Lab 5: Quick findings in the US and Canada

1. How many counties does the State of Iowa contain?
The State of Iowa contains 99 counties.

2. Which county in Georgia contains the largest number of people, as of the year 2001?
Fulton Country contains the largest number of people in the year 2001.

3. How many cities, with populations greater than 15,000, are located within the State of Washington?
38 cities with populations greater than 15,000 are located within the State of Washington.

4. How many miles long are all of the interstates that are crossed by the outline of Los Angeles County?
2363.5916 miles, 103.4055 miles, 1357.0795 miles, and 27.060987 miles
Total: 3851.137587 miles

5. Simplify the UrbanBoundaries feature class so that only the urban areas of Los Angeles County are visible.

How many acres of urban
area lie within Los Angeles County?
Lancaster—Palmdale=50238.516 acres, Los Angeles=1220379.1 acres, Oxnard—Ventura County—96828.25 acres
Total: 1367445.866 acres

6. How many zip codes have their centroid in Los Angeles County?
522 zip codes have their centroid in Los Angeles County.

7. Create a layer displaying the provinces of Canada using the data provided to you in the Canada dataset.

8. Which Native American Indian Reservations lie within 75 miles of the City of Thurso in Quebec, Canada?
Duncaster Indian Reserve 17, Kitigan Zibi Indian Reserve, Kahnawake Indian Reserve 14, Kanesatake Indian Reserve 16, and Akwesasne Indian Reserve 15.

Lab 6: Mount Saint Helens using ArcMap

Lab 4- Census maps

Judging by what the data on the map shows, it seems like the percent Asian population is concentrated in the western side of the United States, most particularly in the mid-portion of California. From there, the Asian population seems to spread down into the rest of Southern California quite constantly. As far as the overall percent Asian population is concerned, it is significantly small in the Mid-West. The Asian population seems to be more concentrated around the South-Western and North-Eastern Coasts.

According to this map, the distribution and concentration of the percent Black population is quite different from that of the Asian population. There is a significant concentration of the Black population in the eastern and mainly south-eastern side of the United States. Throughout the rest of the United States, the percent black population is scattered and becomes a little more concentrated in Southern California, but never as much as it is on the south-eastern side.

This Some Other Race map shows a much bigger concentration of the percentage of that population in just one side of the United States, not found in the other two population maps. Clearly, the some other race percentage is quite concentrated starting from the north-west, down to the south-west, spreading all the way out into the mid-west. Surprisingly, from thereon into the east, the percent Some Other Race population declines significantly.

Rather than just merely judging what the map series shows by analyzing each map individually, it is important to analyze all three of them as a whole. The main reason for analyzing and judging it as a whole is found in the difference of percentage range present in each one. Out of all three of them, the black population had the biggest percentage ranging from its minimum percent of 0.010289 all the way up to its maximum percent of 86.488706. This helps explain why the concentration of a certain percentage of population of race in one part of a map does not necessarily mean that there is necessarily a bigger population of that race in that area and/or overall. The percent data present in each map was all relative in accordance to each one of the three percent race populations individually, rather than all of them together. For example, although the percent Asian Population in the West seemed to be much more concentrated than the percent Black Population, according to the data provided by the maps, there were actually more Blacks than Asians in those very same "Asian-concentrated" areas. It is our responsibility to keep this in mind when analyzing and drawing conclusions from these census maps.

In this lab, GIS is used to make three separate layers of data using three different tables to create three different census maps. GIS very conveniently allows the user to switch between map data to have a better visual understanding of different kinds of data through a varying range of patterns. As far as the actual transfer of data from the US Census 2000 website was concerned, GIS made it especially useful in that the user did not have to manually insert the data into the ArcMap him/herself. GIS made the process very efficient and quick in that it allowed the user to copy excel tables from the Census website, make a few adjustments, and finally add them, with just the click of a button, as data to ArcMap. In addition, by copying the exact data from the website almost directly into ArcMap, there was a decrease in the amount of error in the final displays of each map created.

