Lab 8 (Final Lab)

       On August 26, 2009, a wild fire ignited in the Angeles National Forest in Los Angeles county. This wild fire would become known as the “California Station Fire,” and quickly grew into one of the largest wild fires in modern Los Angeles history. It burned until October 16, 2009, and consumed “a total of 161,189 acres – or nearly 252 square miles” (“Station Fire Recovery”) of land, and destroyed a total of 209 structures, including 89 residencies (“Station Fire News Release ^[1]”). A few weeks before it had been entirely extinguished, the U.S. Forest Service launched a forensic investigation into the wild fire, which had claimed the lives of two firefighters. The organization attributed the wild fire to arson. The national Forest Service quickly determined that “the fire was started intentionally, and they labeled the firefighters’ deaths [as] homicides” (O’Connor), prompting a homicide inquiry by the Los Angeles Sheriff Department. Through various visual and analytical tools, much can be learned from this unique, and devastating fire that undoubtedly altered Los Angeles county in a variety of aspects.

       One such tool that allows for a detailed and powerful visual analysis, is GIS. GIS, or Geographic information system, is a tool utilized by the vast majority of contemporary map makers. Using the power of computers, GIS incorporates multiple data sets to create a visual “layer cake” that can convey a lot of information and a particular message. In this lab, a reference map was created to show the spread of the Station Fires in Los Angeles county. This was done by layering the different perimeters of the wild fire at different dates and times on top of a representation of LA county. The largest and most notable increases in perimeter occurred from August 29 to September 2, with breaks represented on the map at 2:48am on August 29th, and again at 12:25am on August 30th. The last perimeter represented on the reference map is that of September 2, at 7:02am. Additionally, the map features another data layer, a grid digital elevation model, which is a form of raster data. The DEM uses a color gradient to represent different elevations in LA county, with a white or red representing relatively high elevations, and a green or yellow representing relatively low elevations. Finally, the network of major LA roads is added to the map as a final layer. Using simple spatial analysis, a reader of this map can quickly discern some of the impacts this fire had on LA county. The location of the fires in relation to the rest of Los Angeles, and its progression and growth are easily understandable. Furthermore, the sheer size of the perimeter represents a significant portion of the county, easily 15-20 miles across in some areas, using the scale bar as a reference. Its close proximity to high elevation terrain also indicates that the fire occurred in the mountainous regions of the Angeles National Forest, where a high concentration of shrubbery is found. Although much can be known from analyzing the reference map of this fire, GIS allows for the creation of other maps that can reveal much more.

       The multitude of maps that can be created using GIS can answer a vast variety of geographic questions. Certain maps, along with spatial analysis, can reveal certain geographic relationships, and add value to spatial data. In the case of the Station Fires, we can ask the question: How do these wild fires affect transportation in Los Angeles? After analyzing the previous thematic map, we can hypothesize that due to the nature of the fire’s large perimeter, as well as its location in the county, transportation in Los Angeles will be hindered to a significant degree. It would seem likely that all forms of transportation, including road, rail, and air travel, will be affected by the station fires to a significant degree. With GIS, the construction of a thematic map, containing relevant data layers, allows us to see the geographic relationships between the fires and LA’s transportation conduits. This is just one way in which GIS can be used to answer geographic questions, as this is only one aspect of the Station Fire and its impact on LA county.

       The thematic map used to test this hypothesis will essentially be the same reference map constructed earlier, with additional data layers to reveal certain geographic relationships that are not easily discernible from analyzing the reference map. The first of these data layers is the network of major LA highways, which correlates to transportation by car. The second is the network of railways, which correlates to transportation by train. The third layer shows all major airports within LA county, and their perimeters. The fourth and last data layer shows all heavily populated regions within LA county, which reveals what medium of travel is most accessible and used by most LA residents. These heavily populated regions are clustered primarily around highways and railways, although they are clustered slightly more around major highways. This would suggest that car travel is the most popular means of transportation in Los Angeles. This is confirmed by data from the US census, which in 2005 reported that of “1,662,238 workers [over the age of 16 in the city of Los Angeles], all but 7.8% live in households where at least one car is available” (LA Transportation Profile). Thus, the roads that lie within, or near the station fire perimeter represent the most significant effect on transportation. The two major highways that intersect with the fire perimeters are California State Route 2, which runs primarily in the east-west direction, and California State Route 39, also known as Angeles crest highway, which runs primarily in the north-south direction. Shortly after the fire had attained the perimeter represented by the date and time of September 2 at 7:02am on the thematic map, “more than 40 miles of the Angeles Crest Highway from La Canada Flintridge to Islip Saddle [had] been closed indefinitely,” despite the fact that it handles “as many as 11,300 trips by motorists a day” (Weikel). This closure is quite evident from a simple spatial analysis of the thematic map. Furthermore, even after nearly three weeks, “the Angeles Crest Highway (Highway 2) and Highway 39 [remained] closed” to motorists (“Station Fire News Release ^[2]”). Again, this is strikingly evident on the thematic map by the extent to which these highways were engulfed by the fire perimeter on September 2nd. Additionally, other, less frequented roads within the fire perimeter “were left without guard rails, and regulatory and safety road signage making them unsafe for regular traffic” (“Angeles NF”). Although a variety of major highways were closed, railways were largely unaffected by the Station Fire. This can be seen as well on the thematic map, as many more roads come within close proximity to the fire than railways. And finally, LA airports appear to be the most unaffected of any data layer, though it should be noted that the airspace closest to the fire perimeter was likely closed to all aircraft except for emergency vehicles.

