Geog7 Lab #8

Mapping the Census

In the final lab of Geography 7, our task was to complete three GIS maps that used population data from the Census Bureau to calculate the population density of certain races and elasticities across the continental United States.

The map above shows the population density of Black people in the continental United States by counties. As you can see from the map, they are clustered mainly in the Southern and North Eastern Coast of the US. Apart from coastal areas, you can also see that along the Mississippi river, there is also clustering of Black populations that follow the river. There are some counties in the Midwestern US that have almost no Black populations, and as we move to the West coast, the second largest cluster of Black population lies in the State of California. This phenomenon can be explained by history in which Blacks were brought to the South to work in plantations, and this is why there is a high Black population there today.

The map above shows the population density of Asian people in the continental United States by counties. The largest cluster of Asian people is on the West coast  in California, and the second largest cluster lies in North Eastern US near Connecticut and Massachusetts. Again, there are very few Asians living in the Midwest. Asians came into the United States as immigrants, and logically would cluster around the most developed regional economies of California and the North East.

The map above shows the population density of other races in the continental United States that make up a significant part of the population. Since Hispanics make is the second largest ethnic group in the US, I would assume that this map is generally showing the population distribution of Hispanics around the US. From the map, the largest cluster are in California, Arizona, New Mexico and Texas. This follows well from the previous assumption as these States all border Mexico, which is where a lot of Hispanics enter the US and thus, have stayed around the area. Other population centers are in the North East and Florida, both of which, again, are large regional economies.

GIS Course Summary
I started the course because a friend of mine recommended I take a course in GIS if I am interested in the Environmental Science field, and I do not regret taking her advice or this course. I feel like I've been introduced to something huge, something that can be applicable in many different fields, and my appreciation for spatial knowledge and maps in general has also grown significantly. If I were to have taken this course earlier on in my college career, I might have decided to take the GIS minor and pursue a career in this direction, but regrettably, this is also my last quarter at UCLA. I do not know whether or not I will pursue GIS after I graduate, but surely, this class has opened my eyes to the different ways that GIS can be used to present spatial data.

Geog7 Lab 7

Geography Lab #7: Mapping the Station Fire in ArcGIS


The aim of this lab is to map and analyze the Station Fire- which is the largest and deadliest wild fire in Los Angeles County in 2009, using GIS data from Governmental agencies. First, I will attempt to map the Station Fire progression from August 29 2009 to September 2 2009 using ArcGIS. Secondly, I will make a series of thematic map that highlight high fire risk neighborhoods in LA County that may be used to inform people who currently live in or plan to live in these areas.



































The above map is the Station Fire Temporal Progression map which shows the outlines of the perimeters of the Station Fire over time using data from http://atlas.ca.gov/. The extent map shows where the Station Fire occurred in the context of the State of California. From the progression of the fire perimeter above, I can see a pattern that shows the fire is not spreading out evenly in all directions, but tend to spread to the West and South-West directions predominantly.

I hypothesize that the dry Santa Ana winds that come through Los Angeles in the Fall that blow west offshore into the coast has something to do with the way the Station Fire spreads. According to the Department of Atmospheric Science at UCLA, "Southern California's Santa Anas are dry, north-easterly winds having speeds in excess of 25 knots (46 kilometers/hour) These offshore winds usually occur in late fall and winter when a high pressure system forms in the Great Basin between the Sierra Nevadas and the Rocky Mountains." This information matches the dates of the Station Fire- which also occurred in late fall and early winter, as well as the direction of the fire. Therefore, I have mapped above, high fire risk neighborhoods that lie on the West and South-Western directions of the Station Fire using data on population centers overlaid on the perimeter of the Station Fire. I also overlaid hydrological features onto the map, which shows that the largest water sources lie in the North West and South East, which increase the risk of fire in the South West.

The map above maps the wild fires of 2008 and 2009 in Los Angeles using data from the USDA Forest Service, and along with vegetation cover and population centers. High risk neighborhoods in this map are extension of the hypothesis that the Santa Ana winds are important factors in determining the spread of wild fires, thus neighborhoods that lie in the South West of fire prone areas are marked black. The graph shows the different types and quantity of land cover of Los Angeles County- the land cover that covers the largest non-urban area is the Hardwood Forest and Shrub. According to California Green Solutions, "the Chaparral ecosystem of Los Angeles has highly flammable shrubs" that fuel wild fires. Therefore, identifying the areas with highly flammable vegetation can help identify high fire risk areas as well.

The above map is again another extension of the previous maps but over a larger temporal scale. This time, wild fire data from four different years are mapped together with population centers to show the trend of where fires tend to originate and spread. This map clearly shows that there is a horizontal belt of fire prone areas with three main clusters. Again, all of the fires exhibit the tendency to spread to the West, thus explaining their horizontal elongated shapes and matching my Santa Ana winds hypothesis.