Lab 3: Exploring Map Projections

Distance (in miles) between Washington, D.C. and Kabul, Afghanistan
Mercator: 10,114.41431 miles
Gall Stereographic: 7,154.417641 miles












Distance (in miles) between Washington, D.C. and Kabul, Afghanistan
Plate Carrée: 10,111.569936 miles
Sinusoidal: 8,094.198608 miles











Distance (in miles) between Washington, D.C. and Kabul, Afghanistan
Bonne: 6,737.656021 miles
Mollweide: 7,924.80534 miles


Map projections are very significant in that they present different views and perceptions of the world in different shapes and forms. In other words, each projection (conformal, equidistant, and equal area) can be seen as a representation of the same idea but from a different point of reference. Different types of map projections have different ways of bringing what is really a three-dimensional spherical object (the Earth) and making it easily readable as a two-dimensional object. Arc Map is very useful when changing, comparing, and analyzing map projections since it permits users to make and activate several layers of data, all under one common projection.

It was by taking part in this lab that I really was able to have a hands-on experience working with the distortions of views and information produced by changes in map projections. Surprisingly enough, the Gall Stereographic conformal and Plate Carree equidistant map projections represented the 2D map of the world in very similar ways especially when it came to the size of the continents. It also surprised me how such an unrealistic heart-shaped view of the world as seen in a Bonne projection (an equal area projection) is not too different when compared to another equal area projection known as the Mollweide projection when measuring the distance between the two cities of Washington D.C. and Kabul, Afghanistan; just a difference of 1187.15 miles. One other very important observation was that between the conformal projections, Gall Stereographic and Mercator, there was a much greater difference in distance (than the one seen between the two equal area projections above) between Washington D.C. and Kabul, Afghanistan—2959.99 miles.

There was great distortion in the relative sizes of the continents when they were accurately represented in different projections. Out of all of the map projections observed in the lab, I found that the equidistant Sinosoidal map projection was the most distorted and most different from all of the rest. It seemed to me that North America and Asia were stretched vertically in a very exaggerated manner.

Some of the perils of map projections based on this exercise were found while measuring the distances between Washington D.C. and Kabul, Afghanistan. The problem was that I was constantly making the mistake of measuring in meters rather than miles because the measurement tool tended to automatically change back to meters. I had to go back and check each map projection in order to make sure it was measuring in miles to avoid inaccuracies. In the end, I found that the process of separating the different projection layers and making separate files for them in order to post them on my blog was a little tedious; especially when I realized that each map needed its own scale bar even though it shared the same type of projection with another map. Example: Even though Mercator and the Gall Stereographic were both conformal map projections, I needed to give each one of them a separate scale bar.

Lab 2b: Schools Affected By Noise

My first ArcMap experience was neither the smoothest nor the roughest. It never occurred to me that an ArcMap had so much potential apart from displaying map features. By completing this assignment, I learned how to create many layers of information by using all kinds of data in the forms of tables and graphs. This experience made me even more aware of the fact that there is so much in this new world of technology that I do not know about.

The most confusing part of following the tutorial was saving my information correctly. I have never had very much experience with moving files to different drives and putting them under different drive names. I had to always make sure to ignore the tutorial directions that asked me to save my information in the C: drive. When transferring and saving my files to the D: drive, I made a mistake of not making a separate folder for this particular lab and kept saving everything under the folder “Map.” I was nervous and stressed about making sure that the files were constantly being saved in the right spot.

The directions on the tutorial were easy to follow in the beginning. After about page 18 of the tutorial, things started to get more complicated and there was a lot of information to take in at once. There were many new features and terms that I had never toyed with before and I was constantly afraid that I had not clicked on the right button or typed in the wrong information. Luckily, due to the slow rate I was working at, I very seldom misclicked or mistyped. In addition, I also noticed that no matter how carefully and closely I followed the instructions, my maps were never going to be identical to the ones used as models from the books without doing further adjustments of my own (zooming, moving, etc).

