The US Census, 2000

ALL THREE MAPS SHOW DATA FROM THE 2000 US CENSUS

PCT = PERCENTAGE

   The Asian-American population during the year 2000 was mainly clustered in California and Washington.  This is reasonable because these states are in the West Coast, which is obviously closer to mainland Asia than the East Coast.  According to the map above, many Asian-Americans lived around the big cities of the West Coast, namely Los Angeles, San Francisco, and Seattle.  In Washington, Seattle and the surrounding regions seem to be the only places in Washington where Asian-Americans significantly contributed to the population.  However, California was the state where most Asian-Americans decided to live in.  San Francisco, Los Angeles, and their surrounding regions were not the only ones with significant population numbers.  Central California also had many Asian-Americans living in its cities.  Across the country, there were not many Asian-American population centers.  Most of them were scattered; there were some major communities within the central part of the country.  After the West Coast, the East Coast, as a whole, had the next highest number of Asian-American population centers, namely around New York City and Boston.

   During the year 2000, African-Americans clustered deep within the American South.  This is not surprising because the South has a deep history with African-Americans.  The map above clearly shows that the majority of them lived in Louisiana, Mississippi, Alabama, Georgia, South Carolina, North Carolina, and Virginia.  There were some other highly populated counties in the US that were predominantly African-America; in the map, they can be found in states that border the Great Lakes and/or the Mississipi River.  With that said, the map above shows an absence of predominantly African-American counties around the western US.  There were significant percentages around important cities, such as Seattle, San Francisco, and Los Angeles.  The same is true for the East Coast; the map shows that significant percentages were present around New York City and Orlando.  In other parts of the country, namely the Midwest, the 2000 African-American population generally was consistent at around 1-3%.

   During the Census 2000, the government obviously asked Americans what race they were for statistical purposes.  It provided the option of "some other race," which signifies that the person filling out the forms did not perfectly fit in the other given race categories.  People who filled in "some other race" were mutiracial or Hispanic/Latino, which did not have its own category.  In fact, 97% of the people who reported "some other race" were either Hispanic or Latino.*  That statement alone tells a lot about the map above.  States that border Mexico - California, Arizona, New Mexico, Texas - had a great percentage of "some other race."  There were a few "some other race" counties near the northwestern US that had high percentages.  The rest of the nation did not have many counties that had people who claimed to be another race that was not listed on the Census forms.  With respect to Hispanics/Latinos, it can be inferred from the map that the majority of them decided to stay around southwestern America and to not go too far out into the country, particularly the Midwest or the Eastern Seaboard.  There are some darker shades of green around the Eastern Seaboard, but that could probably be explained by the presence of white multiracial people.

*http://www.census.gov/mso/www/rsf/racedata/sld008.htm

   My census map series provides a lot of racial information about the continental US during the year 2000.  Asian-Americans tended to have greater populations around the West Coast, and African-Americans tended to have greater populations around the South.  These facts are not surprising at all.  With respect to the entirety of the continental US, the West Coast is the closest to Asia.  Black people have had a long history with the South, especially in terms of slavery and civil rights.  They have not migrated en masse out of the area.  It would not be surprising to find descendants of former slaves and black civil rights protesters in the South.  Many people who responded to "some other race" were Hispanics/Latinos; it was also not surprising to find high county percentages of "some other race" in the states that border Mexico.  In all three maps, there are a few counties that are white.  It is interesting to see how there are a few counties in the entire nation that do not have people of a certain race.  On the other hand, people in that county may not have filled in the forms correctly or even at all.

   Doing this assignment was not easy at first, but I eventually got used to it.  I am amazed at what I was able to produce using the GIS software.  A GIS professional can definitely make a plethora of useful maps that tell a lot about the US and its people.  While the GIS software can be frustrating at times, it is rewarding at the end.  There is a lot a user can do with GIS with respect to data and cartography.  GIS's ability to connect with Microsoft Excel is invaluable because Excel is usually associated with creating and storing data - GIS puts that data in map and visual form.  I could have tweaked my three maps in many different ways.  With respect to this assignment and the various details it brought along with it, GIS allows the user to change the color scheme, add a bar scale, add additional information to the side, and much more.  GIS is also very detailed, and this assignment proved it.  The software enabled me to see every county in the continental US and its percentage of a certain group of people.  Even though I was able to guess what my maps were going to look like, GIS can undoubtedly display detailed data that were not even expected in the first place.  GIS is an example of a piece of technology designed for the person who thinks intellectually and creatively.

DEM's

Digital Elevation Models

   The area I chose for my DEM is located in the Sierra Nevada Mountains.  As anyone can expect from a typical mountain range, the terrain is steep and uneven.  However, the jagged terrain and the valley in the middle - which are both seen in the 3D image of the location - make this area atypical contrasted to stereotypical mountain images in movies, calendars, etc.  There is many data that can be pulled out from these images.  One side of the valley contains many mountain peaks that are higher than the peaks on the other side.  The valley in the middle is not very wide, and it seems to perfectly cut through the mountain range like a pair of scissors.  The higher mountain peaks are around the same elevation compared to each other; on the other side of the valley, the elevation is more uneven.  In the shaded relief model, one can also see a small valley cutting through the higher mountain peaks.  Overall, the area I chose was not anything too dramatic, but it had some very interesting features.

