi.

Lab 4: Getting started with ArcGIS

Tutorial Exercise 1:
In Exercise 1, the first step we did after learning how to start ArcMap and open an existing map document was to change the display symbol for "schools" from a dot to the more appropriate School 1 symbol.  This makes it easier for the experienced viewer to recognize right away where the schools are located on the map without having to check the legend.  Looking at the noise contour and the locations of schools close to the airport shows us which if any schools are at risk of experiencing too much noise from the airport.  We then added text to identify the one school within the noise contour as Northwestern Prep.  Finally, we added a title, legend, North arrow, and scale bar to further help with understanding and interpreting the map.

Tutorial Exercise 2:
In Exercise 2, we first created a new data frame with the parcels data.  We then ensured that both data frames were the same size before copying the layers for the noise contour and airport area from the Schools data frame to the parcels data frame.  Next we adjusted the symbols and color scheme of the parcels so that instead of all having the same symbols they had a different color for each land-use type.  We then made a table to show the total area of each land-use type and the number of parcels of each type within the noise contour.  We used this table to make a bar graph displaying the number of parcels of each land-use type.

Tutorial Exercise 3:
In Exercise 3, we created another new data frame to display the population density data for the county, which shows where people are located in the area.  To do this we first opened ArcCatalog and copied the layers for arterials, tracts, and airport_area to the new data frame.  Then we added the population data by adding the table that contained the population data to the data frame.  We joined the table with the population data to the census tract data table so that each tract would have its own population data.  Finally in order to map population density we created a new field in the tracts table with a calculation that measured population density by dividing the population of the tract by the area of the tract converted into square miles.  We then changed the colors of the tracts to reflect their population densities, the darkest having the most dense populations.  This map allows you to easily see where the most people are concentrated in the county.

 

Tutorial Exercise 4: 

 In Exercise 4, we edited the data in the first map of Schools and Noise Contour to elongate and curve the airport road to make a loop that connects back to an arterial road at another point.  First we started editing using the Editor Toolbar and set snapping so that any new arterial lines we drew would "snap" to the lines that were already on the map.  We then started drawing the new road by clicking with the sketch tool and inputting various command such as making a segment parallel to an existing line or making a ninety-degree turn to the right.  Finally we finished the loop by connecting the new road segment back to an existing arterial and naming it "Airport Dr". 

Tutorial Exercise 5:
In Exercise 5, we tweaked the appearance of all the existing data frames to make them easier to understand.  We added backgrounds, titles, and simplified legends to the data frames that did not have them.  We relocated the north arrow from the Schools data frame to the bottom right corner of the layout because the same north arrow applies to all of the maps.  We gave the entire layout the title of Proposed Airport Expansion to show at first glance what the data is about.  Finally we added an extent rectangle to show where the proposed airport expansion area is on the map of the entire county.  To beautify the map we added a neatline and some drop shadows to the data frames.

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Lab 3: Neogeography

View A Summer Day on Martha's Vineyard in a larger map

     I think neogeography can be a great thing because it allows normal people to share knowledge of places and things that only they might have with the rest of the world through the creation of maps. 
I think there is huge potential for neogeography to benefit people because it can instantly turn a tourist into a local or make the newly re-located feel right at home.  I personally have used neogeography many times to my advantage when visiting places I have never been so that I can get to the local favorites and skip the tourist traps.  I think you can get a much better feel for a new place when you follow what the people who live there see and do everyday instead of just going to the standard crowded tourist attractions.
     There are, however, definitely some pitfalls and negative consequences of neogeography as well.  Since literally anyone can make a map now and make it public on the internet, the quality is obviously not going to be consistent.  Bad maps or maps made by people with very little knowledge of the places and attractions they include may lead people in the wrong direction.  The ease of making a map will make the sheer quantity of maps available so large that it will become increasingly difficult to find the good ones.  Also, for locals who prefer to keep their favorite spots secret, one person can make a map and instantly reveal all the places it took them years to find.  In this way, I think that neogeography definitely takes away some of the pride involved with getting to know a place intimately by living there and discovering things on your own or through real life interactions.  Neogeography so easily available on the internet makes visiting places less personal; instead of asking someone how to get somewhere or for a tip on where to eat, we now go straight for our computers or cell phones and search the internet.

Lab 2: USGS Topographic Maps

1. What is the name of the quadrangle? Beverly Hills Quadrangle
2. What are the names of the adjacent quadrangles? Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, and Inglewood
3. When was the quadrangle first created? 1966
4. What datums were used to create your map? The North American Datums of 1927 and 1983 and the National Geodetic Vertical Datum of 1929
5. What is the scale of the map? 1:24,000
6. At the above scale, answer the following:
     a) 5 centimeters on the map is equivalent to how many meters on the ground? 1200m
     b) 5 inches on the map is equivalent to how many miles on the ground? 1.894mi
     c) one mile on the ground is equivalent to how many inches on the map? 2.64in
     d) three kilometers on the ground is equivalent to how many centimeters on the map? 12.5cm
7. What is the contour interval on your map? 20 Feet
8. What are the approximate geographic coordinates in both degrees/minutes/seconds and decimal degrees of:
     a) the Public Affairs Building; 34*04'30"N = N34.075, 118*26'33"W = W118.443
     b) the tip of Santa Monica pier; 34*00'30"N = N34.008, 118*30'00"W = W118.500
     c) the Upper Franklin Canyon Reservoir; 34*06'15"N = N34.104, 118*24'41"W = W118.411
9. What is the approximate elevation in both feet and meters of:
     a) Greystone Mansion (in Greystone Park); 580ft, 176.8m
     b) Woodlawn Cemetery; 140ft, 42.7m
     c) Crestwood Hills Park; 800ft, 243.8m
10. What is the UTM zone of the map? Zone 11
11. What are the UTM coordinates for the lower left corner of your map? 3763000m northing, 362000m easting
12. How many square meters are contained within each cell (square) of the UTM gridlines? 1,000,000 square meters
13. Obtain elevation measurements, from west to east along the UTM northing 3771000, where the eastings of the UTM grid intersect the northing. Create an elevation profile using these measurements in Excel (hint: create a line chart). Figure out how to label the elevation values to the two measurements on campus. Insert your elevation profile as a graphic in your blog. 

14. What is the magnetic declination of the map? Plus 14 Degrees
15. In which direction does water flow in the intermittent stream between the 405 freeway and Stone Canyon Reservoir? The water flows South ("down" the map)
16. Crop out (i.e., cut and paste) UCLA from the map and include it as a graphic on your blog.