       Through the creation of a variety of visual tools, including thematic maps, GIS allows us to answer certain geographic questions. In the case of the California Station fires, a hypothesis stated that most forms of travel would be affected by the fire. This hypothesis was only partially true, as a spatial analysis of the map revealed that road travel was significantly affected, whereas railway and air traffic were largely unaffected by the fire perimeters. These findings were confirmed with data released by the US government regarding the closures of certain highways in LA county. This is just one of the many ways in which GIS can answer geographic inquiries, and reveal spatial relationships that otherwise may not be evident at first sight.

Bibliography

“Angeles NF - Station Fire.” U.S. Forest Service. United States Department of Agriculture, 4                       November 2009. Web. 12 December 2012. http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev3_020019.pdf

O’Connor, Anahad. “Los Angeles Fire Was Arson, Officials Say.” The New York Times. The New York Times Company, 4 September 2009. Web. 11 December 2012. http:// www.nytimes.com/2009/09/04/us/04fires.html?_r=0

“Station Fire News Release^[1].” Incident Information System. U.S. National Forest Service, 15 September 2009. Web. 12 December 2012. http://inciweb.org/incident/article/9535/

“Station Fire News Release^[2].” Incident Information System. U.S. National Forest Service, 26 September 2009. Web. 12 December 2012. http://inciweb.org/incident/article/9640/

“Station Fire Recovery.” USDA Forest Service. United States Department of Agriculture, n.d. Web. 11 December 2012. http://www.fs.usda.gov/detail/angeles/home/? cid=STELPRDB5292773

“The City of Los Angeles Transportation Profile.” LAcity.org. Los Angeles Department of Transportation, 2009. Web. 11 December 2012. http://ladot.lacity.org/pdf/PDF10.pdf

Weikel, Dan. “Angeles Crest Highway closed indefinitely because of fire.” LA Times. The Los Angeles Times, 4 September 2009. Web. 12 December 2012. http:// latimesblogs.latimes.com/lanow/2009/09/angeles-crest-highway-closed- indefinitely- because-of-fire.html

Lab 7

     As the name suggests, the first map shows the black population of almost every county in the United States. The data are from the US Census Bureau, and were collected in 2000. It measures the population of interest as a percentage of the total county population. These data are represented by various colors, and is displayed on the Lambert conformal conic projection of the contiguous United States. A yellowish color denotes a relatively low percentage (less than or equal to 5 percent) whereas a reddish color denotes a relatively high percentage (55-90%). The map legend breaks the size of the black population into 6 categories: 0.0103-5%; 5-15%; 15-25%; 25-35%; 35-55%; and 55-90%. There is a concentration of counties with a large black population in the southern United States, most notably in the states of Louisiana, Mississippi, Alabama, Georgia, South Carolina, North Carolina, and Virginia. Most of the remaining states have a majority of counties that contain a black population less than 5 percent of the total population, with a few counties in the midwest that have a relatively large black population. Counties that are white indicate that there were insufficient or no data.

