Week 9 LAB

http://earthobservatory.nasa.gov/IOTD/view.php?id=40118

http://quickfacts.census.gov/qfd/states/06/06037.html

 http://www.fire.ca.gov/communications/downloads/fact_sheets/20LACRES.pdf

The two maps here provide information on the extent and possible consequences of the 2009 Los Angeles County Station Fire.  The first map provides a look at L.A. County and the extent of the fire over its entire course from August 29th until its containment on September 2nd.  The second map shows a closer look at the area around the fire, specifically it's proximity to major L.A. County hospitals. Both maps include a digital elevation legend in meters as well as a reference overview of the station fire parameters within the Los Angeles County border.

The Los Angeles Station Fire of 2009 is the 10th largest California wildland fire with 160,557 acreage burned over the course of just five days.(1)  Furthermore, it is the largest fire ever in the Los Angeles National Forest, which demolished nearly 209 structures and took the lives of two firefighters working to contain the blaze.(2)  

What made the station fire particularly difficult to contain is the fact that the fire burned in an area littered with steep cliffs and canyons.(3,4)  Firefighters struggled to deal with the difficult terrain of the LA National Forest as the fire continued to edge closer to local urban communities of Los Angeles, one of the most populous urbanized areas with nearly 10,000,000 inhabitants.(5)

The second map here shows the proximity of the fire to Los Angeles hospitals in order to assess how the fire could have effected those health centers closest to the L.A. National Forest.  The three closest health centers (Verduga Hills Hospital, the Pasadena Impact Drug and Treatment Center, and Pacifica Hospital of the Valley) all fall within a range of 1.289 to 4.789 miles to the closest point of the blaze.  The map shows just how close these hospitals were to the fire, which would have had catastrophic consequences should the blaze have forced an evacuation of patients and medical personnel.

The Los Angeles Station Fire proved to be one of the largest and most destructive wildfires in the history of Southern California.  The maps above show just how catastrophic the blaze could have been to the greater Los Angeles community had the fire not been contained as quickly as it was.

(1) "20 Largest California Wildfires". Accessed December 6, 2012. http://www.fire.ca.gov/communications/downloads/fact_sheets/20LACRES.pdf

(2) "Station Fire". Accessed December 7, 2012. http://inciweb.org/incident/1856/

(3) "Fires in Los Angeles County". Accessed December 6, 2012. http://earthobservatory.nasa.gov/IOTD/view.php?id=40118

(4) "Station fire claims 18 homes and two firefighters". Garrison, Jessica. Zavis, Alexandria. Mozingo, Joe. Los Angeles Times. August 31, 2009. http://articles.latimes.com/2009/aug/31/local/me-fire31

(5) "Los Angeles County, California". Accessed December 7, 2012. http://quickfacts.census.gov/qfd/states/06/06037.html

Week 8 LAB

The first map in this post represents census data from 2000 showing the density of the Black population throughout the nation.  The darker green areas represent a higher black population in those particular counties, which are clustered around the South and Southern-Eastern coastline of the United States. The darkest green counties clustered across the southern areas of the nation can have as high as an 86% Black population density.

The second map shows the density of Asian populations in the continental United States.  The darkest blue areas representing the greatest density of Asian population are clustered in the coastal California regions of the Bay area and Los Angeles areas with some further clustering in the New England territories of the United States with some counties retaining between a 25-42% Asian population.

The final map in this post documents the population density of some other race across the United States.  The darkest orange areas in the mid-western states of California, Arizona, New Mexico, and Texas contain numerous counties with the highest density of this particular population. As much as 42% of the population in these dark orange counties are made up of this other race.

Taken together, these three population maps provide an interesting picture of the continental United States as related by racial population density.  The areas with the highest population density of any given racial group (represented by the darkest counties in these maps) have very little overlap.  With the exception of the high Asian and other race populations in California, all three high density clusters in these maps are located in three different areas around the nation.  For the Black population, this area is the Southern U.S. For the Asian population, this area is coastal California and the Northern East coast. Lastly, for the other race population, the high density populations are largely clustered in the mid-western part of the United States.

