My first map displays the percentage of the Asian population of the contiguous United States from the 2000 Census statistics. The percentage is from the total population. By looking at this map, the first pattern that arises is that the are with the highest Asian population is California, particularly southern California. This is where the most clustering occurs as well as the highest percentage. There also appears to be a small clustering along the upper east coast.
In this next map, the percentage of the Black population around the United States is displayed. Rather than being clustered in a small area like southern California, the black percentage of the population is spread primarily around the south east portion of the country. This extends through several states, but is still clustered more towards the coast line. In addition, there still is a clustering in California, in the central to southern area.
This last map shows the percentage of the population that is American Indian and Alaskan. Here too is a clustering in California. On top of this, there is a strong clustering in the states between California and Texas; the percentage is higher here than it is in California. Unlike the other maps this one shows pockets of their population periodically throughout the entire United States.
Receiving the Census data on paper as numbers only tells you so much information. By joining this data with county lines of the United States, the geographic location of certain races takes a visible form. This shows clustering in certain areas. For example, each race has a clustering in California. This could be used to explore aspects of California such as policies, school systems, social characteristics, stereotypes, etc. as to why other races cluster here rather than other places.
In general, I really enjoyed my GIS experience. My biggest problem was that horrible red exclamation point telling me that my data was from the wrong place. I also had no idea how much creative license there is in GIS. I didn’t realize how many different projection types there were; I just assumed that maps were unbiased representations of the world. I also was not aware how much things like color ramps and label placement and color and font mattered, it really makes a difference in what you are trying to say with your map and what audience you have. I’m looking forward to continuing with GIS in future quarters.
