Week 9: Mapping the Census

This week, I created a series of maps showing county-level race data of Black, Asian, and Other Race based on percent composition of the respective race. These maps were created using publicly available US Census 2000 data in the form of Excel spreadsheets. These spreadsheets were then converted into a format which could be used in ArcGIS. Counties in white were missing Census data and the states of Alaska and Hawaii were not included in these map.

This map shows the percentage of Blacks in each county, with red symbolizing a greater percentage and yellow showing a greater percentage. According to the map, the counties with the greatest percentage of Blacks are found in the Southern United States which include Virginia, North Carolina, Alabama, Georgia, Louisiana, Arkansas, Mississippi, parts of Texas, and Washington D.C.. These are where most of the deep red counties are located, indicating that 53-86 percent of the population is Black. Additionally, light orange and orange counties are found around almost every major city, with percentages ranging from 8-25 percent. The less densely populated counties, namely rural areas from the Midwest, going west until Nevada, and areas far to the north such as Maine, Montana, North and South Dakota, and Minnesota show the lowest percentage of Blacks. For historical reasons, it makes sense that the South has so many counties that are predominately Black. It also makes sense for every urban area to have a greater percentage of Blacks because most of the United States population lives in urban areas.



This map shows the percentage of Asians in each county, with a deep purple color indicating a high percentage of Asians and a lighter purple color indicating a lower percentage. A higher percentage is seen around most major cities. The greatest concentration of Asians is found along the West Coast in California. As many of the first Asian-Americans arrived by boat, it would make sense for subsequent generations to establish the Asian-American community in California, around the Los Angeles and San Francisco, with percentages ranging from 10-48 percent. Other major Asian communities outside of California are found in New York City and Houston. Hawaii, not shown on the map, also has a high percentage of Asians.
This map shows the percentage of "Other Race" in counties across the United States. The category of "Other Race" includes anyone who does not identify themselves as White, Black, Asian, or Native American. This demographic would most likely include the Latino and Middle Eastern populations. The greatest percentages of this demographic are found in California and the Southwest, especially along the Mexican border, making up as much as 39 percent of the population. Interestingly, high percentages are found in the less populous counties in Central California. I would guess that most of these counties are representative of the Mexican population, along with other Hispanic communities. Since Los Angeles is known for its significant Iranian and Armenian populations, these groups may also comprise a significant portion of the "Other Race" demographic. I am also guessing that the "Other Race" populations in Florida and New York may be composed of minority groups from Caribbean nations such as Haiti or Cuba rather than Mexicans.

I found these maps were very interesting and revealed much about how the various minority groups were distributed about the United States. When reading these maps, it is also important to note that each county has a different number of people within it. About eighty percent of the population lives in urban areas, so the best way to gauge the national percentage of a particular race is to look around the major cities. For African Americans, the national average is about 12 percent. Asian Americans make up about 5 percent of the national population, and Latinos make up about 15 percent of the population. It was interesting to see how drastically the demographics in counties across the country varied compared to California's. As the Census 2010 information is collected, it would be important to track any changes in these demographics from 2000.

My experience with GIS has been challenging but was not without its rewards. From learning exactly what GIS was, to learning how to use ArcGIS, I came to understand both the complexity of utility of GIS. I was amazed to find all the publicly available information released by the USGS and Census Bureau which could be readily used by ArcGIS. I was interested in how spatial analysis could be used to produce quantitative data from both vector and raster data. I also liked how ArcGIS included tools to produce a professional layout, complete with legends and scale bars. Learning about GIS really made me appreciate how much work goes into gathering data and creating a map.

Week 8: Mapping the Station Fire

This week, I am using ArcGIS to create a map of the 2009 Station Fire, which I will analyze in terms of the damage caused to the burnt area and surrounding communities. My map includes the maximum area covered by the fire as it ran its course in the month of September. The burnt areas are represented by the orange area, layered over the green area representing the Angeles National Forest. In addition, I represented bodies of water in blue and major roads with red lines. I used a dataset of LA County cities to label my map with major communities so that the relative location of the fire can be more easily referenced. Lastly, I used DEM data to show the elevation gradient of land in brown because I found topographic information to be relevant to the spread of a natural disaster such as a wildfire. Additionally, I included a population density map showing that significant populations in the Pasadena, La Canada, and Glendale regions were in great danger at the peak of the wildfire.

