Final Lab

The distribution of Asian population is shown in red and reveals a heavy concentration in the West Coast. In further detail, it seems that the majority of Asians are located in Washington and California. In fact, the only counties that contain 20% or more Asians can only be found in these two states. Throughout the rest of the continental United States there is the occasional county that will contain 4% or more Asians in their population.

The distribution of Black population is shown in blue. The primary concentration of Blacks is in the South, in states like Texas, Louisiana, Georgia, and Florida. However, there are quite a few counties in California that have a Black population in the 8-20% range.

Lastly, the distribution of some other race is shown in green. This category accounts for people who consider themselves interracial or Hispanic/Latino. This group is primarily concentrated in the South Western region of the United States.

In conclusion, the racial analysis of reveals some interesting facts about how the United States’ population is distributed. These three graphs illustrate that the more “ethnic” counties are located along coast, with Blacks more towards the Southeast while both Asians and Others are more apparent in the West. It can thus be inferred that the population distribution in the middle and Northern regions of the continental US is dominated by Whites. Also to note is that, in comparison to Asians, Blacks and Others occupy more counties in higher concentrations. There are many more darkened Green and Blue counties than Red.

Geography is such a practical subject because it forms connections between other areas of study and the real world. GIS is a mechanism that can create these connections through a set of analytical tools. GIS allows users to perform your own analysis to suit the issue or problem at hand. The only setback of GIS, that I had noticed when dealing with GIS systems, is the preparation of data. It seems difficult to prepare raw data to be able to input it in GIS programs like ArcGIS. Aside from this last assignment, we never even touched base on the creation of shape files or data files. However, aside form this aspect of GIS, my impression of this subject is a positive one. It can show the environmental effects by delivering a map of the effect of the BP oil catastrophe or illustrate the wealth distribution within the United States. GIS reveals patterns in information that would otherwise be invisible. This is where the value of GIS comes from.

Week 8 Lab

Both maps illustrate the spread of the Los Angeles Station fire over a time span from August 29-September 2, 2009. This fire was the most devastating fire of the California fires in 2009, burning over 160,000 acres and killing two fire fighters (LA Times). It is also important to note that the fire destroyed 89 residence, 94 out buildings and 26 commercial properties (KTLA news). I must emphasize this information because it comes into conflict with the spatial analysis I conducted. However I will give possible reasons for such discrepancies because they are too large to ignore. The fire engulfed the northern part of Los Angeles County, just North of Pasadena, and burned a considerable portion of the Los Angeles National Forest.

There was some controversy surrounding the fashion in which the Los Angeles Fire Department dealt with the Station Fire. Some postulated that there was a lack of resources due to budget shortfalls. However, the Forest Fire Chief at the time ensured that any lack of deployment of resources was based on their belief that the fire could be easily contained (KTLA news). Initially, only three helicopters and limited ground forces were deployed to combat the fire. Soon, it was realized that this was not enough. As the maps show, the fire grew tremendously in the 2^nd day. This expansion was fueled by the Santa Ana winds, which are notoriously strong in this area. The end all cost of this fire worked up to a hefty sum of $83 million (KTLA news).

The reference map incorporates a variety of data. As a base, it shows the county area in yellow. From here there is major highway, river, water body, city and park data overlain to better orient the viewer. There wasn’t any elevation data added to this map, however, a viewer can indirectly determine the mountainous regions based on the river data.

The thematic map measures the households that were affected, over time, by the Station fire. The spatial analysis I conducted highlighted the population data that was within the area the fire covered. I chose to focus the analysis on the number of households that were affected by this fire. If you refer to the excel graph included in this report, the number of houses affected reaches upwards of 800 (UCLA Gis Pop Block Data). In this map, each data point does not represent an individual household. Instead, it is a value that represents the number of households in close proximity. I am unaware of how strongly these values are tied to their geographic location, but this presents some problems that I will discuss.

As I referred to before, the number of households affected by the fire as determined by my analysis was drastically different from the actual reporting. 800 is nowhere near 209, which is the official record. This error could be a result of the time the data was measured, which was in the year 2000. However, this is unlikely because the population or the number of households is likely to have gone up since then. The more likely reason for this discrepancy is the spatial inaccuracy of the data. It is probably the case that the points from the population data don’t precisely correlate with its geographic location. Furthermore, some of the houses highlighted by my analysis could have been within the area of the fire but remained unaffected.

