Lab 8 Final Project- Mapping the Station Fire

August 26, 2009, the Station Fire started in central Los Angeles County in Angeles National Forest. The fire was 98% contained by September 27, and spread over 160,000 acres (251 square miles). The fire started in the southern part of the forest and spread to the North over the next few days until it was mostly contained. Over the time period, the fire mainly stayed within the boundary of the forest. Therefore, damage to homes only could occur on the northern most border of the populated area. At least 18 homes were destroyed. The fired threatened houses and other structures in La Canada Flintridge, Glendale, Acton, La Crescenta, Pasadena, Littlerock and Altadena. The goal of this project to see what the population density looked like for areas near the fire.


My hypothesis was that the most population dense cities in the county would be minimally affected by the fire. To test this hypothesis I created a fire extent map. This map showed the extent of the fire at different times and days. My main focus was looking at the furthest extent of the fire. I created 2 buffers around the furthest extent (9/2 12:39AM). The first buffer showed the area within 2 miles of the furthest extent. The second buffer showed the area within 4 miles of the furthest extent. This illustrated which areas were under high, low, and medium threat. 


I then collected data on cities and population. I found a shape file of Los Angeles County cities and added it to the map. I also found data on population of most of the cities. I joined these two attribute tables so that I could compare the different cities on the map. I decided to map population density as opposed to just population. This is because population density gives values that have already taken into account area. If two cities have the same population, the city with a larger area will have a smaller population density. This larger city will be more safe in terms of evacuating during an emergency. Though Los Angeles has the highest population, it is not very population dense because it covers such a large area. Population density, not population is what matters in terms of safety in an emergency.


After mapping out population density of different cities, I found that my hypothesis was somewhat valid. The areas with very high population densities (10,000+ people per square mile) were relatively far from the fire (8-10 miles). However, not all areas near the fire were of low population density. Some areas were still of medium population density (7000 people per square mile). However, for the most part the northern part of the populated area was not as population dense as the rest of the county. In terms of evacuation and safety, this was a very good thing. No area within the 4 mile buffer was above a population density of 8000 people per square mile.


I also looked at the elevation models of the fire extent. The graphs dealing with elevation only contain areas near the fire, not all of the county. First, I created a hill shade and 3D model of the area's elevation. I found that most of the National Forest (where the fire extended), was of higher elevation, and the populated areas were of lower elevation. I also mapped out the fire extent over time on top of the elevation models. At each time period, there is a map of the elevation and fire extent. As shown by the maps, the fire starts at the southern end of the National Forest. The fire then spreads northward. In terms of elevation, the fire generally spreads to higher elevation. It does not spread downhill into the populated areas and cities. While one could assert that the fire spread northward because it was attracted to higher elevation, that is not the most probable conclusion. It is more likely that the fire spread in that direction because of wind direction and the direction that the dry forest was located.


In conclusion, the Station Fire spread to a large area of the Angeles National Forest. Though some areas in threat of the fire were of medium population density, highly population dense areas were generally safe from the fire. This probably made it relatively easy to get people evacuated and safe as the fire spread closer to homes. Also, by looking at the digital elevation models, we see that the areas of highest elevation were in the National Forest. The fire generally stayed within this boundary of high elevation. The fire spread to the north into the National Forest.





"Station Fire Information." Lasdblog.org, 3 September 2009. Web. 30 November 2011.  http://www.lasdblog.org/Pressrelease/PR_Folder/SFUpdateTH-00.pdf

"'Angry Fire' Roars Across 100,000 California Acres." Cnn.com, 31 August 2009. Web. 30 November 2011. http://articles.cnn.com/2009-08-31/us/california.wildfires_1_mike-dietrich-firefighters-safety-incident-commander?_s=PM:US

"Station Fire." InciWeb, 10 November 2009. Web. 30 November 2011. http://www.inciweb.org/incident/1856/