The Station Fire of September 2009 posed a great danger to LA County before it was finally contained. It was the largest Los Angeles County wildfire ever documented. The fire started in late August and lasted through to mid October. Originating in the Angeles National Forest, the fire burned a total of 160,577 acres. The cost of fighting the fire reached over $93,779,000, which does not factor in the damaged and destroyed properties. In addition, the fire threatened the nearby cities to the south, and closed many roads going in and around the Angeles National Forest.

Though the fire started in the forest, nearby communities became threatened as the fire crept closer. The communities of La Canada Flintridge, Altadena, Glendale, Acton, and Palmdale had evacuation orders in anticipation of the fire. Unsure about whether the flames would engulf their community, residents halted their daily activities and braced for the worst. The La Canada and Glendale School Districts ordered school closures until it was deemed safe. Over 20,000 residents in La Canada alone were endangered by the Station Fire.

The forest which the fire started in experienced the most damage. Within the forest itself, an estimated 12,000 structures were threatened. Important wildlife habitats were also destroyed in the fire. Animals not killed in the fire were left homeless. By being forced out of their natural habitats, animals such as deer, bears, and mountain lions are prone to greater interaction with humans. This disturbs their natural way of living and can be detrimental to their species in the long run. Although wildfires occur naturally and pose potential benefits to the ecosystem, the scale of this wildfire makes it difficult for the ecosystem to recover from.

Overall, the fire destroyed over 200 properties. Of these, 89 were residential homes and 26 were commercial properties. An addition 57 properties were damaged in the Station Fire. 22 injuries and the death of 2 firefighters were caused by this fire. At the start of the fire, 3,655 people were assigned to fight the fire, using 400 fire engines, 10 air tankers, 8 helitankers, 5 helicopters, and 58 hand crews. Atop Mt. Wilson, numerous communications towers and multimillion dollar astronomy facilities were at risk, although these facilities were not damaged. Towards the end of its course, the Station Fire was determined to be a result of arson.

bibliography

Beltzer, Yvonne. "Station Fire Takes a Big Toll: Wildlife Habitat Lost." NBC Los Angeles. NBC, 04 09 2009. Web. 27 May 2010. .

Knoll, Corina. "TV signals from Mt. Wilson at risk." Los Angeles Times. Los Angeles Times, 31 08 2009. Web. 27 May 2010. .

"Station Fire evacuations." Daily News Los Angeles. N.p., 30 08 2009. Web. 27 May 2010. .

"Station Fire News Release." InciWeb. N.p., 31 08 2009. Web. 27 May 2010. .

"Station Fire News Release." InciWeb. N.p., 27 09 2009. Web. 27 May 2010. .

Willian-Ross, Lindsay. "Station Fire Update: Evacuations, School Closures & Other Info." LAist. N.p., 30 08 2009. Web. 27 May 2010. .

Week 7: DEMs

From top to bottom: Shaded relief map layered over a hillside model, slope map, aspect map, 3d rendering of my DEM

The DEM I have chosen is a 1" by 1" area of the Sierra Nevada Mountain Range within California, near Lake Tahoe. The extent of the map is 38.738 degrees North to 38.426 degrees North and -120.154 degrees West to -119.702 degrees West using the GCS North American 1983 coordinate system. Both the shaded relief map and 3d rendering show that the selected DEM contains many mountains and hills. The red represents higher elevations and the blue represents lower elevations, so by seeing the whole range of colors in the image I can tell that this region is very mountainous. The slope map appears to be mostly blue, a color used to show areas of high slope. This observation agrees with the steep nature of the terrain. Lastly, the aspect map color codes the DEM depending on which direction it is facing. As this is a mountain range, there is a homogeneous mixture of every color except for gray, which is used to denote flat regions.

Week 6: Map Projections

Map projections aim to solve the issue of how a 3D globe can be accurately reproduced on a 2D surface. The importance of accurate cartography has prompted the creation of many types of projections, each with its own strengths and weaknesses. The maps I have created were made using six different types of map projections. In the first row, on the left, is a Mercator conformal projection, and on the right, is a WGS 1984 Web Mercator conformal projection. The maps in the second row display the Cylindrical Equal Area projection and Sinusoildal Equal Area projection on the left and right, respectively. In third row, the map on the left is a Plate Caree Equidistant projection and the map on the right is a Equidistant Cylindrical projection.

