Other races
Most of the people who listed themselves as “Some Other Race” in the 2000 census were Hispanic (ADNET 2000). I have classified the data into seven categories, so that the map can portray the extremes of the concentration of the population of the race and the viewer can understand the overall distribution with as much detail as possible. According to the above map, we can see that the counties in the South and South Western of the continent have the highest percentage of their population with people of “Some Other Race” compared to other areas. Then there is another concentration of high “Some Other Race” population in Florida. When we regard this map at a macro-level, we can see that Mesoamerica and South America are closest to the South and Southwest regions in which these high density of “Some Other Race” groups exists, therefore it makes sense that since the Hispanic population, which has a close proximity to these areas, would prefer to first settle in these regions and then slowly expand inwards to other states/counties. Similarly, Florida and its nearby regions is very close to Puerto Rico, and if the first immigrants settled first in these regions, the later generation would prefer residing in those already established communities of their own race. Nevertheless, this map also has its biases. Viewers have to bear in mind that even though Hispanics were the largest group who reported that they belong to “Some Other Races,” population density of some other races are also included in this map. Moreover, the visual effect of the map might be misleading because even though the spread of the density of the population of “Some Other Race” might be significant, the overall highest ranking population of “Some Other Race” is only 39.1%, which is less than half of the whole population. Therefore, in order to remedy this, I have added to the legend the highest percentage of the overall population of a county. So that viewers would know even though the population might cluster in specific areas, the population of “Some Other Race” itself is not so huge compared to the whole population.
Black population
Immediately after looking at this map, we have the impression that the black population concentrates in the Southeast of the U.S. Continent. I have classified my data into six different categories since even though the population might concentrate in the SE, it could lead to a lot of biases and making invisible the other areas in which the black population is also concentrated. Therefore I also used two different colors to show that there is also a medium high concentration in the West coast, which might be also significant to some map viewers. The significantly small concentration of Black population (as shown on the map by the range 0-1%) in many other regions might due to certain factors including ethnic issues and group preferences, which might include segregation in the low black population concentration areas and the firm establishment of communities within the high black population concentration areas. On the other hand, the African Continent is much closer to the Southeast area of the U.S. Continent than the Western area, therefore it might make sense for the first immigrants to settle in these areas and then slowly expanding to the nearby counties. Similarly, most of the black immigrants arrived earlier in history were from the Caribbean (Kent 2007). Given the close proximity of the Caribbean and the area on the map which the concentration of Black population is clustered, we can say that this factor, together with the factor regarding the African Continent, contribute to the firmly rooted communities created by the immigrants and therefore the high concentration of Black population in this region of the Southeastern of the U.S. Continent.
Asian Population
Compared to the Black map and Other Race map, the Asian map seems comparatively more evenly distributed across the U.S. Continent but still has its own concentration in the West coast. Since it was hard to tell the significant clusters when I tried to classify the data into more categories, therefore I have decided to break them into four categories so that the overall pattern can be portrayed and that the areas with high Asian population density can be highlighted. Interestingly, when compared to the other two choropleth maps, the Asian population consists of a high density in Hawaii (highest when compared to the other states). Because of the clusters in the West Coast, the high Asian population density in Hawaii, and also the close proximity of these areas with the Asian Continent, we can infer that the first Asian immigrants settled first in these areas and then slowly expanding their territories with the U.S. Continent. And in fact, since Chinese is one of the largest Asian population in the United States, therefore the huge number of Chinese who rushed into the West Coast in search for Gold (Le 2010) might also account for why this area has such a high cluster of Asian population. Nevertheless, this map also contains certain biases. Asians do not only consist of Chinese, they consist of other larger population as well, including Koreans, Japanese, Indians, Filipinos, etc. That is why the map viewer has to be especially careful when decoding the implications of the map. And this might also contribute to one of the reasons why the Asian population above portrays the population as so evenly spread compared to the other two maps. Since these different ethnic groups might have settled in the U.S. Continent at different periods or may be simply because of difference of culture, therefore they have set up their own unique communities or “turfs” that their own respecitve group might be more attached to. Lastly, similar to one of the drawbacks of the first map (“Some Other Race”), although this Asian Map shows how some of the regions might be containing a high concentration of Asian population, the highest percent of population density is only 46%, which is still less than half of the whole population of an area.