Garrison, Jessica. "Station Fire Claims 18 Homes and Two Firefighters." Los Angeles Times, 31 August 2009. Web. 30 November 2011. http://articles.latimes.com/2009/aug/31/local/me-fire31

"Wildfires in Southern California." The Boston Globe, 2 September 2009. Web. 30 November 2011. http://www.boston.com/bigpicture/2009/09/wildfires_in_southern_californ.html

Lab #7 Population by Race- 2000 Census

This is a map of the black population in the United States. The country is divided by county and each border is represented. The data values that are plotted are the percentage of blacks of the total population of that county. Therefore, though one county may have a higher population of blacks, a small county could have a higher percentage. Each county is shaded a different color based on the percentage of the total population that is black. The legend on the map shows what intervals of percentages correspond to which shades. We can see from the map that counties in the southeast generally have the highest percentages of black population. This corresponds to what we would expect from United States history. The rest of the country has mostly counties with about 1% black population. However, there are scattered areas where the black percentage is as high as 5-10%.

This map shows the population by percent of the asian population. Each county is shaded based on the percentage of asians compared to the total county population. Shades of green indicate low percentages of asians (0-2%), yellow indicates a medium percentage of asians (2-10%), and orange and read represent a high asian population (over 10%). Most of the country has a low percentage of asians in the population. We see mostly green throughout the country. In the middle of the country, there are scattered counties of yellow counties. Where we see noticeable clusters of asian populations is in the northeast. Even more so, we see clusters on the west coast, especially in California. There is only one county in the country with an asian population of over 25%. It is located in California. Once again, we can trace these patterns back to immigration patterns we have seen throughout American history.

This map shows the population density of "some other race." This means any race other than Caucasian, Black, Asian, Native Hawaiian, and "mixed race." Once again, percentage is depicted on the map by color shade. In this map, yellow means a low percentage, green means medium percentage, and blue means high percentage. From this map, we see definite patterns. Most of the east side of the country has low population percentages of "other" races. We see most of these races distributed on the west side of the country. We see that most of the high percentage counties are located in the south west. We can conclude that this is most likely caused by immigration to the areas by the hispanic population. We conclude this because these areas are the closest to Mexico.

From these three maps, we can see patterns of the percentages of different races in each county. Since the map easily illustrates patterns, we see where certain ethnic groups are clustered and where they are not found. We can then connect these patterns to history of immigration patterns and see why these groups cluster in these locations. Population density maps are a good tool to measure population because a numerical population is not always good enough. For example, if we measured population by number instead of percentage, we would see that every race is clustered around the same areas. This does not show anything about race. It only shows that these areas have a high population in general. Therefore, it is better in this case to measure the percentage so that small and large county populations are represented equally. In this way, population density can illustrate many demographic patterns.

My overall impression of GIS is that it is a useful and relatively easy tool to use. GIS is user friendly and easy to learn how to use. After only a short time of using GIS, I feel confident in my ability to make basic maps. With GIS, a user can map anything. With the proper data, it is easy to transform information into easy to understand illustrations. GIS allows users to transform data from an excel spreadsheet into a form that Arc GIS can read. This is very useful to users because it allows them to create their own data or to get it from an online source. GIS is a very important tool in the spread of geographic knowledge. One of its major effects is the growth of neogeography. With the correct software, virtually anyone can make maps on their own.

Lab #6 Digital Elevation Models

These are digital elevation models of the Sawatch mountain range in Colorado. The range is home to many mountains and peaks. The two highest peaks are Mt. Elbert (14433 ft.) and Mt. Harvard (14153 ft.). As seen by the slope map, most of the range is of very steep slope or flat slope, with little area covering intermediate slopes. The 3-dimensional elevation model displays elevation in a way that is much easier to see. Different DEMs demonstrate different aspects of the land cover. The extent information for the maps is as followed: Left 106.692499998839, Top 39.6599999994514, Bottom 38.5452777771542, Right 106.171944443249. The geographic coordinate system used is the GCS North American 1983 coordinate system.

