Part 4: Report-Public Health Analysis

As part of the first project in Special Topics in GIS we were to produce a health study based on air quality in the San Francisco bay area. The study is for local bay area county officials to determine if there is a correlation between asthma rates, air quality, and race. This included looking at asthma hospitalization rates, ozone and particular matter concentrations, and which hospitals to target for more resources. The study was broken into three parts: Demographics, A Closer Look at Asthma, and Targeted Hospitals.

Public Health Analysis: Part 1 Demographics

The first study was to look at the uninsured and poverty indicators in the nine counties and to determine the correlations with race, primarily Hispanic and African-American, and single mothers. The main goal was to determine which demographic needed to be targeted for the most help with health care. In order to accomplish this we used scatterplots to show the correlations between each demographic and the uninsured/unemployed. A series of comparison maps with scatterplots were used to determine that the African-American population appeared to be population which needed the funds the most.

Public Health Analysis: Part 2 A Closer Look at Asthma

The second part of the study was to see if there is a racial component to those that are being admitted to hospitals for asthma realted illnesses. By using a series of comparison maps with scatterplots to determine the racial correlation with asthma hospitalization rates we can determine where the funds need to be allocated. Once again the analysis showed the African-American population had the highest admittance rates and Alameda County was the target point.

Public Health Analysis: Part 3

Part 3 of the study included looking at where targeted asthma sufferers may suffer due to point sources of pollution and which hospitals are most likely to be utilized by the targeted population. The study mapped sources of pollution such as Toxic Release Index (TRI) point locations and roadways, hospitals and the distribution of the targeted population at the Census Tract level. These factors were compared by using weighted overlays to identify areas where the proximity to a hospital, the pollution factors and the targeted population could most likely lead to increased staffing and funding.

Results

The overall results found that the there is a clear correlation between poor communities with little to no health insurance. Demographics show that the African-American population has the highest number of asthma realted hospital admittance rates in the bay area and specifically in western Alameda County. After the weighted analysis based on TRI, asthma hospitalization rates, and the targeted population, the hospitals highlighted would need the most help in allocated funds to treat the people most affected. Overall I feel that this analysis was very effective in identifying the section of the bay area of San Francisco where the allocated funds are most necessary.