Module Six-Assignment Six

Example One:

The information I found related the median amount of money earned by people with varying degrees (lol) of education. The information shows that people who have achieved a professional degree or higher typically earn quite a bit more money than people who do not have this level of educational experience. I was also interested in the fact that people with high school diplomas did not make a whole lot more money than people who did not graduate high school. In fact, there was not really a terribly large wage gap until the graph reaches professional and doctorate degrees. For my visual representation, I remade the graph but where the original source used bars to graph, I used pencils of varying lengths and sharpness. The people who didn’t graduate high school and were likely to make the least amount of money were represented by a broken pencil and people who earned a higher degree wee represented by a full length, fully sharpened pencil. Pencils are obviously related to education and sharpness might refer to intelligence.

Example Two:

I found an article on the Thinknum website titled “The opioid problem: Who’s buying, who’s selling, who’s hurt most”(link: https://media.thinknum.com/articles/guess-which-age-group-has-the-biggest-opioid-problem/) that found people in their fifties and sixties to have the highest rate of prescription opioid overdose. Two things in the article inspired my data visualization. First, the graph “Poisoning Death Rates by Age” shows the death rates of different ages from 1963 to 2011 and the author points out that starting in the 70’s when 20-somethings were the highest, each successive decade is topped by successive age groups (20s in the 70s, 30s in the 80s, 40s in the 90s, 50s in the early 2000’s, 60s in the 2010’s) which lead the author to believe that it’s the baby boomer generation throughout all these years that are super prone to opioid overdose. I wanted to visualize this by ordering the people standing in line in front of a pharmacy counter by age, so the oldest looking man first in line to get his prescriptions represents his age group in this statistic. The second part of the article that interested me was when the author points out that we need not forget the other most important player in this phenomenon: the pharmaceutical company and those distributing the drugs to begin with. There was a 7.3% increase in opioid prescriptions from 2007 to 2012, and while I don’t aim to account for these particular numbers in my visualization, I did want to set it in a pharmacy in order to shift the focus from solely the people themselves to other contributing factors to their use.