Lab #5 Map Projections

Equal area map projections preserve area. Both the Eckert IV and Mollweide projections are pseudocylindrical. 

Conformal map projections preserve angles on a map. The most common conformal projection is the mercator projection. The Hotine projection, also known as the Hotine oblique mercator projection, is a cylindrical projection. The Azimuthal stereographic map projection is also conformal.

Equidistant maps preserve distance. The polyclinic, or American polyconic, projection is equidistant and projected using a cone shape. The Berghaus star projection is equidistant in the northern hemisphere and is based on the Azimuthal equidistant projection.

Map projection is a very important part of geography. We live in a three dimensional world, but we look at two dimensional maps most of the time. In order to translate the three dimensional globe onto a two dimensional map, a projection needs to be chosen that will suit the map’s purpose. By projecting a map, some aspects of the map will be distorted. Different map projections will distort different parts of the map. For example, conformal maps will distort distance and area, but preserve area; equidistant maps will distort area and angles, but preserve distance; and equal area maps will distort distance and angles, but preserve area.


Different map projections are useful in different cases. For example, conformal maps are useful while navigating. Though they distort distance and area, angles are preserve, which is the most important part of navigating. The most common conformal map is the mercator projection, which is often used in navigation. If the map's purpose is to compare the distance between points, equidistant projections will be the most useful. Even though area and angles are distorted, distance is the only important aspect of the map in this case. In many map projections, the size of different areas are distorted. For example, Greenland is often huge, while Africa is relatively small. If the purpose of the map is to compare sizes of continents, countries, or other areas, equal area maps are the most useful. These three map projections are useful for different purposes.


Knowing how map projections work is very important when looking at a map. Depending on the map you are looking at, it is important to realize that some aspects will always be distorted. For example, in a conformal map projection, it is important to realize the sizes of areas are more and more distorted the further away from the equator you get (assuming you are looking at a mercator projection.) Distance is a key aspect of maps that will be distorted. Looking at the measured distances between Washington DC and Kabul, you see discrepancies between the projections. Though some of the projections measure distance relatively accurately, they still variate, especially when looking at a conformal map.


Other differences to note between projections is that some projections cannot use a North arrow. For example, if the north pole is located in the middle of the projection, as opposed to the edge, a North arrow cannot be used. Also, the scale bar becomes irrelevant when looking at certain projections. Usually, equidistant maps are able to use scale bars. Also, some conformal and equal area maps can use them to an extent. However, some map projections distort distance so much that it becomes very unreliable to use a scale bar to measure distance.

Lab #4 Arc GIS

This week I explored the ArcGIS 10 software and became familiar with what exactly GIS is about. While working through the tutorial, I found that the functions of ArcGIS are relatively simple and are quite useful. GIS is mainly about working with different layers of information to form one cohesive map that brings all of the information together. In this case, the tutorial used information about the effects of expanding an airport. With GIS, I was able to map out which areas were affected by the airport, especially in terms of noise. Looking at the land within the noise contour, I was able to see that most of the land that was affected by the noise was residential. 

Through the tutorial, I also mapped out population density and schools. The map of schools showed that only one would be affected by the noise of airport expansion. The population density map shows how populated certain areas near the airports are. This enables the reader of the map to see which areas that will be affected by the airport have the most people. In addition, the tutorial taught me how to use the editor to add lines, points and polygons to the map. In this case, I added a road (a series of lines and curves), to the map. 

ArcGIS 10 is a very useful software and increases the potential of GIS. This software, and other GIS softwares, make using GIS readily available and relatively easy to use. GIS allows people to create maps that layer different pieces of information on top of each other. Information contained in a map is usually easier to understand than a series of tables. As the importance and potential of GIS grows, it will be easier to convert separate pieces of information into one layered and cohesive map. 