Conformal maps, by definition, have longitude and latitude lines that intersect at 90 degree angles and therefore preserve local angles. These types of maps are useful for preserving direction. However, the projection distorts distance as you move away from the equator. In the Mercator projection, the distance between Washington D.C. and Kabul, Afghanistan was measured at 16,290,029 meters. In the WGS 1984 Mercator projection, the distance was 16,155,109 meters.

The equal area projections, as the name implies, aims to preserve the relative areas of all the countries on the map. This is useful for subjects like ecology and meteorology where the area something occupies is relevant. Getting an accurate measure of area is essential to extrapolation and drawing conclusions about an affected population in the area. In the Cylindrical Equal Area projection, D.C. and Kabul are 15,985,172 meters apart. This projection also preserves direction at the cost of being stretched out lengthwise. The Sinusoidal Equal Area map shows that D.C. and Kabul are 13,230,876 meters apart. This map is distorted to preserve the spherical shape and distance of a globe, but the direction is not preserved.

The equidistant map projections are used when distances are important, such as in airline navigation. Distances are preserved in respect to a chosen reference point on the map, which is usually the equator or a longitude line. The Plate Caree Equidistant projection spaces out circles of latitude evenly along a rectangular grid. By doing so, the projection distorts area and direction. According to this projection, D.C. and Kabul are 16,367,464 meters apart. The Equidistant Cylindrical projects the globe cylindrically along a longitude line. This causes the North-South distances to be stretched and the East-West distances to be compacted. In this map, D.C. and Kabul are only 8,145,837 meters apart. Equidistant map projections are especially important for calculating trajectories and weapon ranges in the military.

Week 4: ArcGIS

In my experience with ArcGIS, I've learned about both the complexity and power of this software. This software allows the user to create layered maps that are linked to a table of values. By drawing connections between these maps and values, valuable information can be extracted and graphed. For example, the contour line in the example represented noisy areas around an airport. Through commands in the software, it is possible to select land parcels that touch the contour as well as any that border those parcels. ArcGIS is able to specifically select sections of the map that the user wants to select. Because there is so much information linked to the set of spatial data, the user's selection criteria is limitless.

By giving the user so many ways to manipulate the data, ArcGIS tends to be complicated at times. The user would need to be very experienced with the software in order to utilize all of its functions. Unlike neogeography tools such as those provided by Google Maps, ArcGIS cannot be learned in a quick tutorial video. Even after going through the tutorial of ArcGIS, I would still need to repeat it several times before I understood what exactly I am doing at each step. Additionally, the software is very expensive and inaccessible to the general public. Instead, it is more suitable for professionals in urban planning, social sciences, ecological studies, or other fields which utilize spatial data.

I found that the most important thing I learned while using ArcGIS is the importance of spatial data. Through spatial data, the software is able to extract quantitative data. The resultant graphs from ArcGIS were crucial to drawing conclusions from spatial relationships and various values. The ability to select specific sections and layers, and then make a separate map out of it, was great for showing off spatial relationships. The noise contour from the tutorial exercise could potentially be useful for an urban planer who must present the information at a city council meeting to those trying to solve the issue of noise generated from the airport. ArcGIS provides an quantitative approach to solving such an issue.

The versatility of ArcGIS can also allow users to skew the information to argue a specific point. This is both a benefit and a potential problem to those who are not well informed about an issue. As I learned in lecture, every map has a bias stemming from what is included and what is not. As it is impossible to include everything in a map, the user is responsible to inputting the data into ArcGIS. By controlling the GIS data, the user will generate maps that only reflect the information that was chosen to be included. A biased map could potentially be a powerful argument tool. Similarly, a scientist trying to come to accurate conclusions about an issue would be limited to the pool of data that was gathered. As it is impossible to gather all the data available in the world, there would be a degree of error associated with any type of conclusion made.

Week 3: Neogeography

This is a map I made using Google Maps, which shows after-hours dining options around UCLA.

View UCLA Late Night Food Map in a larger map

The growing popularity of neogeography has brought many benefits to those who utilize it. The ability to create and view custom maps using tools like those in Google Maps allows users to share spatial information. However, every personalized map contains a bias as to what is shown and what is not. In doing so, the map conveys the creator's personal interests. For example, a map which maps out a walking tour of San Francisco would be representative of what landmarks and activities the creator is interested in. A walking tour of the city created by a photographer may may point out many scenic spots, making it especially useful to other photographer that want to find the scenic points. However, the same map may not be as useful to someone who wishes to visit historical landmarks in San Francisco.