The use of GIS in the creation of these maps makes analysis of population density across the nation easier.  The process of creating these GIS, particularly the combining of Excel data onto the ArcGIS program, proved difficult. However, the end results shed light on just how beneficial GIS can be when analyzing different areas or trends.

Week 7 LAB

 

The use of the ArcGIS program in the creation of these four map models (Shaded Relief Model, Slope Map, Aspect Map, and a final 3-D Map) reveals some of the pitfalls and potential of using ArcGIS for spatial analysis projects.  When looking at these three models together, one can get a better picture of how spatial data relates.  This would prove to be especially beneficial in situations where one wanted to look at the terrain of a given elevated area in order a building project, road, or pathway. However, this potential is somewhat marred by a possible pitfall of using the program for these purposes.  The use of this program for spatial analysis is limited to what one can read from the given data fields, but this does not necessarily lend itself to figuring out the best possible use of the land unless all known variables are analyzed. Aspect, slope, ect. are not the only variables that come into play and ArcGIS may not be able to adequately present all of the necessary data projections for complete spatial analysis.

Week 6 LAB

For this week's lab, the mapping of six different types of globe projections revealed the attributes and limitations of three types of projections: conformal, equidistant, and equal area. Two examples of each of the three major types of projections are posted to provide examples of variances within the specific types of projections.

The first set of maps, Mercator and Stereographic, are examples of conformal projections.  These projections are useful in that they preserve all the angles within a localized area. Specifically, the Mercator projection is used commonly, despite the severe area distortion of land masses like Africa and Greenland, making the two seem like they are roughly similar in size, when they are not in reality.  Although right angles may be preserved in conformal projections, the occurrence of such area distortions are a significal pitfall of these particular map projections.

In the second set of maps, Equidistant Conic and Equidistant Cylindrical, are specific examples of equidistant map projections.  In these projections, angles and area may not be preserved on a map, but the distance from a particular point or set of points are preserved.  This may prove especially useful when the primary goal of a map projection is to ensure that point distances are maintained above all else (as in this lab where the distance between two cities, Kabul and Washington D.C., is a key component).

The final set of maps, Cylindrical Equal Area and Sinusoidal, are equal area projections.  Unlike the previous two projections, these maps work to preserve global areas. However, there is a trade off as angles and distances may be distorted in various places on the map.  The three projections all trade off on accuracy on issues of distance, area preservation, and angle preservation.  However, if one is familiar with the potential benefits and inaccuracies that come with each particular projection, one can pick a projection that best accomplishes whatever goal the map is meant to achieve.

Week 4 LAB

Working with ArcMap throughout the course of this week’s lab was a completely new experience.  I do not consider myself in any way computer savvy, so working my way around a brand new program to complete a lab on a subject I am only recently acquainted with proved to be challenging.  However, the ArcMap program and the tutorial that came with it made the process easier.  Although it took me a period of time to become comfortable with the layout and basic functions of ArcMap, by the end of the tutorial, I found the program to be very user-friendly even for GIS novices like myself.

Certain features within the ArcMap program, like the “Zoom to Layer” application, were irreplaceable time savers.  One of the biggest challenges upon starting the lab was accidentally zooming in too close on certain data field layers or even completely losing them in whatever particular field view I had placed myself in.  Similarly, the table of contents displaying all of the different data frames and layers proved to be an organized and efficient way of keeping track of the different data sets that I was working with throughout the lab.  Other actions asked of me during the tutorial, like joining two tables together, required outside assistance.  Luckily, once the process was shown to me I was easily able to repeat the process again the next time I completed the tutorial.  Overall, the ArcMap program was interesting to work with.  Although I was worried about exactly how much I did not know how to do when I started using the program, I now feel comfortable carrying out the tasks asked of me throughout this lab.  