Bibliography

“On the Fire Lines.” LA Times. URL: http://www.latimes.com/news/local/la-me-bigpicturefire,0,5985825.htmlstory

“Report: Number of Firefighters reduced before Station Fire” KTLA News. October 9, 2009 URL: http://www.ktla.com/news/landing/ktla-angeles-fire,0,5292469.story?page=2

UCLA Gis URL: http://gis.ats.ucla.edu//Mapshare/Default.cfm#

“2009 California Wildfires.” Wikipedia. URL: http://en.wikipedia.org/wiki/2009_California_wildfires

Week 7 lab

The geographic location of the area covered by these DEMs is Lake Tahoe and its surrounding mountains. The data collected from the USGS website had some errors. If you take a look at the lake in the Color Ramped Shaded Relief and Aspect maps, it shows varying “strips” of data across the lake. This is most likely caused by measurements taken at different times or perturbations in the water. Also to note, the DEM maps include parts of both California (Western side) and Nevada (Eastern side). Furthermore, there is a buffer area around the lake that captures most of the high elevation areas. The range of elevation spans from roughly 1400m to 3300m. The extent of this map from West to East is –120.304 to 119.720, and from North to South it is 39.345 to 38.880. Lastly the geographic coordinate system used for these DEMs was the GCS North American 1983.

Week 6 Lab

The average map on-looker has no idea that maps come in various projections, each of which offers its own advantage. Most would never question how a flat map could represent curvaceous objects like our planet Earth. In this sense, maps are very versatile and can serve many different purposes. This project reveals the different advantages, and disadvantages thereof, that a map can offer. This becomes much more apparent when comparing the distance between DC and Kabul that each map projects. This exercise required us to include two maps that are conformal, two that are equidistant and two that are of equal area. Each serves its own purpose.

Conformal maps, by definition, preserve the angles between the curves that the map is oriented about. This is said to produce more accurate shapes and angles on the local scale. However, it does not necessarily maintain equal area nor equal distance. Of the 6 map projections, the Mercator and Gall Stereographic are the two exclusively conformal maps. Despite the accurate representation of angles, these conformal maps distort both distance and area. However, a map like the Robinson projection map is neither equal area nor conformal, which is said to bring about a better view of the entire world. In conclusion, there is no perfect projection.

The advantage of Equidistant maps is fairly obvious; the distances from the center of the projection to any other place on the map are uniform in all directions. This is important when the spatial analysis of a map requires accurate distance readings. An example of this would be the range of missile attack that a country is capable of. Wouldn’t you want to know if your country was within range? The Equidistant Cylindrical and Equidistant Conic projections are the two equidistant maps. Even though they produced two different distances for DC to Kabul, the distance from the center should still be uniform in all directions.

Lastly, Equal area maps are advantageous when area is of analytical importance. For example, if one were to compare the amount of fertile soil two different countries have, equal area maps would produce the most accurate results. The Bonne and Cylindrical equal area maps are the two that maintain equal area. Despite their distortions in shape, they produce accurate representations of area. In conclusion, each map distorts some features while keeping others much more accurate. Choosing the appropriate projection is a matter of what feature is important to you.

Week 4-5 Lab

Note: The computer I used in SSCL wouldnt allow me to pull or manipulate data from my USB (I had this problem in class). This created a problem for the third exercise. I did all the steps correctly however it refused perform the calculations for POP_Den. It just gave me for all the values. That is why my bottom graph doesnt show any green area, and I couldnt include a legend as a result.

Potential and Pitfalls of ArcGIS

How broad the ArcGIS proved to be during my experience with it thus far has truly amazed me. The program’s ability to run calculations, generate graphs, incorporate aesthetic features, and perform spatial analysis all goes to show that it is very expansive. The program seemed like it took Microsoft Excel, Adobe Illustrator, Microsoft Word and mashed them up into a single map-oriented program. I thought each of the exercises would delve into finer details; however, this was not possible because ArcGIS has so many different applications to offer. Each of the 4 exercises served to merely scratch the surface of a different function that ArcGIS can perform.

However, the breadth of this program has its consequences. For a beginner like myself, the range of applications in this program creates a steep learning curve. The program still felt unfamiliar after going through the exercises multiple times. Furthermore, even though the tutorial easy to follow, it seemed like, as I said before, it only scratched the surface. The more advanced settings and features that the tutorial shied away from were very daunting to just look at. The program is not necessarily user-friendly in the way GoogleMaps is; however this pitfall is easily cured by experience and practice.

The dynamic performance of ArcGIS is another extremely helpful attribute of this program. ArcGIS is not a map. It has the ability to perform spatial analysis, recalculate and focus in on any details the mapmaker wishes. After finishing my map, I realized that I could boot the program back up, make a few slight adjustments, and present a map that had a completely different intended purpose. The way in which ArcGIS enables you to zoom in and out and recalculate information on different dataframes (the county boundary minimap) was tremendously useful.

I noted another setback during the fourth exercise. Even though the program offered tools to improve the look and feel of the map with colors and drop shadows, it still lacked some graphical splendor. This is of course irrelevant in an academic context because scholars are primarily concerned with the spatial analysis or accuracy of one’s work. However, eye-catching graphical displays are good in that they grab the attention of on-lookers. An informative map is not of much use if people don’t look at it. I have only worked with the program for a few hours so I am not certain that it lacks this feature, but it is one that I noticed.

