Week 9: Cartography

For this week, I created three maps based on data from the 2000 U.S. census.

Here is a map showing the Asian population percentages for each county. Unfortunately, with all three of my maps, some county polygons failed to calculate properly when I used the GIS calculator to find the population percentages. So, on all three of my maps, there are certain polygons that are left white and blank because the calculations did not go through. I was unable to fix the problem in GIS or locate the attributes that were not calculating properly. Out of all the three maps I made for this lab, it appears on this map that the Asian population seems to have the most varied locations where its population percentage is greatest. While the most concentrated Asian populations are on the west coast in California, some higher percentages are also scattered in various spots on both coasts, and some are scattered in the midwest.

Here is a map showing the black population in the U.S. It is obvious that the population is more concentrated in the South: out of all the three maps I made, this map has the clearest trend. I like how the color scheme worked out for this map's symbology: using a color scale that uses light shades to denote smaller percentages to heavy or bright shades of red for the larger percentages helps viewers more clearly see the large concentrations of the black population in the South.

Here is a map of other population percentages, namely populations that are not any of the U.S.'s major racial or ethnic groups, such as the Caucasian, black or Asian populations. I tried a new kind of  color scheme for the symbology on this map: this time, the darker blue colors indicate a higher percentage, while anywhere that is green will have low percentages from 0 to 2.31 percent. I thought this two-color scheme was appropriate to use for this map, since the range percentages that the green color represents are all pretty much the same, because they are so small. Thus it would not be a poor choice to use one color shade to represent these multiple percentage ranges. This color scheme allows the viewer to see more starkly and clearly where the variation in racial groups are, which are mostly in California and in the southwest. Also, the darker blue shades help denote where the diversity is even greater in these areas. However, I can see how this color scheme could lead to pitfalls, since it might lead the map viewer to make generalized assumptions about the area covered only by the same shade of green.

Considering the actual data involved in the making of these three maps, I'd say the third map of the "other" racial population percentages surprised me the most. I did expect the most diversity in California, but I did not expect as much diversity in Texas, New Mexico and even Colorado. It was unfortunate that a handful of counties were unable to be calculated in each map. But after completing three other exercises using GIS, these maps were not difficult for me to create. The only way I think these maps could be misleading is the fact that some counties are larger than others. Thus, the larger counties that take up more space on the map lead to a greater generalization of that area, while the other smaller counties are more accurate because they attribute a statistic to a smaller area. There could be any amount of variation within the larger counties that are masked because the entire county is covered by one color.

I have more respect for GIS than I did when I first started this course. I used to think it was too difficult for any common, non-GIS-trained person to use, but now I see that as long as somebody gets an initial introduction to how to use ArcMap, they can easily start making maps on their own. It especially helps that, when you place your cursor over each command button in ArcMap, it gives you a description of what that command does. I do wish I knew if ArcMap had more spatial analysis capabilities, since the only ones we had time to learn about in lab were overlay analysis and some raster analysis functions. But overall, GIS is very helpful in going beyond the capabilities of traditional print maps to portray information. It is much more flexible and allows the user to manipulate data using different geographic coordinate systems with precision, helping to eliminate the chance of human error. GIS is in some ways limited, however, whenever there are errors or glitches in the ArcMap program.