The first map I created is the one titled “Percentage of Black Population by County.” For this map, I decided to shift the break points for the data and make them significantly lower than their “natural” location. Whereas the original set of break points included break points relatively evenly spaced up to approximately 90 percent, I chose to have five of my six ranges below 10 percent, with the final range spanning the whole range from 10 to 90 percent (which is represented by a deep red hue). This had two results on the data. Because the top range spans a wide 80 percent range, essentially the entire southeastern region of the map turned red, which visually appeared to increase the number of blacks in the region when compared with the coloration of the map when the original break points were in place. This also visually made it seem that the region with a higher percentage of black individuals expanded north along the east coast of the United States, when in fact a large number of the northern counties in this region that are exhibiting the reddish hue actually contain black population percentages closer to the 10 percent section of the top percentage range. Secondly, skewing the break points to have so many break points representing lower percentages illustrated how truly minimal or absent the black population is across the United States, especially in the northernmost regions. By skewing the break points as I did, I was neither able to show a complete national presence or absence of the black population; however, I was able to boldly contrast the regions where the black populations are the largest (the southeast) and where they are they are the smallest (the northern regions of the United States).
The second map that I created is the one titled “Percentage of Asian Population by County.” For this map, I left the break points relatively close to their original locations by simply rounding off the original ranges to make for visually cleaner resulted in both the legend and on the map. In doing this, two main visual goals were accomplished. Firstly, the percentage ranges of 5.00000-9.999999, 10.000000-14.999999, and 15.000000-24.999999 are spaced relatively evenly across the majority of the nation (minus the northernmost Midwest) in a visual sense. Secondly, by shifting the break points to 25-50 percent, only San Francisco County was able to fall into this range, thus making it the only county on the map with the hot pinkish hue. Because of this, San Francisco County is singled out as the county with the highest percentage of Asians in its population in the entire continental United States (had I also included Hawaii and Alaska on this map, the State of Hawaii would have also illustrated the same coloration of San Francisco County in various counties). In California, the map shows a visual lightening of color in the regions the farther away one travels from San Francisco, which could be appropriately assumed to show a point of immigration (from Asia) and dispersal (as the hue of the counties lightens with distance from San Francisco County). Due to the alteration of the break points for this map, I was able to show a national presence of Asians in counties across the United States, that San Francisco County contains the highest percentage of Asians in the continental United States, and the general path of the immigration of Asians from San Francisco County throughout the State of California.
The third map that I created is the one titled “Percentage of Some Other Race Alone Population by County.” For this map, as there was no percentage of “some other race alone” in a single county higher than 40 percent, I chose to use six break points, with a range of either five or ten percent between them. Had I increased the size of the percentage ranges (for example, turning 30-40% range into a 20-40% range), I could have increased the visual presence of “some other race alone” as I did with the graph that illustrated the “Percentage of Black Population by County.” However, the graph that I have created shows the highest percentage range existing primarily in the southwestern United States. Based on this map and the lack of a “Hispanic” category in the 2000 Census, it is feasible to conclude that this map shows the immigration of individuals of Hispanic dissent into the United States from the southwestern border from Mexico and Central America, provided that counties closer to this region exhibit higher percentages of “Some Other Race Alone” within their borders. However, the highest break point for this map (40%) is lower than highest break point for the other two maps (50% and 90% respectively). Therefore, while “Some Other Race Alone” may seem to have a larger presence on this map based on this sole set of data from the 2000 Census, if it were to be combined with the data from the graph titled “Percentage of Asian Population by County,” “Some Other Race Alone” may not appear to be as prominent.
In this lab, we learned how to portray data in ArcGIS, and in all honestly, I was surprised at how easy it was to manipulate the data to achieve a desired goal. By simply altering the break points for each map, it is very simple to change the illustrated results of each of the maps. With regard to the “Percentage of Black Population by County” map, altering the break points can make it seem like a certain population has more of a presence in a certain region. With regard to the “Percentage of Asian Population by County” map, altering the break points allowed me to highlight San Francisco County as the percentage with the highest percentage of Asians. By doing this, I was able to show a specific point of immigration and dispersal of the immigrant population to the surrounding regions. With regard to the “Percentage of Some Other Race Alone Population by County,” a more even distribution of break points was able to illustrate a gradual dispersal in immigrants entering the United States from the southwest border.
At this point, my overall impression of ArcGIS is becoming significantly more favorable of the program. I found it highly useful to learn how to take a consistent set of data and alter various aspects of the data frame (break points, color hues, color ramps, data ordering, etc.) in order to get the map to show a desired result. After this lab and the last lab (where we made three maps of a specific terrain, showing slope, hillshade, and aspect), I finally feel that I have an understanding of how ArcGIS is used to present data visually. I found the Week 7 Lab to be highly useful for understanding how to represent physical geographic data; however, I feel that the methods that were practiced in this lab are probably used more commonly for data representation. Last time I was asked to write about my impressions of ArcGIS, I felt that performing certain tasks required too many steps, thus making it too complicated; however, as time has progressed and I have acquired more experience working with ArcGIS, I feel that the program is becoming increasingly easier to use and performing certain actions, like creates joins, altering break points, create data layers, importing data, export data, storing data, and other similar actions are as second nature as the tasks and tools in a Microsoft Word program.
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