Graduation and median income levels vary from county to county in Alabama. The question our group wanted to answer was how graduation rates affected median income levels. Our first prediction was that counties with high median income levels would also have high graduation rates. Poverty levels in the county also affected the graduation rates and median income levels. We predicted that counties with high poverty levels would have lower median income levels and lower graduation rates. The last variable that we decided to analyze was how population affected median income levels and graduation rates. Our prediction that coincided with the population variable was that counties with lower populations would have lower median income levels.
This map displays median income estimates in Alabama by county in 2011. Shelby and Madison County are the two counties with the highest median estimates. Counties located along the Black Belt like Wilcox, Butler, Conecuh, Sumter, and several others are some of the counties with the lowest median income estimates.
First, we have to locate the counties, county names, and the county boundaries in Alabama. There are sixty-seven counties located in the state of Alabama. From our general knowledge, we knew that counties in the Black Belt region of the state were some of the poorest counties while Shelby and Jefferson were two of the most populous counties. Next, after distinguishing county boundaries, we begin to gather information on graduation rates in individual counties. We found median income levels after graduation rates. After seeing disparities between counties in Southern and Northern Alabama we decided to look to do research and obtain the poverty levels of each county.
The map above displays the
percentage of children in poverty Alabama counties in 2000 and the Black Belt
region of the state had the highest poverty levels.
The map above displays the percentage of children in poverty Alabama counties in 2011 and the Black Belt still produced some of the highest poverty levels while some levels in the region were reduced.
The fusion table above shows population by county in 2000 and 2005, graduation rates for 2000 and 2010, percentage of children in poverty, and median income estimates.
Graduation rates for the state by individual counties were difficult to locate. The formula that has been used in previous years to determine graduation rates has been realigned to produce more accurate numbers. Internet research was the primary method used to find data by county. As we begin to delve into the research for the GIS project we looked at information from Annie E. Casey Foundation and their Kids Count book. The Annie E. Casey Foundation had information on poverty levels by county and after looking at the numbers we decided to use poverty levels and see how they compared with the population of counties and graduation rates.
After looking at the Annie E. Casey Foundation we discovered that the website had the graduation rates by county and year. The Casey Foundation was a good source of information for poverty and income variables.
The results from our findings indicate that population, graduation rates, median income, and poverty levels are all dependent on each other. One variable seems to always affect other variables. Graduation rates seem to be the strongest variable in each county. Counties that experienced a decrease in graduation rates saw a slight change in median income levels over an eleven year period. Counties with higher populations tend to have higher median income levels and the graduation rates also tend to be higher. The counties that have lower populations also tend to have higher graduation levels due to the fact that they are graduating a smaller population. Poverty levels do not seem to directly coincide with lower median income estimates. For example, Shelby County is one of the counties with a high median income estimate at $66,362 and also has a poverty percentage of 17.3. Our initial prediction was that counties with high median income estimates would have poverty percentage under ten and Shelby County along with Madison County proved that our prediction was invalid.
As we began working, we initially thought that graduation rates would be the primary variable that would show the differences of income and poverty between counties. Further along into our research we discovered that graduation rates were not as impactful as population was as a variable.
The map above displays poverty levels, graduation rates, population, and median income estimates. The legend to the left displays the graduation rate increase of decrease between the years of 2000 to 2010.
Located on the Annie E. Casey Foundation website was percentages that showed the percentage of students in each county that received free or reduced lunch as a part of federal funding. The free/reduced variable seems to directly coincide with poverty levels in counties. Median income estimates, poverty percentages, and free/reduced lunches are three variables that seem to impact each other.
The map above displays the percentage of students in Alabama counties that receive free or reduced lunch. The Black Belt region of the state, the poorest region, has a high percentage of students that receive free or reduced lunch. Poverty levels, median income estimates, and the percentage of students that receive free or reduced lunch are coinciding to each other.
In conclusion, we discovered that some of our predictions about population and median income estimates were found to be true because counties with lower populations did tend to have lower median income level estimates. The group also predicted that counties in the south, the Black Belt Region, were some of the poorest counties in the state. (Marengo, Wilcox, Sumter, Dallas, Lowndes, Barbour, Macon) Our results also found that counties with high median income level estimates tended to have higher graduation rates (Shelby County). By conducting our research we discovered that graduation rates across the state do not have a significant disparity like median income level estimates do. The major disparity between counties in the state deal with median income estimates and population. For example, Shelby County has a population of 171,691 and a median income level estimate of $66,362 while Wilcox County has a population of 12,369 and a median level income estimate of $20,990, which shows that are huge disparities between the two counties. This project helped us to discover that population, poverty levels, median income, and graduation rates are all variables that affect each other.
References:
"Graduation Rate." Datacenter.kidscount.org.
Anne E. Casey Foundation. Web. 25 Oct 2013.
"Children in Poverty." Datacenter.kidscount.org.
Anne E. Casey Foundation. Web. 3 Nov 2013.
"Median Household Income by County." http://cber.cba.ua.edu.
University of Alabama. Web. 3 Nov 2013.
<http://cber.cba.ua.edu/edata/emp_inc.html>.
"Poverty Estimates for Alabama Counties." http://cber.cba.ua.edu.
University of Alabama. Web. 3 Nov 2013.
<http://cber.cba.ua.edu/edata/emp_inc.html>.
GIS Group 9
Auna Bailey
Ashleigh Taylor