I believe that the information from these thematic maps of fire prone areas can help inform people about the risks of moving to such areas. Such that even if they do chose to lie there, they can mitigate the damage by building their homes with fire resistant materials. The government can also through regulation and zoning, mandate fire resistant materials to be used in buildings homes in such areas to mitigate fire risk. Along with the ideas above, insurance companies can also price their fire insurance premiums higher in these fire prone areas, and if homes are built with certain fire resistant materials, then the premium can be adjusted downwards. These solution can help mitigate human deaths in fire prone areas by increasing the costs of living in these areas, and the deaths of the two firemen such as in the case of the 2009 Station Fire may be prevented in the future.

Geography 7: lab #6

Hillshade map of Grand Canyon, Arizona

Slope Map of Grand Canyon, Arizona

Aspect Map of Grand Canyon, Arizona

3D map of Grand Canyon, Arizona

The area that I have selected for this lab is a part of the Grand Canyon, which is located in Arizona. I selected this portion of the Grand Canyon because it contains a mix of low lying, flat and hilly terrain which can accent the different types of spatial analysis conducted using ArcGIS. Each map contains valuable information about the area- elevation, slope inclination, the direction at which each slope faces and a 3D model of the area. I was very impressed with the ability of GIS to build a 3D model, as it also allows for a futuristic "fly-over"- a first person view of a 3D terrain that is almost like you are there in person.


Extent Information:
Top: 34.7227777768969
Right: -111.091666665802
Bottom: 33.142222221235
Left: -112.274444443659

Spatial Reference:
GCS_North_American_1983

Lab 5 Projections

How many decimal degrees does the equator span? 

360.6DD

What about the northern- or southern-most graticule line on the map?
180.46DD

What do these two lines in fact represent?

(latitude, longitude)

From Washington D.C., USA to Kabul, Afghanistan
Spherical GCS Model Distance = 6932.4 miles

All distances posted below refers to the distance from Washington D.C. to Kabul in different map projections.

Conformal Map Projection

Mercator Projection Distance =10189.5 miles

Gall Stereographic Projection Distance = 7141.3 miles

Equal Area Map Projections
World Sinusoidal Projection Distance = 8112.8 miles
World Cylindrical Equal Area Projection Distance = 10066.6 miles

Equidistant Map Projections
Equidistant Conic Projection Distance = 6985.2 miles
Equidistant Cylindrical Projection Distance = 5079.6 miles

 

In this lab, we had to use project a three-dimensional spherical model of the Earth (Spherical GCS model) onto a two-dimensional planar model that falls into three main categories- the conformal, equidistant and equal area type map projections. Furthermore, as seen above, each category of map projection type can have more than one way to be projected: conformal projections include the Mercator and gall stereographic projections; equal area projections include the world sinusoidal and world cylindrical equal area projections; and lastly,  equidistant projections include the equidistant conic and equidistant cylindrical projections.

The purpose of this lab is to recognize the inevitable distortion that occurs whenever a 3D surface is projected onto a 2D surface, and this can be observed in the differences the projections look from the original spherical GCS model. The conformal projections preserves shapes and angles on a map- and this is important if the map is needed for bearings or angles in flight or ship navigation. The equal area projection preserves the proportionality of areas on the map- important if you want to calculate area. Lastly, the equidistant projection preserves distances from a certain point- which is important if distances are to be calculated correctly.

As seen above, each type of map above measures a different distance from Washington D.C., USA to Kabul, Afghanistan, which shows that if you blindly use one map projection without knowing how it is being distorted, it will give you a lot of inaccuracy in calculations. The perils could include getting wrong bearings while navigating, or choosing a projection that doesn't conserve areas when you are trying to do a comparison between two areas in the world, or choosing a projection that doesn't conserve local distances when you are trying to measure distance. For example, the spherical GCS model, which should be the most accurate because it has not been distorted, shows the distance between the two cities to be 6932.4 miles, and the most accurate map projection that shows this is the equidistant map projection which shows 6985 miles, whereas other map projections are up to hundreds of miles off.

The significance of map projection is the ability to project a 3D model of the Earth onto a planer 2D surface. This has mainly practical purposes because planar maps can be easily stored and read, whereas keeping a three dimensional globe is cumbersome and could be harder to read. However, with such practical improvements come with the drawback of distortion- in the areas of angles, distances and areas. The critical part is to know how each map projection distorts and conserves certain features, and on a case by case basis, depending on the task that the user wants to do, there is a most suited map projection.