Extent Information

Top: 37.4113888882

Left: -118.955277779

Right: -117.780833334

Bottom: 36.7605555548

Information About The Geographic Coordinate System

Spatial Reference: GCS_North_American_1983

Linear Unit

Angular Unit: Degree (0.017453292519943295)

Datum: D_North_American_1983

Map Projections

   Map projections are an essential part of cartography.  Even though their use is rare with respect to normal everyday commuters in a particular city, different map projections are undoubtedly helpful for government agencies, airport officials, ship captains, etc.  While 3-D globes are an ideal tool of navigating the world, they are not very practical.  2-D maps are used more often because they are versatile in viewing the entire world on one flat sheet of paper.  The reason why having an overview of the entire planet can be useful in, for example, travel activities and government relations is that the map shows the user where all of Earth's cities, countries, continents, oceans, etc. are in relation to each other - the user cannot do this with a 3-D globe.  Perhaps this is the main significance of 2-D maps.  However, flat paper maps are not without imperfections.  The 3-D world is pressed onto the 2-D image - some aspects of the real world do not make it onto the map.
   One example of distortion is related to distance.  For example, the Mercator Map Projection shows that the distance between Washington DC and Kabul is 10,112 miles.  However, the Equidistant Conic Map Projection shows that this distance is 6,972 miles.  That is a difference of 3,140 miles!  Which map is telling the truth?  Or are they both inaccurate?  The actual distance between the two cities is above 6,900 miles.  Map projections fall short of perfection because they distort the actual distance between two locations - some do this to a greater degree than other projections.  In the end, distance is definitely compromised when the actual distance has to be forced onto a certain map projection.  This is not the aspect of the real world that suffers.
   Another example of distortion is the shape of land masses.  In particular, Antarctica always falls into this problem.  The Azimuthal Equidistance Map Projection shows Antarctica as a small continent.  Compared to the size of the US, Antarctica's area seems to be similar.  The "small" countries of Africa are clustered up on their continent and seem to be minute when taken individually.  However, the Mercator Map Projection tells a different story.  This projection presents Antarctica as a giant monster!  The continent is obviously not big, but this projection is misleading with respect to the area.  At a first glance, it seems as if Antarctica could fit in all the countries of the world.  Antarctica's area is skewed, but the areas of other world regions, such as Africa, are not.  In the Mercator Map Projection, Africa seems to be the same size as it is on the Azimuthal Equidistant Map Projection.  Land masses are undoubtedly altered during the process of translation from 3-D to 2-D.
   Even though we live in a highly digitized and technological world, map projections are still being used for their aforementioned versatility.  They have potential because they can be improved to better conform to the standard of the real world.  The Hammer-Aitoff Map Projection gives a good representation of the world.  Antarctica is not a gigantic supercontinent, and the countries of the world are "slanted," giving the projection a 3-D globe feel.  When a user looks at a typical globe, the area he is looking directly at does not have any distortion.  The surrounding areas are distorted relative to his perspective.  2-D maps are also useful for planning the logistics of a major international trip; for example, several users can draw and write on the maps in order to facilitate the planning.  There will always be map distortions in any 2-D projection.  However, there are many map projections to accomodate the various needs different people may have.  The Equidistant Conic Map Projection may be helpful in planning trips over the North Pole.  The Stereographic Map Projection may be useful in planning trips over the South Pole.  There are numerous map projections in existence that are suited for many needs.

Getting My Feet Wet In ArcGIS

   GIS has many technological features that make it a useful tool in creating maps and storing data.  While doing this exercise, I was amazed at the many different layers of data that were synthesized to make maps that contain much information.  These maps could be used by citizens to make them more aware of the issues that surround them in their community.  Policymakers could also use these maps in an effort to address concerns of the community as a whole.  Data in both picture and table form, such as in the picture above, could be regarded as weighty pieces of evidence in favor of a piece of airport noise legislation, for example.  Even though this exercise required only a few data frames, I am certain that GIS professionals use many more data frames in a single project to give different perspectives on the same piece of land.
   GIS technology is also dynamic, which makes it very useful with respect to the real-world applications.  The real world is not constant - things are always changing - but GIS is adapted to the world's mutability. If I were able to change the data for the exercise, the graph would change based on the new data.  The fact that GIS can adapt to change is important because circumstances vary as time passes by.  Printed maps cannot change with the times.  If an airport starts to decrease in size and importance, noise levels may not be a huge concern to a nearby neighborhood.  GIS would be able to process the new data and inform various policymakers and city officials of the lessened problem.  These leaders may end up not having to spend X amount of money on fixing the noise problem.
   Even though GIS is a very sophisticated piece of technology, it does have its shortcomings.  In the example above, I created a population density map, which is divided up into many different-sized regions.  Each region is characterized with a shade of green, according to its population density.  While not blatantly inaccurate, this labeling is misleading - the map says that the density is uniform in every single part of region X.  It is as if the map is telling its user that the density will radically shift the moment somebody crosses over a border between two regions.  In my GIS experience so far, I remember extracting data that could only be used for a certain region.  Perhaps the map would have been far more accurate if the GIS program provided more data for smaller regions.  This would definitely help in suggesting different pieces of legislation, in navigation, and in personal use.  The issue of data is related to another downfall of GIS.
   As I went through the GIS exercise, I realized that while GIS is a very useful tool for mapmaking, it is constrained by the amount of data it has at any given time.  In other words, the ability of GIS to serve its user well is directly related to the amount of information it receives and already has.  While GIS can definitely be updated minute-by-minute, that depends on how fast information and data are being gathered in the outside world.  If somebody needs to know the sound levels of a nearby airport, he can easily ask for other people to do the job of collecting data regarding the amount of decibels in the nearby area.  However, what if the airport changed its regulations concerning the amount of air traffic that it can receive in any given period of time?  And what if this change was not publicly announced?  The end result would be an incorrect map, whose creator was unaware of the new airport policy.  He would again have to ask others to collect updated information about the sound levels, which takes time.  GIS is very much limited in its abilities with respect to the amount of information it has at the moment.  As previously mentioned, GIS is only as useful as the present amount of data already in the system.