Lab8: Mapping the Station Fire in ArcGIS

     The Station fire, which occurred from August 25 - October 16, 2009, destroyed over 100,000 acres of land, threatening over 12,000 structures and bringing havoc to neighboring urban regions. Originally started in the Angeles National Forest off the 2 Freeway near La Canada, the fire quickly spread over the mountainous regions affecting nearby communities, such as La Crescenta, La Canada, Glendale, Acton, and areas near Altadena as well. The map above represents a reference map, showing the general region of the wildfire as well as the spread of fire as a function of time. Starting with August 29 at 2:48, the legend demonstrates the spread of wildfire until September 2nd at 9:02. As one could see, as time progressed, the wildfire enlarged, engulfing more of the mountain and getting closer to the interstate highways represented by the white lines. The elevation shown by the DEM, noted in the blue to red progression in the background of the graph, correlates with the wildfire starting in the mountain regions at higher elevations and spreading to lower elevations where the communities were located. In using GIS, one could get a better and easier visualization of the progression of this disastrous fire over time and the effects of the wildfire on different aspects of life.
     The station fire holds particular significance in my life because I live in La Crescenta; I was evacuated twice during this time and was able to see the fire spreading first hand to my backyard. In particular, I chose to utilize GIS to analyze the effects of the wildfire on the threat of schools because my younger brother was attending highschool at Crescenta Valley High School at this time, which is located in La Crescenta and was one of the evacuation sites for residents in the area. As the fire spread, more homes were evacuated to nearby shelters, one of them being schools. Using GIS, I wanted to see the threat that the school evacuation site had as people were pouring into the gym with all their belongings.
     When it comes to fire threat, there could be a wide range of stories one could explore; I chose to see the fire threat as a function of school location as my parents and brother were being evacuated there at that time. I operationally defined fire threat as the distance of the fire in relation to the school, utilizing somewhat of a buffer analysis analyzing the distance at different miles from the fire. As represented below, purple regions reflect closer distances to the fire and thus a higher threat of being destroyed by the flames. As the colors change from purple to green to yellow, the distance from the fire increase witha  negatively correlating decrease in the threat of the fire.

    In particular, I chose to label Crescenta Valley High School for reasons I specified above; Crescenta Valley High School is located in the purple region suggesting a relatively high threat of fire. Knowing this information, I probably would have asked my parents to evacuate a further distance as to find a safer location with relatively lower risk of threat. One sign of relief that is shown in this thematic map is that as time progressed, the fire seemed to spread in a North Eastern direction, away from the high population of schools; in that particular direction, the map shows relatively no school in that region as the majority of that area is part of the National Forest.
     Also shown in the map are major roads or highways that may have been under threat; As I live in the highest street right below the mountain and North of the 210 Freeway, my house was under high risk of fire threat. My street was closed off and rather than having an optional evacuation as people living a couple blocks down from us had, we were forced to evacuate immediately to Crescenta Valley High School or other evacuation locations they had set up as time and the wildfire progressed. This thematic map provides much more information than merely the location of schools, highways, and fires, but one can really analyze and see the cause and effects of such hazards on the daily lives of people living in those particular areas. This thematic and reference map both demonstrate a greater insight as the how the station fire became so large and dangerous. As there was only brush and trees in the National Forest, it only makes sense that the Station Fire got so large in such little amount of time. From looking at this map, one can ask further questions and make suggestions as to prevent fire threats from reaching such close proximities to the neighboring communities; using the fire threat map, one may be able to suggest ways schools with the highest threat of fire can utilize to protect their students as well as the residents in that area from possible fires in the futures and ways to better effectively contain wildfires in this area. Although fires may not be unstoppable, using maps like this can provide mechanisms and motivation to help stop fires like these from getting so dangerous to the communities in the future.


Bibliography

Greninger, Mark. "All Station Fire Perimeters (as of September 2, 07:02) -€“ Complete Set." Los Angeles County Enterprise GIS: All Station Fire Perimeters (as of September 2, 07:02) – Complete Set. N.p., 2 Sept. 2009. Web. 15 June 2012. <http://egis3.lacounty.gov/eGIS/2009/09/02/all-station-fire-perimiters-as-of-september-2-0702-complete-ste/>.