At the beginning, I just followed the directions carefully without thinking very much about the purpose. It was only when I went back and reviewed the tutorial that I was able to analyze and understand the different layers and each of their purposes when locating schools near the airport and which ones are affected by noise from the airport. Overall, it was fascinating to see how such a simple concept of locating schools can branch out into such an array of layers, data, tables, and graphs; all embedded into what seems to be a simple map of schools and an airport. Arc Maps have great potential when it comes to building and constructing schools near noisy areas such as airports in this case; they help to prevent future complaints from school officials, teachers, parents, and students.

Lab 2a: Fun with Google Maps

View The Best Places To Visit In Nantes, France in a larger map

Before completing the Lab 2a assignment, I had never even thought of making my own Google Map. For a long time, I had been taking Google Maps for granted and never took a second to think about the work that people went through when using neogeography. I have to admit that making my own Google Map was not an easy task, considering the fact I am not anywhere close to being a computer genius. It was only after coming up with an idea and purpose that could potentially convey my own experiences and understanding through knowledge of place (that other people would be inclined to take a look at) that I started to feel the first hint of excitement. It was at that moment that I realized how much potential the idea of identifying and describing the best places to visit in Nantes, France had and how much other people could benefit from it. After spending nine months in France last year, I understood the frustration of getting to a place and not getting enough help from local people when it came to sightseeing. I realized that local people tend to take places where they have lived their whole lives too much for granted; many had never even visited the museums. It was then, up to me to take matters into my own hands and to see for myself. I created my Google Map by combining all of the places that I loved the most during my time there and that I would willingly go visit again. Looking over my completed Google Map was very rewarding in that I knew that I had fulfilled everything that Neogeography was all about: sharing location information with friends and visitors, helping shape context,
and conveying understanding through knowledge of place.
The completion of this project was not just rewarding for me, but potentially rewarding for others who have found themselves in the same situation. Overall, it had more positive consequences than negative ones.

It was only through hard work and toil that I was able to make it to this rewarding end. Most pitfalls concerned javascript coding. Doing the right coding for images and links was difficult enough for me. The most frustrating thing was placing a code into my google maps and getting something like “<” as an ending product. It took a long time for me to realize that there were slight errors when I was copying and pasting codes into codes that would not allow the image or link to appear. One very memorable and stressful incident was that after embedding a manifold of images and links into my Jules Vernes Museum information window, a few hours later, they were all gone; despite the fact that I had seen them in my final product after saving and closing the editing mode a few hours ago. I could not understand why this happened, so I made sure that while working on Google Maps, I would save constantly to ensure that it would not happen again. This showed me how unpredictable and unreliable technology could be especially when it comes to coding. One other thing that I had to fight with and surrendered to was embedding youtube videos into the information windows. After following all of the instructions carefully, under the “editing” screen, it showed that the video had been successfully embedded. It was when I clicked on “Save” and “Done” and viewed it, that the video disappeared. Thinking that it was a problem since I already had an image in the information window, I took off the image and tried again to no avail. I tried several times using other youtube videos but none of them showed up in my ending product. That was when I decided that it was best to stick to meaningful links and images that could help replace the benefits of attaching a video. Out of all of the positive consequences of neogeography, one negative one was found in that after hours of working on my Google Map, I knew that those who would view it, would not think that so much time was put into it at all.

Lab 1b

1. What is the name of the quadrangle?

Beverly Hills

2. What are the names of the adjacent quadrangles?

Van Nuys, Canoga Park, Burbank, Hollywood, Venice, and Inglewood

3. When was the quadrangle first created?

1995

4. What datum was used to create your map?

North American Datum of 1927 (NAD 27) and North American Datum of 1983 (NAD 83)

5. What is the scale of the map?

1:24,000 which means one inch on the map is 24,000 inches on the ground

6. At the above scale, answer the following:

a) 5 centimeters on the map is equivalent to how many meters on the ground?

1200 meters

b) 5 inches on the map is equivalent to how many miles on the ground?

1.8939 miles

c) one mile on the ground is equivalent to how many inches on the map?

2.639 inches

d) three kilometers on the ground is equivalent to how many centimeters on the map?

