Correlations
Appendix
Map

             The average cumulative AIDS rate per 100,000 people of Brooklyn, New York is 1933, which exceeds 5.65 times the national rate.  In order to get a better understanding of this extreme high AIDS rate, correlations of various aspects influencing the big increase of AIDS in the area has been analyzed.
            In Brooklyn, there are a total of 37 zip codes.  This would make “n,” the degree of freedom, 35 (n= 37-2).  With the confidence of 95% confidence interval, any correlation coefficient is higher than and between 0.304 and 0.349 would be considered as significant (p=.05, n=35).  However, in all the presented tables and charts, the zip code of 11237, the area of Bushwick, is taken out of the calculation of the correlation coefficients because it is considered as an outlier, which does not correlate with the other data.
 
Family Income v. Cumulative AIDS Rate
            My first approach to the correlations was thinking about how others would stereotype or perceive the Brooklyn neighborhood.  I remember meeting a few new friends that were from out-of-state. When I informed them that I was from Brooklyn, their faces froze and ask me, “Is it dangerous there? Aren’t there shootings everyday?” These questions provoked me in concluding that Brooklyn is an unpleasant neighborhood, and I immediately thought of the link between high crime rates and poverty rates, therefore this provoked me to analyze my first correlation, the family incomes of Brooklyn.  In contrasting the correlation between the family income and the cumulative AIDS rate, I used two distinctive incomes: family income of less than $10,000 and family income of $100,000 to $124,999. According to National Council of Churches USA, the poverty line for a family of four people in 2002 is $18,100 (Cooper, 2002).  According to Table 1, the correlation coefficient of income of less than $10,000 is 0.585262399, and a coefficient of -0.357228157 for the family income of $100,000 to $124,999. These results are highly significant, this means that when family income tends to be lower, the higher the AIDS rate would most likely be. Conversely, there is a negative correlation when the family income is higher, meaning that when the income is higher, the AIDS rate tends to be lower. When the family is in financially challenged, the family cannot afford to pay for the expensive HIV and AIDS testing and medicine. This correlation can be further demonstrated in Graphs 8 and 9, where it shows the general trend and correlation of AIDS rate and percentage of income.

Public Assistance v. Cumulative AIDS Rate
            Similarly to the nature of the family income, I thought the relationship between public assistance and AIDS rate would be significantly correlated.  I obtained statistics of public assistance from the U.S. Census, and found the correlation of the AIDS rate and the percentage of households with no public assistance to have a coefficient of -0.6783 (Table 1).  This negative correlation shows households that do not need or receive public assistance tend to have lower cumulative AIDS rate. The correlation coefficient of percentage of households who receive public assistance is 0.6686, which is highly significant. The households that do not receive public assistance are not below the poverty level, thus are able to obtain reasonable HIV and AIDS testing. Contrastingly, those who do receive assistance are poor, and often are not able to afford to pay for many things, in this case study, AIDS medication and testing.  These correlations can be clearly shown in Graphs 6 and 7.

Education v. Cumulative AIDS Rate
            In accompany to the financial aspects that contribute to the AIDS rate, general education correlates with the AIDS rates as well. In gathering my statistics, I found that the percentage of female who are high school graduates have a negative correlation in relations to the AIDS rates. The coefficient is -0.4581, which is significant (Table 1, Graph 1). In other words, females who have graduated from high school demonstrate to have a lower cumulative AIDS rate. Education allows people to obtain correct information on preventing HIV and be educated about safe sex, thus it would ultimately lower the cumulative AIDS rate. Surprisingly, the males’ education correlations are not significant, and the females’ education correlations did not show significance except for females who are high school graduates. 

 Races v. Cumulative AIDS Rate
            Subsequently, the correlation coefficient of cumulative AIDS rate varies in the Black Hispanic and White population in Brooklyn.  In the Black population, the correlation coefficient is 0.5829, which means that as the percentage of the Black population increases, the AIDS rate increases as well (Graph 10). This trend is also shown in the Hispanic population of having a correlation coefficient of 0.3726 (Table 1, Graph 11). However, contrastingly, the white population has a negative correlation coefficient of -0.6441(Table 1, Graph 12). This shows that as the percentages of white population decreases, the AIDS rate decreases as well. The white population resides in neighborhoods such as, Bensonhurst (11209), Bay Ridge (11204), East Flatbush (11230) and Sheepshead Bay (11235) have the least AIDS rate. These neighborhoods are in the AIDS rate range of 64-1601 per 100,000 people. However, there are exceptions such as Greenpoint (11201) and Park Slope (11217 and 11231) because of the high male population in those areas, thus the rate of men having sex with men is very high. The Black population is predominant in the areas of East Flatbush (11203), Williamsburg (11206), East New York (11207), Bedford-Stuyvesant (11212 and 11213), Crown Heights (11216, 11233 and 11238), Bushwick (11221), and Flatbush (11226).  These areas are all high in AIDS rate, from the range of 868 to 4054 per 100,000.

Family v. Cumulative AIDS Rate
             There is a significant correlation between family status and the cumulative AIDS rate.  I found out that the martial status of people influences the AIDS rate.  In the percentage of married-couple family versus the cumulative AIDS rate, the correlation coefficient is -0.5872, which means that as the percentage of the married-couple family increases, the AIDS rate decreases. Commitment within a marriage keeps the couples from being sexually active with anyone else, except for their spouse. Therefore this explains the negative correlation and the low AIDS rates among married couples in Brooklyn (Graph 2). As the percentage of husband engage in the labor force increase, the lower the AIDS rate (Graph 3) and its correlation coefficient is -0.4558. As the husbands work, they bring back money to support the family and enable the family to be more financially stable. Furthermore, the percentage of the wife not being present is correlated to the cumulative AIDS rate, where there is a 0.5969 correlation (Graph 4). Since there are no wives in the family, men tend to be more sexually liberal, and may have more sex partners, therefore increasing the chances of contracting the disease.  This is also similar to the percentage of the husband not being present, in which it has a highly significant correlation of 0.5628 (Table 1). The women would be more sexually active and may have more sex partners, increasing the rate of transmission of AIDS (Graph 5).

Conclusion
           
There were many interesting correlations that I did not expect to have a correlation in and I am surprised about the education correlations. Education was the trend that I expected to show relationship between the more education that one receives, the AIDS rate would be lowered.  However, the correlations calculated in males did not show any significance, while in the female only the high school graduates show the significance in correlation to the lower AIDS rate. The family income and the AIDS rate correlated the best, and as I predicted prior to calculation that it would be significant. The AIDS rate has correlated with the percentage of Black, Hispanic and White population, female high school graduates, marital status, and financial status.  However, these are perhaps only a few correlations that demonstrated to have a significant relationship, a further research may pose more questions on what to focus on to the correlation of an area where AIDS epidemic is quickly and destructively damaging the general well being of the society in a very subtle act.

References
Cooper, Mary Anderson (2002, October 3). Poverty Increases in the United States, U.S. Census
            Bureau Reports.  Retrieved July 17, 2008, from http://www.ncccusa.org/publicwitness/povertyincreases.html.


US Census Bureau. Retrieved July 18, 2008, from http://factfinder.census.gov/home/saff/main.html?_lang=en.

Zip Code Definitions of New York City Neighborhoods. Retrieved July 18, 2008, from
            http://www.health.state.ny.us/statistics/cancer/registry/appendix/neighborhoods.htm