"Mapshare: UCLA's Spatial Data Repository: Los Angeles County." GIS at UCLA: Mapshare DB" UCLA, 1 Apr. 2008. Web. 15 June 2012. <http://gis.ats.ucla.edu/Mapshare>.


"Mapshare: UCLA's Spatial Data Repository: Los Angeles County Highways." GIS at UCLA: Mapshare DB" UCLA, 1 Apr. 2008. Web. 15 June 2012. <http://gis.ats.ucla.edu/Mapshare>.


"Mapshare: UCLA's Spatial Data Repository: U.S. Geographic Names Information System Schools for Los Angeles County." GIS at UCLA: Mapshare DB" UCLA, 1 Apr. 2008. Web. 15 June 2012. <http://gis.ats.ucla.edu/Mapshare>.


William-Ross, Lindsay. "New Evacuation Orders Issued Due to Station Fire." LAist. N.p., 31 Aug. 2009. Web. 15 June 2012. <http://laist.com/2009/08/31/new_evacuation_orders_issued_due_to.php>.

Lab7: Census

    The above maps represent the distribution (ranked by percent) of three particular races across the United States; all data was based on the census data collected in 2000. The first map above (in purple) displays the distribution of percentage of Black Americans throughout the counties of the United States. The lighter purple colors display a smaller percentage of Black Americans while a darker purple shade reflects a higher proportion of Black Americans in that particular area. As shown, there is large, dense population of Black Americans in the Southeastern portion of the United States while some other portions in the Midwest and the North do not show the same dark shade, reflecting a smaller proportion of Black Americans living in that area. There are some darker areas near Wisconsin, but the majority of the Black Americans tended to live in the Southern regions of the United States in 2000.

     The second map reflects the percentage distribution of Asians living in the United States in 2000. In comparison to the first map, there seems to be a greater even distribution among the counties throughout the country in terms of percentage distribution; however, we do see highly populated regions in California as well as places like New York. The percentage of Asians living in an area tends to be greater near the coast as California, New York, some parts near Wisconsin, and other areas all sem to border the water or ocean. This may be due to migration from the motherland or merely just preference among this particular race.

     The third map shows the percentage distribution of some other race alone, representing the percentages of other races based on the 2000 census data of the United States. We can see the distribution of minority races in this map. We must note that in this map, the legend colors signify not a gradient shade from light to dark where light shades signify a lesser percentage of population but a two-colored legend signifying the percentage distribution. In this case, blue would represent a lesser populated percentage area while red signifies a heavily populated region. It is clearly shown that the Southwestern regions are more heavily populated by 'some other race' while the Northern most parts of the United States show the lightest percentage population.

     Mapping the data of 2000 census offers an easy visual representation for the every viewer to see the distribution of different races as well as other types of census data; It is easy to read and simple for many people to understand. GIS can easily map out these types of data and thus pose and address questions based on these maps, such as why there seems to be aggregates of particular races in certain regions of the United States and so forth. GIS also makes it simple for the viewer to compare between maps to see which counties have a higher percentage of one particular race over another. Given these maps created by GIS, one could possibly look deeper into other issues, such as the history of these particular races and/or migration patterns that have brought these races to the areas they are at in 2000. Also, GIS could show through dynamic mapping, changing from year to year when the next census data is collected. We could potentially see changes in the percentage distribution among these races, in terms of where the majority of the people moved to or any other changes possible.

Lab6: DEMs in ArcGIS

     The region I selected for the Digital Elevation Model (DEM) analysis was located at the latitude and longitude coordinates of 36 51 27.03N, 117 44 23.82 W. The coordinates of the rectangle analyzed were as follows-- top: 36.9791666659, left: -117.805277779, right: -117.242777779, and bottom: 36.6699999993. According to Google Maps, these coordinates refer to the Death Valley National Park, specifically to S. Warm Springs Rd. in California. The Spatial reference utilized was the GCS_North_American_1983, referring to the Geographic Coordinate System using the datum North American 1983. The Death Valley National Park contains various geographic environments, ranging from valleys and salt flats to large mountains and canyons. In the particular area I chose within this national park, we see a rather large mountainous range surrounded by lower valley-like regions that are shaded in the Shaded Relief Map; the elevation for the mountainous region is clearly shown in the 3D map as well.