     The second map shows the asian population of nearly every county in the United States. Like the first map, the data used in this projection are also from the US Census Bureau, collected as part of the 2000 census. Again, the population of interest (asian) is measured as a percentage of the total population of the entire county. Counties with different asian populations are represented with a different set of colors, and this map utilizes the North American conformal conic projection as well. A light blue color represents a relatively small asian population (less than 1 percent) whereas a dark blue indicates a county with a relatively large asian population (greater than 20%). The map legend breaks the sizes of the asian populations into 6 categories: 0.0085-1%; 1-3%; 3-5%; 5-10%; 10-20%; and 20-50%. Counties with the largest Asian populations are found along the west coast of the US, with a concentration of these counties near the bay area of San Francisco, as well as Los Angeles. The Asian population of the remaining counties is sparse and relatively low, with a few counties throughout the midwest and east coast that have large Asian populations. Counties that are white indicate that there were insufficient or no data.

     The third map shows the population of some other race in almost every county in the United States. Like the other two maps, the data used are from the US Census Bureau from the 2000 census. The population of interest, which is neither black nor asian, is again measured as a percentage of the total population of the whole county. Counties that have different sized populations of "some other race" are again represented by varying shades of a color. Like the other two, this map also uses the same Lambert conformal conic projection on which to display the data. Light green represents counties with a relatively small "other race" population (less than 2 percent), whereas dark green represents counties with a relatively large "other race" population (greater than 20%). The legend breaks the sizes of the "other race" population into 6 different categories: 0.00795-2%, 2-5%, 5-10%, 10-15%, 15-20%, and 20-40%. Counties that have a large "other race" population are found throughout the western portion of the US, especially in the states of California, Arizona, New Mexico, and Texas. Based on existing cultural and social knowledge, one might infer that "some other race" might refer to Hispanic or Latino. Counties that are white indicate that there were insufficient or no data.

     All three of these maps were created by joining spatial data and attribute data. Every map used the same spatial data, which was the Lambert conformal conic projection of the contiguous United States, with the county boundaries present. Each map had its own attribute data, which gave the size of certain populations of interest within counties. Furthermore, each map tells its own story, and certain social or historical implications can be made by a simple analysis of the projections and their data. These three census maps provide an excellent example of the advantages of "joining" data sources, and how GIS can be used to aid in the interpretation and analysis of data of all kinds.  They are easily understandable and straight forward, thus making them universal and intended for all.

     At the beginning of the quarter, I had absolutely no knowledge of GIS, or how it is used. I have a relatively mild understanding of geography, and I like to see myself as more geographically aware than the average person, but I had no prior knowledge of GIS. It quickly became evident that GIS is a critical component of a large number of fields, and has developed alongside a rapidly evolving technological world. My ineptitude with computers served only as a catalyst for frustration, as the labs at the beginning of the quarter seemed exceedingly difficult and time consuming. As ArcGIS was used more and more in each subsequent lab section, I slowly built up my confidence, and simple tasks and map-making in ArcGIS became easier. I now sufficiently understand the important of geographic information systems, and how it is utilized by almost everyone on a daily basis. Additionally, I also have an understanding of the limitations technology places on GIS, and how knowledge of computers is often required for a productive and stress free GIS experience. GIS is critical in how we perceive and analyze our world, and is only becoming more and more important for all of us.

Lab 6

     Four maps were created in this lab section, a shaded relief model that layered a hillshade and a color ramped DEM model, a slope map, an aspect map, and finally a 3D map. The potential user-friendliness of this aspect of ArcMap was almost immediately evident, as the maps were quickly and easily created. Each offers a slightly different visual representation of the same raster elevation data, with one focusing on slope, another on orientation, another on elevation, and another on a three dimensional representation. This lab made it evident that DEMs can be displayed in a vast variety of ways, with just a few previously mentioned. This allows for a multi-faceted approach to analyzing certain types of GIS data, thus broadening the potential applications for DEMs and related GIS programs. Furthermore, they specifically function to improve the efficiency and accuracy of spatial analysis, which is a critical component of GIS. This became strikingly evident upon the creation of the 3D map during the lab, since it presented us with perhaps the most "realistic," and easily analyzed visual representation of the data. However, as with many other facets of GIS, DEMs are not without their pitfalls. These representations are limited to the hardware and software being used, and must also keep up with improvements made in technology. And even though the maps created in the lab were constructed with relative ease, individuals seeking to display some form of elevation data may be limited by their lack of knowledge and unfamiliarity with ArcGIS and other programs. Finally, one might also encounter problems when using DEMs to represent reality. Temporally affected data, such as lakes and rivers that dry out but return at certain times of the year (or the solid ground beneath them) may be difficult accurately represent on a DEM.