Geog 7 Lab# 4

Using Geographic Information System software for the first time is really an eye-opening experience. I enrolled in this course because one of my friends who had graduated with an environmental engineering degree, and had worked in the green industry for two years, talked to me about her job and how she uses this software frequently in different situations. This sparked my interest, and my hands on experience with this $10,000 piece of software did not disappoint. Although the lab was merely following directions of the GIS tutorial, it really made me understand why it's called a "system," namely, there is a lot of data that is hidden behind the simple maps above, and the interaction of different data can be visualized by superimposing them on top of one another. Another fact that impressed me was the sheer amount of manipulation that is available- that both numeric and spatial data can be manipulated in a very precise and subtle way. For example, a user can choose from dozens of styles for a roadway symbol, and customize it to his or her liking.

The negative side of my experience with GIS has to do with its complicated structure, and it showed me that this software requires a lot of training before it can be used well. There are literally hundreds of options, buttons, drop-down-lists and tool-bars to choose from and it would take some time to get used to it. Even though the tutorial was clear for the most part, I had trouble figuring out how the data and the map have to be treated differently, and that ArcCatalogue was needed to copy the entire project. In summary, I think I can see how using GIS software can be a very tedious task for the user, and the time frame needed to be proficient with GIS can take months to years due to its sophistication and complexity.

GIS is a very powerful tool and it holds a lot of potential in my opinion. Facts and figures cannot be debated, and the fact that GIS is based on rigorous data that can be checked, makes it a powerful tool in negotiations and predictions. People with different opinions may collect data and be able to visualize it using GIS, and visual data is the easiest and quickest form of information to understand. Implications or theories by different groups can be modeled, and using real world data, predictions can be made that all parties can agree on. For example, a watershed system is very complex, and using traditional forms of presentation would require stacks of slides or graphs that would be disorienting. However, using GIS, that can overlay the data into one map can make visualization easy as well as maintaining the complexities of a real watershed system. This is very efficient in an information presentation standpoint, but GIS can further be used to predict what would happen, for example, if a flood were to occur in the watershed system. High risk areas of flash floods can be located and preventative actions can be taken. The potential of GIS is even higher when you consider that the example is only one of literally millions of ways to use GIS. Just to name a few, GIS is used in excavation, crime prevention, infrastructure upkeep, military and farming.

GIS is a very powerful tool, and like how the police choose to use it to predict and prevent crime, the technology can also be used in destructive ways. Terrorists could potentially use the same methods to locate high risk areas and target them. Another potential pitfall could be that a spatial boundary be superimposed on areas or ecology that don't have well defined borders, thereby creating a human construct of boundary where one did not exist before. Some animals may not perceive these boundaries and the GIS map may not represent a ecosystem in flux very well.

Geog7 Lab #3

UCLA Undie Run Route and Student Guide to UCLA

View Guide to UCLA and Westwood in a larger map

The purpose of my map is to give prospective students a taste of how a UCLA student typically views and "talks" about the campus. From personal experience, it took me a long time to visualize the places that older students were referring to while using UCLA specific "lingo." This is why I have mapped areas and matched them to the "UCLA lingo" that describes them. An example would be "the hill," which is the term to describe the residential halls, less those in De Neve Plaza. I have also attached a video of how a typical dorm room is like. Another fact that I think prospective students would appreciate is the mapping of famous UCLA traditions such as the route of the Undie Run through campus and the location of the annual 'Spring Sing' event. Especially since the Undie Run is officially banned by the UC Regents, information regarding it is lost through official means such as the UCLA website, and thus students who wish to learn about this banned tradition can learn about it through this map and the video attached to the route. Typically, this type of information is passed on through word of mouth from more senior students to freshmen, but I believe that my mash-up map can help freshmen students fit in and orientate themselves to life on campus faster.

Neogeography is about people creating their own maps on their own terms. These 'user-created' maps celebrate the freedom and creativity of people who wish to share location information with other people, and moreover, unlike traditional maps, neogeography allows the creator of the map to convey a personal viewpoint by letting them choose what to show and how they show it.

The potential that neogeography holds is very high because it can reach a very large audience due to its easy user interface and ease of distribution via the world wide web. The former allows people with no programming experience to still create their own personalized maps and post them on the internet. On the other hand, usersability also became easier, so people who have difficulty understanding official, say, USGS topographical maps, can understand most neogeographic maps. More specifically, neogeography revolutionized sharing locational information by democratizing the monopoly on the mapping process that used to be the realm of the government. Map diversity increased drastically as different content and types of map sprang up from a more diverse group of creators.

The drawback, however, is also linked to its large potential listed above. The ease of creating such maps means that anyone can make their own maps, which brings about the question of authority, quality and accountability. The quantity of maps may be increased, but that say nothing about the quality of maps. People typically should not treat such maps as being very precise, since the creator may well be a child. There is also no accountability or recourse if such maps are indeed inaccurate. Secondly, since creators of such maps choose how and what to present in their maps, audiences should be aware that the context they are being shown may be very subjective and biased.

