In the end I decided to do the visualization project on Asthma. This is a topic close to home because as a kid I always had really bad Asthma but I’ve been lucky enough to have it much more controlled through medicine. Asthma affects 235 million worldwide and affects a variety of countries, not just developing countries, but the developed ones as well. In the US alone, there’s about 25 million people with asthma which is about 11 percent of the American population according to the Census Bureau, an increase of 4.3 million people since 2001. I took the data from one article, The Global Burden of Asthma from the Chest Journal. I wanted to use the words in the article as well as the numbers to visualize the information in different ways. I feel that visualizing the words used as well as the data shows the article in a more dynamic kind of way. I would like to preface that this article admits that its information is lacking in regards to Africa, which may be as a result that there are not a lot of doctors who can diagnose asthma.
This first visual is a classic Wordle. I simply copy and pasted the article into the Wordle generator, changed the color and played with the settings. Wordle takes text and then creates an image based on how often particular words appear. In the article asthma is the most used word so it’s the biggest. And words less used are smaller. Since this article is about how global asthma is, the most used words seem to fall under the category of how common it is, like “prevalence,” “management,” and “patients”. There are no often used words that indicate a specific nationality thus demonstrating the world wide affect asthma has.
This visualization is a word tree. A word tree takes a word that you type in from the article and then the program then shows the different contexts that that word appears in. In this tree I typed in “asthma” and the most common context it appears in is with “prevalence.” An interesting thing to note was that in this article when I typed “asthma” only four countries were closely associated with the term. Ironically, when you type “global” only one hit shows up, but it is also interesting that no specific country comes up in that association. Unfortunately the colors are very somber and does not seem very visually attractive which may make this visualization less effective because it does not keep the reader’s attention, and if a problem with the website occurs (which seems to be quite prevalent on www-958.ibm.com) there is no motivation for the reader to fight through the problem and continue exploring the context in which keywords appear.
This last visualization is a little more traditional form of demonstrating the article. In the article there was a section where the author listed the percentage of individuals affected in certain countries. Unfortunately there were only a couple of countries listed which indicates how this article is not very well rounded in its data. While the article preaches the global affects of asthma there is not a lot of hard data to back up exactly how much asthma affects people globally. A problem with this visualization is the lack of color variety. It is very dull and boring looking. Preferably, I would have loved to use bright colors, that correlate with asthma. Like use blue to indicate the more asthma prevalent countries to represent how if you have an asthma attack you do not have oxygen in your blood which is blue when it’s deoxygenated. And red for countries with less asthma rates to indicate blood with oxygen.
This fourth visualization serves to supplement the previous one. In case one isn’t very good with maps then this visualization give one a very simple and straightforward comparison of how prevalent asthma is globally, and this way the size of the country does not accidentally mislead the reader into thinking that just because the country is smaller that they have less asthma. This is purely a percentage comparison across countries.
Link to article: http://chestjournal.chestpubs.org/content/130/1_suppl/4S.full