Census Maps

Above: All U.S. counties are ranked based on the concentration of self-reported ethnicity. Counties that reflect darker shading have higher concentrations of African-Americans. This data set, obtained in 1999, shows the highest levels of concentration in the Southeastern United States. Traditionally Southern states have the highest proportion of self-identified blacks, in which nearly half or more of the population is black. These states include Louisiana, Mississippi, Alabama, Georgia, South Carolina, North Carolina, Tennessee, Virginia and Maryland. While more diffuse, select counties in Florida, Michigan, Texas and California reported higher concentrations of blacks, nearly a quarter or more of the population. The breaks that the software created do not reveal the complexities of the differences in concentration between the 8.11% and 24.25% threshold and the 0.1% to 8.1% as well. This data set would be more revealing if contrasted, for example, with data from the 2010 Census.

Above: Overall, concentration levels of Asians were lower than for Blacks; this was due in part to the fact that there are fewer Asians in the total population than Blacks. In 1999, highest levels of self-reported Asians were concentrated in the West, mostly in the states of California, Oregon, Washington, Alaska and Hawaii. Select areas in the Eastern United States also had concentrated populations of Asians: the New York region appears to have higher concentrations. The threshold, again, is too general to give insight into how these concentrated areas compare to one another. The range, 17.57% to 46.04% does not represent diversity as clearly. Perhaps inserting another tier, ranging from 35% to 46.04% would have better illustrated the distinction. Other regions that had more diffuse, but significant concentrations of Asians, were Florida, Texas, Michigan, Arizona, Nevada and Wisconsin. Again, contrasting this data with more recent data would show trends not currently visible.

Above: Although somewhat ambiguous in its definition, this map reflects the distribution of respondents who identified themselves as "Some Other Race" in 1999. According to the 2000 Census, "Some Other Race" is an "other" category, reserved for self-reporting a race that is not represented by a given category within the survey. Because "Hispanic" or "Latino" is not a given category on the census (the only choices are White, Black or African American, Asian, American Indian or Alaska Native, Native Hawaiian or Pacific Islander, and some other race) this category is likely selected by people who self-identify as Latino. Within this category, there is the option to write in a race, such as Mexican, Puerto Rican, Cuban or some other self-reported race. Assuming that this is the primary group reflected in this map, the populations are concentrated in the Southwest, as well as the Northwest and Florida. The greatest concentrations (20.83% to 39.08%) are located in California, Arizona, New Mexico, Colorado and Texas. Washington, Kansas and Idaho also have a few counties that report relatively high concentrations.

Overall Map Series:

In order to compare these maps to each other- which would be possible, given that the data was all collected in the same year from the same survey- the breaks would have to be set equal to one another. As the maps are now, they only reflect concentrations of one race compared to total population, whereas if the breaks were set to match one another (in increments of 10% for example) then, it would be possible to compare concentrations of race by county nationally. These maps are only useful insofar as they show distribution at a given time period; however, these are static frames that do not reflect moving or changing patterns of dispersion. Because these maps were created in GIS, it would be possible to make them dynamic, given more data (from other years) and other variables, such as housing information or some other factor, to show other trends.

Overall Impressions of GIS:

Now that I better understand how GIS uses Excel tables, I can imagine using GIS in new ways. Prior to doing this lab, I didn't understand how I could find data on the web and harness GIS to analyze it, but after undertaking the process to find data sets, download them, edit them, upload them and join them, I feel like I understand how to use it in the future for more complex tasks.