3D DEM

Lab 5

     Perhaps the significance of map projections is indicated by their very definition: the way in which we perceive the world. Since it is simply not possible to accurately transform the three dimensional surface of the earth into a two dimensional map, every projection preserves certain physical aspects, while distorting a few others. These simple distortions, while often subtle, can be extremely significant and must be taken into consideration when choosing an appropriate projection. The main elements of a projection that are either preserved or distorted are distances, scale, bearing, direction, area, and shape. The vast number of existing map projections is indicative of the versatility of maps, and their many different uses. Different projections can be used to convey a wide array of information, and a particular map's purpose or function usually dictates what projection will be used.

     One of, if not the most well known map projection is the Mercator projection. It is a conformal cylindrical projection produced by Gerardus Mercator in 1569, and quickly became the primary map used for nautical navigation since a straight line on the map represents a constant course, which is arguably its most significant potential use. However, land masses that are farther away from the equator are more distorted in terms of area than land masses closer to the equator. As the map continued to gain popularity, it became the primary projection of the world typically found in US classrooms in the 20th century. This was critical, as the average person is not cognizant of the many distortions associated with map projections, and could believe that the sizes of certain countries and regions compared to others are accurately depicted. This is entirely false, and the relative sizes of regions like the United States, Russia, Africa, Greenland and Australia convey subtle messages that can subconsciously be interpreted as one region having "importance," or some other critical aspect, over another region. As a result, contemporary atlases no longer use the Mercator projection, and instead use equal-area projections more often than conformal ones. Along with the Mercator, another conformal projection is the Stereographic projection. It is advantageous when mapping the Earth's poles, and also has applications in photography when capturing wide-angle views.

     

     

     Another significant type of projection is the equal are projection. As the name implies, it preserves surface area and accurately depicts the relative sizes of regions. An example is the Mollweide equal area projection, also known as the elliptical projection, which distorts angles and the accurate shapes of regions in order to maintain accurate relative sizes. Created in 1805 by astronomer Carl Mollweide, this particular projection is advantageous for mapping the entire globe, as well as the sky. As a result, the Mollweide projection is found in a great number of 19th century star atlases, and is frequently used to display astronomical observations, such as cosmic microwave background radiation in full-sky format. An additional type of equal area map is the Bonne projection. Named after Rigobert Bonne, this pseudoconical projection displays every parallel as an arc of a circle. Like the Mollweide map, the Bonne projection maintains accurate area, and distorts shape. These distortions are more severe farther from the center of the map, but are minimal towards the center. The scale is accurate along the straight, central meridian line.

     

     Along with the equal area projections, another widely used projection is the equidistant projection, in which the distances from a standard line or point are preserved. One example is the azimuthal equidistant projection, which may have been used by ancient Egyptian star maps dating back to the 11th century. The main advantage of this projection is that all points on the map are at correct distances, as well as correct angles from the center point, which is commonly the north pole. This has various social advantages, as the United Nations uses this particular projection in their emblem, since there seems to be no implied "primary" countries or regions. The distortion of shapes and areas is present in this projection, and they are more extreme farther from the center of the map. Another equidistant projection is the conic equidistant one, which is derived from a conic section of the earth's surface. Distortion is minimal along the central parallel, and is constant along any given parallel. This type of projection is useful for displaying only one hemisphere, and is neither equal area or conformal. These six map projections described offer only a brief look at the vast variety of modern and old projections.

Lab 4

I was unable to complete the lab due to technical difficulties. I reached page 22 in the tutorial. Since I wasn't able to paste an image of the end result, I decided to include what I was able to complete:




      After attempting to complete this lab and experiencing ArcGIS for the first time, I have a much better understanding of the potentials and pitfalls of GIS. Perhaps the most apparent potential advantage of GIS is its availability, as anyone can go online and download the programs. Interacting with the program was a rather interesting experience, and the instructions in the tutorial were mostly easy to follow. Not too long after starting the tutorial, it was apparent that there are likely a great number of applications for ArcGIS that go beyond the relationship of airport land and sound contours. ArcGIS allows for the conveyance and display of information that can easily be understood by many people. If the information being conveyed is urgent in nature, for example the extent of damage of some natural disaster, GIS allows for a relatively quick data analysis and has the potential to be critical in certain urgent situations. 

      In conjunction with making information easily displayable to the general population, GIS encourages the sharing and spreading of information since it's part of a web 2.0 that is becoming more and more critical to society. Since almost anyone has access to GIS, and because it has applications in many different fields, including the sciences, engineering, and many more, it's easy to see why GIS would be an efficient medium through which to spread and distribute vital information. ArcGIS is only part of a rapidly growing network of geographic information systems which ultimately makes our lives easier and more efficient.