Using ArcMap has helped to reveal some of the various potentials and pitfalls that surround the use of this program in regards to GIS.  The extensive tools and functions within the program prove to be extremely beneficial when working on data analysis.  Specifically  with this lab, looking at population density within the noise contour of an airport would allow for analysis of the extent to which airport noise effects the areas surrounding the airport boundary.  This could prove useful when deciding what to build in the areas within the noise contour.  For example, one particular school (Northwestern Prep) fell within the boundary of the noise contour of the airport.  Should the county decide to build another school, they can take a look at the data frames built within the ArcMaps program so that their school is not placed within the noise contour boundary like Northwestern Prep was.  This is an example of the potential that GIS and the ArcMap program both have in helping with analysis of real scenarios.

The use of the ArcMap program also revealed some pitfalls that befall GIS.  Although the program has made it so that anybody can work their way through the program, that does not mean that the capacity for human error goes away.  While the program is relatively easy to use with time and practice, it is not a perfect program.  There are still scenarios where human error can distort any potential gains from GIS analysis.  For example, the ArcMap program has a feature that allowed me to extend a specific road near the airport and even build a brand new one.  The first time I attempted to complete this feature, I built my road incorrectly.  The program made it simple to extend the existing map roads, but I was still able to erroneously utilize that function the first time around.  Had I not fixed this problem, this could have led to some misinterpreted GIS analysis should someone be analyzing the roads layer on the ArcMap data frame.  The capacity for human error is not something that the ArcMap program can completely do away with and this provides a potential pitfall in its use with GIS.

Week 3 LAB

View JusticeCorps Courthouses in a larger map This map is a trip around the Los Angeles County courthouses where I volunteered with self-represented litigants in cases involving housing law and family law from August 2011 until July 2012. With each courthouse, I will detail a humorous anecdote of some of my favorite litigants that I assisted during my year with the JusticeCorps program. It begins with the red map marker at the Los Angeles Downtown Superior Court and continues on until it ends at the pink marker signifying the Compton courthouse.

My personal neogeography map retains some of the pitfalls, potential, and consequences that befall all neogeographic maps. To start, a pitfall of neogeography is the questioning of authority and content of the map information. In the specific case of my L.A. County courthouses map, one may question my authority on creating a map with this content. Particularly problematic for some is the gray lines used to document my transition between courthouses. These lines are not meant to reflect an exact route between two given courthouses, but rather, simply to show the general movement that I followed throughout the year. However, the personal nature of neogeography means that another viewer may or may not question the use of these lines and read some incorrect information based off of what I have chosen to portray.

One of the greatest potentials of neogeography involves the widespread movement of information and mapping knowledge across the public sphere. Everyone is now capable of building their own neogeographic map. My personal map has the potential to tune into the good works and resources of either the JusticeCorps program or the many courthouses of the L.A. district in general. Lastly, a general consequence of neogeography is the cost of decreased privacy, which comes with the increase of navigational information. Personally, this map reveals where I have worked, what I did while I was there, and who I interacted with. Although I may not take issue with this consequence in this scenario, it is still a serious consequence for other neogeographic maps.

Week 2 LAB

1. Beverly Hills Quadrangle

2. The seven adjacent quadrangles are Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, and Inglewood.

3. The Beverly Hills Quadrangle in this map was first created in 1995.

4. Various datum was used to create this map including the North American Datum of 1927 and the North American Datum of 1983

5. Map Scale - 1:24,000

6.    a.) Conversions:       1 cm: 24,000 cm; 5 cm: 120,000 cm; 5 cm: .05 m; .05 m: 1,200 m.
                    5 cms on the map is equivalent to 1,200 meters on the ground.

       b.) Conversions:        1 in = .0000158 mi; 5 in (map): 120,000 in (ground); .000079 mi (map): 1.896 mi (ground)
                     5 inches on the map is equivalent to 1.896 miles on the ground.

       c.) Conversions:      1 mi = 63,360 in.; .00004167 mi (map) = 1 mi (ground); 2.64 in (map) = 63,360 in (ground)
                      1 mile on the ground is equivalent to 2.64 inches on the map.

       d.) Conversions       1 km = 100,000 cm; 1 km (map) = 24,000 km (ground); .000125 km (map) = 3 km (ground); 12.5 cm (map) = 300,000 cm (ground)
                      3 kilometers on the ground is equivalent to 12.5 centimeters on the map.