Though GIS is very useful, it can still be improved. Some aspects of GIS are hard to use, and the average person may not understand how to use the software. GIS software needs to continue improving their product and make it user friendly, especially for individuals without experience in GIS. Though some GIS software is inexpensive or free, the best software is very expensive. If GIS software became affordable for the average person, more people could learn how to use it. However, as of now, the software is too expensive and is sometimes difficult to use. Therefore, the average person will not acquaint themselves with GIS software. If a user friendly, inexpensive GIS software was available, more people would want to learn to use it. Neogeography could potentially spread to GIS as well. Eventually, GIS software could be so readily available, that virtually anyone could use it to make maps.

Lab #3 MyMaps- Most Dangerous Vs. Safest Cities

Most Dangerous vs. Safest Cities in United States

View Larger Map
                          Rank            Score
Green--              1-50           (49.35)+          
Turqouise--        51-92         (49.35)-(33.00)  
Purple--             93-130        (33.00)-(15.00) 
Blue--               131-193       (15)-15            
Pink--               194-272        15-58             
Yellow--           273-350       58-126.5          
Red--                351-400       126.5+            


Data Source : http://os.cqpress.com/citycrime/2010/City_crime_rate_2010-2011_hightolow.pdf


This map shows the distribution of cities in the United States based on safeness. The data provided by cqpress lists cities of populations of 75,000 or more in order of rank of crime index. Crime indexes calculated using the number of reported crimes per year per 100,000 people in the city. The crime index is compared to the national average. A score with parentheses is below the national average (safer than average), and a score without parentheses is above the national average. The 50 safest cities are represented by a green point and the 50 most dangerous cities are represented by red points. The remaining colors correspond to the other 300 cities. From safest to most dangerous: turquoise, purple, blue, pink, yellow (blue being the closest to the national average). 


This map shows us several patterns. The most notable pattern is that the red and yellow points (the most dangerous cities) are clustered mostly on the eastern side of the country. The safest cities cluster in Southern California, parts of Texas, and the northeastern states. In he western half of the country, we see clusters of cities in California, but empty space in the rest of the areas. This is because most of these cities have populations less than 75,000. California has its share of dangerous and safe cities. Looking at the top 10 safest and top 10 dangerous cities, there are 3 California cities in each. However, looking in the top 50, California has 7 of the most dangerous and 11 of the safest. 


Neogeography


In recent years, people have had more access to geography. This includes both the ability to read maps and the ability to create maps. neogeography has increased the importance of geography to the average person. With more access to map making, geography has much greater potential. Anyone is able to make their own maps, therefore more maps will be made. Being able to make maps easily allows the average person to feel connected to geography. People also have more understanding of geography and how to read maps after they have created their own. Neogeography has made geography much more important to the average person.


However, there are also consequences to neogeography. Though everyone is now able to make a map, we should only rely on them to an extent. Just because everyone can make a map, does't mean everyone should. A lot of the time, these maps have no purpose and are inaccurate. For example, people may wish to make a personal make that lists their favorite places to go or the route they take to work in the morning. This could be useful to that person, however, what purpose does it serve for the rest of the world. Many maps that are being made can only serve individuals or small groups who made the maps. The average person also cannot be trusted to create an accurate and reliable map. People without a background in geography are not knowledgable enough to make an accurate and useful map. We still need geographers to create maps that have a purpose and that are accurate. Though neogeography has made geography more important in society, it still cannot replace the work of geographers.




 

Topography- Lab #2

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, Inglewood

3. When was the quadrangle first created?  

1966

4. What datum was used to create your map?

North American Datum of 1927, North American Datum of 1983, National Geodetic  Vertical Datum of 1929

5. What is the scale of the map?  

1:24000

6. At the above scale, answer the following: 

a) 5 centimeters on the map is equivalent to how many meters on the ground?  

1200 meters

b) 5 inches on the map is equivalent to how many miles on the ground? 

1.89 miles

c) one mile on the ground is equivalent to how many inches on the map? 

2.64 inches

d) three kilometers on the ground is equivalent to how many centimeters on the map?  