Some consequences of the rise of neogeography may be that people regard such maps to hold more 'entertainment value' than precise locational value. The audiences attracted by the entertainment factor of such user-created maps can increase their general awareness of geography, learn something from the context shown in the map, while not relying on said map for locational precision and objectivity.

Ming Fai Chan

LAB # 2 Topographic Maps

1. Beverly Hills’ quadrangle

 2.

1=Canoga Park
2=Van Nuys
3=Burbank
4=Topanga
5=Hollywood
6= none (it is the Pacific Ocean)
7=Venice
8=Inglewood

3. 1966

4.NAD27. But NAD83 is also shown.

5. Scale of the map- 1 : 24,000

6.
a)5cm on map = 120,000cm real = 1200 meters
b)5inches on map = 120,000 inches real = 1.89 miles

c)one mile real = x inches on map

1 mile real = 63,360 inches real = 63,360/24,000 = 2.64 inches on map

d) 3 km real = x cm on map?

3km real = 300,000 cm real = 300,000/24,000 = 12.5 cm on map

7. Contour interval on map = 20 feet

8.
a) Public Affairs building
= [34 degrees 04' 28''N, 118 degrees 26' 17''W] or [34.074N, 118.438W]
b) Tip of SM Pier
= [34degrees 01' 18''N,118degrees 29' 55''W] or [34.022N, 118.499W]
c) Upper Franklin Canyon Reservoir
=  [34degrees 7' 12''N, 118degrees 24' 32''W] or [34.12N, 118.409W]

9.
a) 560 feet
b) 140 feet
c) 700 feet

10. Beverly Hills is in zone 11N of the UTM system

11. 3763000mN, 361450mE

12. 1000m * 1000m = 1,000,000m2

13.

14. Magnetic declination = 14 degrees East

15. Water flows from North to South.

16.

GEOG 7 Lab #1 September 28 2010

Map 1:
This is a map of the world that was adapted from one by Abraham Ortelius of Antwerp- who was the first cartographer to show the full outlines of North and South America, and is dated to the 1500s. I find this map interesting because it shows what early maps look like, and how different they are to maps nowadays. Not only is the scale different from current maps- the most obvious of which is for Antarctica, the color scheme and information they portray is also vastly less. For example, only a dozen or so cities are named on the map, which is vastly different from current world maps that lists hundreds of cities. However, there are also many similarities to current maps, as we can easily figure out the respective continents on the 500 year old world map due to its generally correct size and shape. This map also reminds me of the age of explorers, where men would risk their lives and voyage out in ships to try to map the world, which lies in sharp contrast to the current satellite map technology that can be accessed over a click of the mouse. The source of this picture is actually from a site called "The Savvy Traveler," that specializes in different kinds of mouse pads and mouse rugs with special prints. Source: www.thesavvytraveller.com

Map 2:
This is another interesting world map because it shows the distribution and intensity of light pollution over the entire world in 2003. You can simply see that most light pollution comes from North America- particularly in the East Coast, Europe and Japan. By contrast, Africa is almost invisible in this map. And by looking at the map as a whole, the entire Northern hemisphere of the world is much brighter than its southern counterpart. It is interesting to note that Australia seems to be very dark even though it is generally considered to be a first world country. And South America, which is generally considered to be a third world country outshines Australia. Another interesting country is India, which is much brighter compared with other neighboring third world countries. There are many different implications that can be further explored by using the information in this map, such as difference in wealth levels, levels of development and energy consumption levels. This is a map hosted by the Astronomical Society of New South Wales regarding light pollution and how it may affect star gazing and the general enjoyment of the night sky. Source: www.asnsw.com/articles/lightpollution.asp

 Map 3:
This is also an interesting map. It shows the locations of different Walmart Stores in the year 2006 in the contiguous United States. You can see that the east part of the US is much more densely dotted by Walmart stores than the west coast, and followed much further by the sparsely dotted middle part of the country. From this map, one can deduce different implications- namely that maybe the eastern part of the US is more densely populated and maybe also more wealthy, that's why their demand can support so many stores. I was surprised that California has not too much more stores compared with other west coast states, as it was once considered the sixth largest economy in the world. You can also see how the desert states in the middle have less stores because of the lower population density. However, it is interesting to note in the Eastern US, that areas above and below the sunbelt seem to have around the same amount of stores, meaning that weather has seemingly no effect on people's purchasing power, because I would have assumed that the high costs of heating required in the cold areas would mean people generally have less to spend on goods than those who live in areas that don't require heating costs. Another interesting fact about this map is that it is a user-created map. This map is hosted on the blog by "Excelhero," who used a Google map function to plot the dots manually using data acquired from Walmart. Source: http://www.excelhero.com/cgi-bin/mt/mt-search.cgi?search=walmart&IncludeBlogs=4&limit=20


Ming