Throughout history, there have been many epidemics that have plagued the populous of great cities. One example of such a pandemic has been the AIDS virus. AIDS, otherwise defined as Acquired Immunodeficiency Syndrome, took hold of the U.S. population in 1981. Since its peak in the mid 1980’s, and with the first FDA approved protease inhibitor in the mid 1990’s, HIV/AIDS cases have steadily declined on a national scale. With that in mind, I wanted to explore HIV/AIDS cases in New York City, the most densely populated city in the United States. I hope to visualize the relationship between HIV/AIDS diagnosis and neighborhood population density, as well as which boroughs, if any, outbreaks were increasing. I believe the following visualizations would be invaluable to outreach organizations focused on HIV prevention in the City of New York.
fig. 1
To begin, I narrowed down NYC 311 Data to two datasets that fit my needs and that of my audience. In figure one, I used the column “Number of HIV/AIDS Diagnosis” as my Y axis, and “Year” as my X axis. With each line representing a borough, using a line graph allowed me to see trends in the increase or decrease of HIV/AIDS outbreaks in different parts of the City. Our findings show that Queens was the only borough to increase in number of disease confirmations over a five year span.
fig. 2
For figure two, I wanted to observe which neighborhoods in the City of New York that had the most HIV/AIDS outbreaks in the year 2013. This was the most recent year available for diagnosis information by neighborhood. A map seemed like a great candidate for viewing neighborhood data and the variables I wanted to represent. The neighborhoods with the largest dots and darkest shade have the most number of new diagnosis of HIV/AIDS. According to this visualization, Bed-Stuy/Crown Heights had the most new cases in 2013. It was also important to note the neighborhood population to show that there was, in fact, not a correlation between number of outbreaks and population density. I used Tableau map layers to shade the map by population per block. Although I believe a map was the best way to represent this dataset, it does not appear to be Tableau’s strong suit. Upon publishing to Tableau Public, the waterways disappeared entirely.
In the future, I would like to expand on this topic with more recent data. Additionally I would like to re-vamp figure two, and create a more readable map. This would require separation of the waterways, creating a gradient of the population density rather than blocks, and do further research utilizing Moran’s I and spatial clusters to detect “hot spots”.
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