12.5 centimeters

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^o04’30”N,118^o26’15”W) , (34.075^oN, 118.4375^oW)

b) the tip of Santa Monica pier; 

(34^o00’27”N,118^o30’W), (34.0075^oN, 118.5^oW)

c) the Upper Franklin Canyon Reservoir; 

(34^o07’11”N,118^o’48”W), (34.120^oN, 118.413^oW)

9. What is the approximate elevation in both feet and meters of: 

       a) Greystone Mansion (in Greystone Park); 

570 feet,  173.74 meters

b) Woodlawn Cemetery; 

140 feet, 42.67 meters

c) Crestwood Hills Park; 

680 feet, 207.26 meters

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? 

3763000 Northing, 362000 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?

14^o East

15. In which direction does water flow in the intermittent stream between the 405 freeway and Stone Canyon Reservoir? 

North to South

16. Crop out (i.e., cut and paste) UCLA from the map and include it as a graphic on your blog.

3 Maps

MLB Fan Distribution by Location

http://www.singaporesoxfan.com/2005/10/map-of-mlb-world.html
This map is from a blog created by Daryl Sng. The map illustrates the distribution of fans of different MLB teams. The map shows which team is the favorite in different areas. There are several interesting parts of this map that catch my eye. I find it interesting that in states where there are several sports teams, each team only claims a small area. In Chicago, and the surrounding areas, the Cubs are represented very well, however, the White Sox are barely noticed. However, in states where there is only one sports team, the area that team claims is much larger. When states are adjacent to states without a team, the area of their team is also larger. Does this mean that there are more fans, or just that the fans are more spread out? It is also interesting that in California, there are quite a few teams that take up a small portion of their home state, but also have a large fan base in adjacent states. For example, the Oakland Athletics have a larger area of fans in Arizona than in California. Also, I find it strange that the eastern half of New York is shown as being Boston Red Sox fans. Most would think that all of New York would be loyal to their Yankees, or Mets.


Obesity vs. Fast Food Locations

http://www.thegrio.com/specials/the-big-issue/obes.php


This map from "thegrio" shows how the number of fast food locations in an area relates to obesity. The red dots on the map represent fast food locations in the United States. The key in the bottom-left corner of the map shows that the shades of the states represent the percentage of people withing that state that are obese. The map shows that the red dots tend to cluster on the east side of the United States. The shades of the states tend to get darker the further ease you look. This leads to the conclusion that a higher density of fast food restaurants in an area leads to more obesity. I find it interesting, however, that some states manage to keep obesity rates down despite having many fast food locations. For example, Virginia is clustered with red dots, but still manages to maintain an obesity percentage less than 23.9%. An outlier on the other end of the spectrum is South Dakota, having very few red dots, but also having a high percentage of obese people in their population.

United States Religion Distribution

http://www.valpo.edu/geomet/geo/courses/geo200/religion.html
http://www.valpo.edu/geomet/pics/geo200/religion/church_bodies.gif



This map, on a site from "valpo.edu" titled American Ethnic Geography, illustrates the distribution of religion across the United States. The map is divided into counties, and the counties are shaded based on which religion the majority of people practice. I find it interesting how different religious groups are distributed. For example, it appears that Catholics inhabit the broadest area of the United States. However, within this area, there are groups of other religions that can be found as well. For example, though the Catholics are located from coast to coast and in almost every state, there are smaller populations within the shaded Catholic area. The West coast and Northeast is highly dense in Catholics. There are also Catholic counties located in the Midwest, but the concentration is less dense. Christians, Methodists, and Lutherans are also large groups in these areas. On the other hand, the Baptists have a large area, but are much more condensed. The Baptists have approximately as many counties as the Catholics, but their shaded region is much more condensed. The Baptists inhabit, for the most part, one (and only one) area. While the Baptists have more counties in which they are the majority, the Catholics have a much broader area because they are more spread out. I find it interesting that some states are completely shaded one color. A few states in the far Northeast are completely colored blue for Catholic. Arkansas and Georgia are bother colored red and are completely Baptist. And Utah is shaded to represent the Church of the Latter Day Saints.