      However, in addition to the various potentials of GIS and ArcGIS in particular, there are also a number of pitfalls and disadvantages. Although the programs themselves are widely available, the average person may lack the necessary computing skills that would make the GIS experience easier and more pleasurable. ArcGIS assumes the user has a certain knowledge of computers, even in the tutorial, and a person lacking these skills and knowledge may encounter some difficulty using the GIS. Furthermore, reliance on technology presents its entirely own set of potential problems. I experienced this when I was unable to retrieve the data I had saved on my flash drive when I tried to continue my work on the lab. Problems like these are exacerbated by a lack of computer knowledge. 

      As GIS is primarily reliant on technology and computers, another possible pitfall is compatibility. Since the world of technology is constantly evolving and improving, GIS must also keep up. Advances in computing warrant similar upgrades for GIS. This requires continuous maintenance for the creators and editors so that the programs run most efficiently.

Lab 3

NOTE: ZOOM OUT TO SEE ALL LOCATIONS!!!


View Aviation Nation in a larger map

         There is no doubt that neogeography has changed the way in which people live and interact with each other. As just a small component of an ever-advancing technological world, neogeography has many positive implications and uses, and has the potential to (and does) vastly improve our lives. Perhaps the most noticeable advantage to the average person is neogeography's revolution of navigation. Navigation has been made more precise, more accurate, faster, and more easily accessible with the existence and prevalence of geography systems in cell phones, computers, GPS devices in cars, and many other devices. With the aid of technology, it is almost difficult for one to be lost in this modern era of navigation. Additionally, law enforcement and EMS can utilize GPS tracking systems in cell phones and other devices to find a criminal's last known location, locate an individual in need of help, or perform a variety of other tasks that are helpful to society. Furthermore, the existence of the internet has given the average person a stronger voice in the process of online and interactive mapmaking, as a sort of "democratization" of mapping has emerged in the past few years. There are instances in which people can vote for the names of new bridges, roads, and other structures or places that will soon be featured on new maps. This sort of interaction leads to an increased geographic awareness of the average person, and serves to highlight and promote the importance of GIS and neogeography alike. As more and more individuals become geographically aware, we can more easily improve upon existing geographic systems and technologies that serve to help us all.

          Of course there are downsides and pitfalls to a rapidly evolving society in which neogeography is so prevalent. Privacy is one of the main concerns, as neogeography can sometimes be invasive in nature, in that an individual's location may be broadcasted against his or her will on a social networking website. In addition to this, criminals, or other individuals with nefarious intentions can easily take advantage of knowing the location of a person of interest, which is often broadcasted in real time. It is not uncommon for companies such as Apple to track customer's locations and browsing histories to use for advertising purposes, and there is currently a heated debate as to whether or not certain government tracking practices are unconstitutional. In addition to the individual suffering from a lack of privacy, modern governments should expect the world to know of radar facilities, or other installations with sensitive locations, should they be in plain view on the surface of the earth. Aside from privacy concerns, the exponential increase in technological innovation has caused a similar rise in the expectations of these technologies and how they perform. It's easy and common to become angry in response to a navigation system giving slightly less than perfect directions. Finally, the cost of technology relating to neogeography is something to consider, as technological innovation is rarely cheap, and has vast financial implications on companies or agencies working to contribute to a rapidly evolving world.

Lab 2

1. Beverly Hills Quadrangle
2. Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, Inglewood
3. 1995
4. The North American Datum of 1927 (NAD 27)
5. 1: 24,000
6. a) 5cm x 24000 = 120000/100 = 1,200 meters
    b) 5in. x 24000 = 120000/63360 = 1.894 miles
    c) 1mile x 5280ft x 12in. = 63360/24000 = 2.64 inches

    d) 3km x 1000m x 100cm = 300000/24000 = 12.5 cm

7. 20 feet

8. a) 34°04'23" , 118°26'23"   34.073 , 118.440

    b) 34°00'25" , 118°30'00"   34.007 , 118.500

    c) 34°07'05" , 118°24'35"   34.118 , 118.410

9. a) 560 feet   170.7 meters

    b) 140 feet   42.7 meters

    c) 740 feet   225.5 meters

10. Zone 11S

11. Zone 11 36200m E , 3776000m N

12. 1,000,000 square meters

13.

 

14. 14°48'

15. From north to south

16.