Lab5: Map Projections

Conformal:
1. Mercator

Planar: 10,112.118968 miles

Great Elliptic: 6, 934.483772 miles

2. Stereographic

Planar: 9,878.038997 miles

Great Elliptic: 6, 934.483772 miles

Equal Area:

1. Behrmann Equal Area Cylindrical

Planar: 8,763.089124 miles

Great Elliptic: 6, 934.483772 miles

2. Bonne

Planar: 6,730.704827 miles

Great Elliptic: 6, 934.483772 miles

Equidistant:
1. Equidistant Conic

Planar: 6, 972.480093 miles

Great Elliptic: 6, 934.483772 miles

2. Azimuthal Equidistant

Planar: 8,341.411788 miles

Great Elliptic: 6, 934.483772 miles

     Map projections are often utilized to portray a part or all of the Earth's surface on a 2-dimensional flat surface; it always comes with some distortion and each projection has its own advantages and disadvantages. There are mainly three types of projections-- conformal, equal area, and equidistant (as shown in above examples), each preserving a different element of shape, angle, direction, distance, or area since a map projection cannot preserve all these features. In this lab, we sought to study the mapped distance between two cities, Washington D.C. and Kabul, Afghanistan and the differences based on the particular projection we were viewing. We measured the distance using two different methods-- Planar and Great Elliptic. Planar simply measures the distance using 2-dimensional Cartesian mathematics while Great Elliptic utilizes the intersection at the surface by a plane that passes through the center of that ellipsoid as well as the start and endpoints of the particular segment.
     The first two map projections, Mercator and Stereographic, reflect conformal map projections that preserve shape, angle, and direction. The conformal map projection may be useful for any tasks that require viewing the correct shape of the countries. These map projections are characterized by the 90 degree angle that is maintained by the latitude and longitude lines. This projection does not, however, preserve area or distance; for example, in the Mercator Projection, Antartica is enormously represented in comparison to the rest of the world. This type of projection is also useful because its directions are fairly accurate, which may aid in navigation. The main drawback of a conformal map would be the increasing amount of distortion as you get farther from the parallels.
      The next two map projections, Behrmann Equal Area Cylindrical and Bonne, reflect equal area map projections that preserve the area, as suggested by the name. This projection is advantageous in that the viewer is able to obtain a realistic visualization of the correct geographic sizes of different entitites. In addition, this map projection can be useful in showing distributions of geographic characteristics, such as pollution and population density. Despite its advantages, some limitations are that this map projection does not preserve shape, angle, distance, or direction. As you get farther from the center, the shape, angle, and direction can get increasingly distorted. In addition, distances can also be inaccurately represented due to it not being preserved.
     The final two map projections, Equidistant Conic and Azimuthal Equidistant, reflect equidistant map projections, preserving the distance of the Earth's surface. This projection depicts true distances that can be useful in mapping buffer zones, as seen by the missile buffer zone in North Korea shown in the lecture slides. Although the distances are true, depending on the type of measurement (eg. planar, great elliptic), the distance can be vastly different. Some limitations of the equidistant map projection is that this projection fails to preserve shape, angle, direction, and area. In addition, although this projection shows true distance, it is merely limited to distances from the center of the projection outward; it does not reflect the correct distance between any two random points.

Lab4: Introducing ArcMap

     My ArcGIS experience went fairly smoothly; other than some rough patches following some of the directions, ArcGIS was a relatively pain-free system to utilize. As the system may look daunting at first glance with countless number of files and various buttons on the screen, the instructions are relatively straightforward and easy to follow which allowed me to pick up the how-to's of the system quickly. This advantageously allows for a greater, wider audience to be able to access and learn how to use ArcGIS, expanding the ability to share, learn, and analyze spatial information in our everyday world. However, despite its easy learning, the system has many files that are needed to be obtained, imported, or converted to create such elegant maps as the above, deterring its audience from even wanting to learn the ways of the program. I, too, had some difficulty understanding what all the .mxd files were before going through the tutorial.

     When following the instructions, I ran into trouble when it came to the zoom in/out button. As there seems to be multiple ways to zoom in and out, I often times found myself zooming in too much on one map thinking I was zooming in on another map and so forth. It took some time getting used to, as this part was surprisingly the hardest part of the tutorial in my opinion. This could potentially be a pitfall for first time users like myself as maneuvering the different basic functions of ArcGIS may not be comfortable at first. One must always make sure what diagram he or she is working with, what layer he or she is viewing at the moment, and how much to zoom in or out.