Concluding discussion about the census maps
Creating the census maps was not difficult except for the part in which we had to classify the data into different categories. In all of the three maps, I used “manual” or “optimal” classification scheme by actually first using “natural break” and then further breaking the data into smaller categories so I could see the minor changes which might be useful when we want to analysis with more details. It would be easy for the map maker to make more sense out of the maps because of the combination of the excel files/metadata of the map. But the viewer of the map might not have access to these extra sources, therefore, as the map maker, I tried to create the overall pattern as clear as possible and as much detail as possible, but at the same time making the classification more systematic without complicated decimals.
Creating the census maps was not difficult except for the part in which we had to classify the data into different categories. In all of the three maps, I used “manual” or “optimal” classification scheme by actually first using “natural break” and then further breaking the data into smaller categories so I could see the minor changes which might be useful when we want to analysis with more details. It would be easy for the map maker to make more sense out of the maps because of the combination of the excel files/metadata of the map. But the viewer of the map might not have access to these extra sources, therefore, as the map maker, I tried to create the overall pattern as clear as possible and as much detail as possible, but at the same time making the classification more systematic without complicated decimals.
When we look at the three maps, we can see that they all have very distinctive patterns but they all have a thing in common, that is, these populations tend to cluster in certain areas rather than evenly distributed across the continent. Even when the viewer does not know the history of the United States, they might be able to infer the reasons for concentration of the population of certain races when looking at the choropleth maps. Since human beings do not randomly select their place of habitation, therefore there must be some factors that pull them into residing in those areas together. For example, the reason why these races tend to concentrate in one area might suggest that their population has first set up in that area and therefore the later generation of the population might be able to fuse into those communities more easily. Together with my analyses, the map shows that the geography at a macro scale and also multiple human factors contribute to the preference for the clustering of populations of different races. And by making use of these maps, viewers can not only able to understand the concentration of different races in different counties, but also predict how these population spread across space and time.
Overall impression of GIS:
Even though this class is just an introductory course to GIS, I felt like I have slowly progressed from an amateur cartographer to a more professional one. From the beginning of using googlemaps to create thematic maps simply by plotting points and drawing lines using an existing map to downloading our own maps and editing them to form Digital Elevation Models and choropleth maps, I slowly started to manipulate the basics of a more professional cartographic tool – ArcGIS. And most of the times we were left to explore the program ourselves, and this had led me to better manipulate the program. “Practice makes perfect,” when I repeatedly used the program, I started to remember which buttons to click to do certain actions including adding an insert map and also selecting attributes using buffers. In fact, I find myself addicted to the use of ArcGIS because once I started doing my project, I did not want to stop using the program because of the multiple options that I could employ to make my maps unique and informative, including the color, the north arrows, sizes, etc. Nevertheless, my addiction led to some negative effects. For example the physical damages that resulted from sitting in front of a computer for hours.
Despite the positive side of ArcGIS, I sometimes found the program confusing because of its rigidity, including how the undoing process could not allow me to undo minor changes, how when I forgot to turn on the “3D Analyst” and “Spatial Analyst” tools I could not make any changes to my Digital Elevation model, and how when I did not save certain files to another location, I could not export or save my map to another location. In addition, the random dysfunctional cases including the sudden termination of and the slow response of the program have led to my frustration.
Overall, I found ArcGIS and the class beneficial because, as an archaeologist, the use of maps is very crucial to the understanding of archaeological findings and its environment, both as a map maker and a make viewer. I really hope to continue expanding my ArcGIS knowledge such as collecting my own data and also making more complex maps so that I could be able to master ArcGIS to create useful maps for the contribution of Archaeology in the future.
Works Cited
ADNET. "Some Other Race." Census Bureau Home Page. 2000. Web. 02 Dec. 2010. <http://www.census.gov/mso/www/rsf/racedata/sld008.htm>.
Le, C.N. 2010. "The First Asian Americans" Asian-Nation: The Landscape of Asian America. <http://www.asian-nation.org/first.shtml> (December 2, 2010).
United States. Population Reference Bureau. Immigration and America’s Black Population. By Mary M. Kent. Population Bulletin, Dec. 2007. Web. 2 Dec. 2010. <http://www.prb.org/pdf07/62.4immigration.pdf>.