7. Contour interval on the map is 20 feet.

8.    a.) Public Affairs Building:
               Longitude: 118d, 26', 25"W OR 188.440278
               Latitude: 34d, 4', 8"N OR 34.068889

       b.) Tip of Santa Monica Pier:
               Longitude: 118d, 0', 29"W OR 34.008056
               Latitude: 34d, 30', 1"N OR 118.500278

       c.) Upper Franklin Canyon Reservoir:
               Longitude: 118d, 24', 40"N OR 118.4125
               Latitude: 34d, 6' 58"W OR 34.11611

9.    a.) Greystone Mansion: Approximately 555 ft. OR 169.164 m.

       b.) Woodlawn Cemetery: Approximately 138 ft. OR 42.0624 m.

       c.) Crestwood Hills Park: Approximately 625 ft. OR 193.548 m.

10. UTM Zone 11S

11. UTM coordinates (Lower Left Corner): 3762950N, 361500E

12. 1,000m x 1,000m = 1,000,000m^2

13.

14. The magnetic declination of this map is 14d east.

15. The stream between the 405 freeway and the Stone Canyon Reservoir flows from North to South.

16. A map of UCLA...

Week 1 LAB

Map #1
Source:http://www.wjla.com/
http://www.wjla.com/blogs/weather/2011/12/odds-of-a-white-christmas-for-d-c-increase-slightly--14050.html

This map shows the probability of any given area in the United States having a "White Christmas." For this map, a White Christmas is defined as a location receiving at least one inch of snowfall on December 25th. What I found interesting about this particular map are some of the inconsistencies found within the more general pattern of increased probability of snowfall the farther north in the United States an area is. A fact demonstrating these inconsistencies is that there is a higher probability of snowfall in the Midwestern regions of Idaho and Wyoming, than a large area of Montana, a state to the north of Wyoming. This begs the question of why this significant area in Montana has a lower probability of a White Christmas compared to other states at similar latitudes like North Dakota and Michigan.

 Map #2

 Source:  http://drhiphop85.com/
http://drhiphop85.com/2011/06/02/u-s-stereotype-maps-movies-tv-shame-and-awesomeness/

This map labels each of the 50 states in accordance with a relatively famous film that took place in that particular state. Although a map like this is subject to the preferences and biases of the map's creator, a wide variety of film genres are represented here, including comedy (There's Something About Mary), horror (The Evil Dead), and family films (The Goonies). This map is interesting to me due to some of the plot patterns to be found in films of certain parts of the United States. For example, many of the films chosen for the Midwest revolve around a plot driven in some part by the rural agricultural landscape. Whether this plot revolves around evil farmer children (Children of the Corn in Nebraska) or tornadoes (Twister in Oklahoma and The Wizard of Oz in Kansas), they are brought about in some way by the flattened wide open spaces of the Midwest.  Furthermore, many of the films located in the Northeastern New England states have a general similarity in that their plots revolve around troubled people doing awful things to one another just because they can, as shown in the cheating spouses of The Ice Storm, the mafia hits in The Departed, and Bill Murray being rude just for the sake of it in Groundhog Day).

Map #3



Source:  http://watchdog.org/category/kansas/
http://watchdog.org/36805/ks-kansas-among-highest-in-education-jobs-per-k-12-student/

This map shows K-12 employment of educators per state for the year 2007. The map gives the percentage of education employees per 100 students, the placement of that state relative to the other 50 based on that percentage, and color codes each state depending on which quartile the given stats place that state in.  What is interesting about this map is the fact that the western United States consistently falls in the bottom quartile while the states up in the northeast place into the top quartile.  This begs the question of why one side of the United States succeeds in hiring out a greater number of educators per student and the other lags behind significantly.  Furthermore, are the students in the states with higher percentages of teachers per student receiving a lesser education in accordance with the figures given in this map. However, when one takes a closer look at the numbers given for each state, one can see that Washington, which placed last is only approximately ten percentage points behind Wyoming, which placed first. This observation supports further research into whether the minimal point differences between the states have a significant effect on the overall education of the students.