     Going through the tutorial step by step proved to be quite helpful and informative, as I was able to learn all the various functions ArcGIS had to offer. There is a vast number of functions that ArcGIS can do when it comes to drawing, creating, editing, and analyzing spatial data that is efficient and helpful to the daily user. Taking just one particular feature on a map, there seems to be countless number of things that could be done, from adding and subtracting layers to creating attribute tables and calculating the populations of particular regions. Without ArcGIS, I would imagine obtaining this kind of information would take a lot longer and maybe not even be as accurate as this system. One could even change up the presentation of the map, adding effects and legends as well as changing up the visuals, such as background, symbols, and colors to portray exactly what one wants.

     Despite the vast potential of ArcGIS, there is another major pitfall to this system. ArcGIS, due to its availability to anyone who is able to access and purchase the system, can be susceptible to potential misrepresentations by amateurs who are not quite familiar with the system or do not provide correct spatial data. This could be potentially dangerous as people within this field often share their spatial data with one another, working off each other to create more maps and to further analyze the information at hand. However, I do not believe that this poses much of a threat because of the efficiency and effectiveness of ArcGIS to provide correct and useful information to users around the world.

Lab3: Neogeography

View Lab3 in a larger map
Deemed the Happiest Place on Earth, Disneyland is one of my favorite places to visit even at this age. Although it may seem childish, this place brings back fond memories of my childhood as well as allows for new memories to be made with friends. The above map is the route that I always take throughout the day on my trip, going from land to land around in a circle. One must be efficient when going to Disneyland, taking advantage of all the best rides by getting a fast pass and skipping the long waiting lines. My favorite ride is Matterhorn, denoted by the red polygon.

     Neogeography, literally meaning "new geography", refers to the emergence of a user and community-centered ability to share geographical information. Often seen as revolutionary, it enabled non-experts and everyday users to utilize and share geographical information with one another through mash-ups such as Google Maps, Microsoft Bing Maps, and Mapquest. As 80% of things in this world are locatable, neogeography proved to have some advantages such as increased geographical awareness, freedom to post and access geographical information, and even the ability to make maps for everyone creating a richer, more interactive experience.
    However, there are some pitfalls to this progression towards neogeography. For example, because it is user-oriented and often used by volunteers, it is susceptible to misrepresentations and errors by amateur geographers and non-experts. Often due to inaccurate mapping, geographical data and information may be unreliable. In addition, many of the details that are of interest to one person may be oversimplified,not represented, or distorted to other viewers that might be seeking similar yet different information.
     In turn, neogeography creates specific maps, catered to one's personal interests; therefore, people must provide honest and reliable information as there are strong interactions with its community. Specific to the needs of the person's needs and desires, this map can be helpful in directing one's needs in a simple representation of a local map that will be much easier to read and free of potential biases of the particular company. In addition, neogeography provides a more practical use to sharing geographical information in everyday culture, helping people visualize locations better and providing a richer experience for its viewers. As neogeography proves to be a volunteer-based sharing of geographical information, any map can be created for any type of information desire but may be vulnerable to distortions of factual geographical information.

Lab2: USGS Topographic Maps

1. Beverly Hills Quadrangle
2. Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, Inglewood 
3. 1966 
4. North American Datum of 1927 
5. 1:24,000 
6. a) 1200 m
   b) 1.8939 miles
   c) 2.64 in
   d) 12.4968cm 
7. 20ft 
8. a) Public Affairs Building: 34° 04’ 30” N 118° 26’ 35” W / 34.075 N 118.443 W
   b) Tip of Santa Monica Pier:  34° 00’ 42” N 118° 30’ 02” W / 34.0116 N 118.5005 W
   c) Upper Franklin Canyon Reservoir: 34° 7’ 05” N 118° 24’ 39” W / 34.1173 N 118.4108 W
9. a) Greystone Mansion (in Greystone Park): 560ft, 170.7m
   b) Woodlawn Cemetery: 140ft, 42.6829m
   c) Crestwood Hills Park: 640ft, 195.1219m
10. Zone 11 
11. 3763000mN 361000mE 
12. 1,000,000 m^2, 10,763,910 ft^2
13. 
14. 14°
15. South
16.