A major setback of GIS is that it relies on inputs of data, which is either primary or secondary. It is advised to use more secondary data because it will save you time, effort and materials. However, this presents some problems. It could be the case that no secondary data exists on the topic of your research which forces you to directly capture your own data. Also, any secondary data that you do use could be inaccurate or outdated. In this sense, GIS can be either very dependent or time consuming and that is one of its pitfalls.

The potential of GIS has to do with its analysis. Humans often lack the capability to point out patterns when faced with an array of data. It usually require pleasant visual displays to point these patterns out. Sure it is possible to comprehend a number (ie the number of gallons of oil spilled in the ocean) but GIS allows viewers to better grasp the effects of the data your are dealing with.

Week 3 Lab: Aston Villa Map

View Aston Villa 2009/2010 Season in a larger map

The Pitfalls of Neogeography

The primary pitfall of neogeography has to do with its validity. Much like how teachers admonish students to not rely on Wikipedia as a reliable source of information, neogeography suffers from the same problem. By definition, neogeography is a usercentric set of tools, ie. Google Maps, that are far less technical and complex as the tools that traditional Geographic information systems use. Consequently, there is a plethora of information that is uploaded onto the web, which has not been checked for its credibility. For example, when I was creating my Aston Villa map, I could have easily included typos or misinformation that would have misguided potential viewers.

The second most apparent setback of neogeography is its technical limitations. Neogeography uses basic user-friendly tools in order to enable people who have no experience in GIS to engage in map making. I’m no expert with Google Maps, but I was frustrated that I could not include a description to complement a video that I included for the Bolton Wanderers versus Aston Villa game. These simple tools can never reach the depth that GIS programs like ArcGIS can offer. In that sense, the information derived from neogeography is mostly confined to non-academic purposes. It can be interesting to see a person’s journey through Europe and the different sites they stopped at, but something like that can rarely serve a higher purpose.

USGS Topographic Maps Assignment

1. The quadrangle is named: Beverly Hills, CA

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

3. The quadrangle was first created in 1966.

4. The Datum used to create this map is called the North American Datum of 1927, the North American Datum of 1983 and the National Geodetic Vertical Datum of 1929

5. The scale of the map is 1:24,000

6.

A. 1200m

B. 1.893miles

C.2.64 inches

D. 12.5cm

7. The contour interval is 20 feet

8.

A. Public affairs location: (34˚4’30”N and 118˚27’0”W) or (34.075˚N and 118.45˚W)

B. Tip of Santa Monica Pier location: (34˚0’30”N and 118˚30’0”W) or (34.008˚N and 118.5˚W)

C. The Upper Franklin Canyon Reservoir location: (34˚7’15”N and 118˚25’30”W) or (34.12˚1N and 118.5˚W)

9.

A. Greystone Mansion Elevation: 600ft or 196.85m

B. Woodlawn Cemetery Elevation: 140ft or 42.67m

C. Crestwood Hills park Elevation: 680ft or 207.26m

10. The UTM zone is 11

11. 3763000m North 362000m East

12. 1,000mx1,000m= 1,000,000m^2. Each cell square contains 1 million square meters.

13.

14. 14˚east

15. From North to South

16.

The World at Night

Here is a map that came from a website named http://rosalieee.files.wordpress.com, under the title "interesting maps." It is a sattelite image of the world at night, which captures the light different cities from around the world give off. The more light an area gives off refers to its level of urbanization. It is only natural that the more densely populated and wealthier areas of the world give off the most light. The Eastern U.S., for example, is probably the brightest location in the world at night. Most of Europe is on par with the U.S. The one area that stands out among the rest is Japan. This map interested me because there was no input of data, instead it is merely a physical image. Nonetheless, the map gives us many insights about the world and reveals interesting patterns to us. Even the most simple maps can be the most revealing.

How Easy is it to Find a Doctor?

This map comes from the Doctors of the World organization, which is a non-governmental humanitarian aid organization. The map shows the countries of the world represented by the ratio of doctors to inhabitants. Generally speaking, the Western countries have ratios around 300 inhabitants to every doctor. However, around Southeast Asia and Latin America, this ratio increases to about 900 inhabitants for every doctor. The reason I chose to include this map was that it showed certain, most likely poverty stricken, areas of Africa which have ratios that reached up to 50,000 inhabitants for every doctor! This map emphasizes the disparity in living standards that people enjoy all around the world. Health is an important issue for everyone and it is frightening to see such astounding data.

US Income Distribution

This map comes from the U.S. 2000 census and shows the income distribution (unequal or equal) throughout the United States. The general pattern of this map reveals that the income disparity is highly correlated with a state's North/South orientation within the U.S. It seems that the Southern states tend to have a higher income disparity. With the exception of Nevada, most of the equal distribution states are clustered in the Northeast. This map appeared interesting to me because it has a loose connection to the politics of those states. The democratic states generally have a more equal income distribution within their population.