12.5 cm

7. What is the contour interval on your map?

20 feet

8. What are the approximate geographic coordinates in both degrees/minutes/seconds and

decimal degrees of:

a) the Public Affairs Building?

34˚ 4’ 27.588”

b) the tip of Santa Monica pier?

34˚ 0’ 27.072”

c) the Upper Franklin Canyon Reservoir?

34˚ 2’ 38.292”

9. What is the approximate elevation in both feet and meters of:

a) Greystone Mansion (in Greystone Park)?

580 feet; 176.83 meters

b) Woodlawn Cemetery?

140 feet; 42.67 meters

c) Crestwood Hills Park?

620 feet; 188.98 meters

10. What is the UTM zone of the map?

Zone 11

11. What are the UTM coordinates for the lower left corner of your map?

34˚00’, 118˚30’

12. How many square meters are contained within each cell (square) of the UTM gridlines?

1000 square meters

13. Obtain elevation measurements, from west to east along the UTM northing 3771000 where the eastings of the UTM grid intersect the northing. Label the elevation values to the two measurements on campus.

Elevation values along the UTM northing 3771000

14. What is the magnetic declination of the map?

magnetic compass needle will always point 14 degrees to the east of true geographic north

15. In which direction does water flow in the intermittent stream between the 405 freeway and Stone Canyon Reservoir?

The water flows south.

16. UCLA from the map.

Lab 1a

This map was found through Google search on selfinterestandsympathy.files.wordpress.com. This is a map of world legal systems: Civil, common, customary, and religious law. I find this map very interesting in its way of conveying global diversity through its use of colors. It is through this map that one is able to really understand how complex Africa is when it comes to legal systems. Out of all of the continents, Africa has the most variety in legal systems: civil, common, and religious. I really like how detailed the map is and how it accurately denotes Louisiana as the only state in the United States with both a civil and common law (shown in brown). Differences continue to exist between Louisianan civil law (from French, German, and Spanish influence-blue) and the common law (from English influence present in all other states-red) found in the other states.



This map was found on strangemaps.files.wordpress.com under Religionism and Religiosity. This map represents the United States as a highly religious country by highlighting the 8 leading Christian dominations such as Baptist, Lutheran, Mormons, Catholics, Mennonites, and Christian, as well as others such as Anglican and Adventist. What is very interesting about this map is that it demonstrates the important link between region and religion. The map goes on to show how the 8 leading Christian dominations represent a plurality of the relevant counties' population. This further reinforces the idea that there are quite a few contiguous religious blocks in the United States. For example, there is a great concentration of Baptists in the eastern/southern states, but as we move further west, we find less and less Baptists and a larger concentration of Latter-Day Saints. The one religious group that seems to dominant the United States at a somewhat constant rate is Catholic.


This map was found on garyhaq.files.wordpress.com under an article titled "Enoughism--The Route to Happiness?". Under the title "A Global Projection of Subject Well-being," this map represents how different countries all around the world vary distinctly in their level of happiness depending on different aspects of lifestyle. The one continent that has a mostly constant high level of happiness is North America. It is interesting how other continents, such as Europe, are smaller than North America, yet vary in their levels of happiness: i.e. Portugal is represented as slightly less happy than Spain that is right next to it. France is not as "happy" as its bordering countries, Germany and Spain. Out of all the continents, Africa and Asia seem to be the most varying in happiness. Another aspect that I find interesting is that supposedly wealthy countries such as North America and Australia are represented as the "happiest", despite the fact that studies have shown that more choice and more things do not necessarily bring more happiness. In that case, countries like France and Italy who went through a lot during World War I and II should be considered happier than they are represented in this map since they value and appreciate food and goods much more than citizens in North America and Australia do. One critical point I will have to make about this map: despite all of the economic and governmental problems its citizens are constantly faced with, Mexico is represented as happier than France. France should be considered happier since its citizens face rather minor problems when compared to those in Mexico. Thus, in my opinion, the fact that French citizens are not represented as much better off than Mexican citizens renders this map inaccurate.