As neogeography becomes more prevalent, popular points of interest continue to gain popularity while unmarked locations suffer. For example, if my map of restaurants became widely used, the locations I marked on the map would see a substantial boost in business. However, the restaurants that I did not include may be losing their customers to these businesses. In the age of iPhones and neogeography, a restaurant would suffer considerably by not being included in a database of restaurants. People have become more reliant on internet reviews and databases when looking for restaurants, which forces restaurants to keep up to date with technology.

Week 3: Neogeography

This is a map I made using Google Maps, which shows after-hours dining options around UCLA.

View UCLA Late Night Food Map in a larger map

Week 2

1. The quadrangle is named, "Beverly Hills Quadrangle," located in Los Angeles, California.

2. The northeast quadrangle is named Canoga Park. The northern quadrangle is named Van Nuys, the northwest is named Burbank. The west quadrangle is named Topanga. The east quadrangle is named Hollywood. The southwest quadrangle is unnamed because that is where the ocean is. The southern quadrangle is named Venice. The southeast quadrangle is named Inglewood.

3. The map was first made in 1966.

4. The North American Datum of 1927 and the North American Datum of 1983 were used to compile this map.

5. The scale of the map is 1: 24000

6. a) 5cm on the map is equal to 1200m on the ground.
b) 5 inches is equal to 1.89 miles on the ground.
c) 1 mile on the ground is equal to 2.64 inches on the map
d) 3 kilometers on the ground is equal to 12.5cm

7. The contour interval is 20 feet.

8. Geographic coordinates of:

a) the Public Affairs building: 34 degrees 4 minutes 27 seconds North, 118 degrees 26 minutes 21 seconds West, or 34.074 degrees North 118.439 degrees West
b) Tip of the Santa Monica Pier: 34 degrees 0 minutes 27 seconds North, 118 degrees 30 minutes 0 seconds West, or 34.0075 degrees North 118.5 degrees West
c)Franklin Canyon Reservoir: 34 degrees 5 minutes 54 seconds North, 118 degrees 24 minutes 43 seconds West, or 34.1 0degrees North, 118.41 degrees West.

9. Greystone mansion elevation: 560ft or 170.68 meters
Woodlawn cemetary: 140 ft or 42.67 meters
Crestwood Hills Park: 700ft or 213.36 meters

10. The UTM zone is 11.

11. The UTM coordinates are 3763000 m North 362000m East

12. The area contained in each square is 1,000,000 square meters.

13.
14. The magnetic declination is 14 degrees east.

15. The water in the stream flows south because the elevation is decreasing in the from North to South.

16.

Week 1

This map was created by a website called Calorielab. It shows the 50 states and Washington D.C. with the percentage of the population that is considered obese in each state. Furthermore, the states are color coded to show these percentages, with Colorado being the "leanest" state and Mississippi being the "fattest." Interestingly, these two states have opposite climates, suggesting a connection between obesity and weather. This map is interesting because the color coding allows me to make a generalization that more people in the Midwest and Southern states are obese, which leads to questions about why that is. Obesity can be attributed to diet, lifestyle, and genetics. Where the obese population is located can allow us to dissect the problem and potentially solve it. By asking what these "obese" states have in common, perhaps obesity can be better understood.


This is a map of Los Angeles and Orange County, found on a Los Angeles tourism site. It shows natural landmarks such as the Pacific Ocean and San Gabriel Mountains. However, the map emphasizes the many freeways in L.A. and Orange County, cities, and major tourist attractions. The major tourist attractions shown on this map include film studios and amusement parks such as Disneyland. This is interesting because it tells us that tourists visiting L.A. often group Orange County along with it. In addition, this map emphasizes how ubiquitous freeways are in Los Angeles. The prevalence of freeways in this area infers that many of its residents drive cars, which leads to further generalizations about Los Angeles culture and lifestyles.

This map by the United States Geological Survey was created with geological data. It shows the San Francisco Bay Area and the areas which are susceptible to soil liquefaction. The most susceptible areas boarder the bay. In an earthquake, liquefied soils provide poor structural support, leading to heavy damage to the structures which rest upon them. This is interesting because many of Northern California's urbanized areas, including major infrastructure, rest atop soil with high risk of liquefaction. The connection raises concern for these densely populated regions and a need to develop structural technologies which reduce the damage caused by an earthquake.