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Center for Advanced Digital Applications

Introduction to Data Visualization, final project

There are two primary purposes to the Tableau data visualization below, which summarizes data available from the U.S. Bureau of Labor Statistics and was designed with a target audience of junior-high/high-school students in mind. The first is pretty straightforward where by that time many students will know, or think they know, whether or not college is in their future. Instead of being presented with a random list of jobs that are expected to grow in the near future they can click on an "Educational Attainment" level, e.g. High school diploma or equivalent, and see which occupations tied to that education are likely to pay more. The length of each data bar represents expected growth percent but that value is relatively unimportant - the growth % is an estimate and a difference between 20% and 30% probably won't have much practical effect. The color of the bar is tied to (2012) salary levels though, with darker green for occupations earning more. Using color for a quantitative value can be tricky, especially when color varies not only in brightness but also in actual hue as below. However in this case the color is really only intended as a reinforcement to the actual dollar value, which is also noted on the same line and is marked in concordance with the bottom axis.

The secondary purpose of the data grid is to encourage pursuit of higher education. Simply by listing the higher-education, higher-wage occupations first a viewer will likely find the salaries listed later in the data grid a bit discouraging. The idea isn't so much as to push a tired-of-school student into anesthesiology as to motivate someone considering the health care field to see that only a few more years of schooling could result in a $70k median salary (dental hygienist) as opposed to $24k (physical therapist aide).

The tie-in to the MSA (Metropolitan Statistical Area) map above is intended as more of curiosity, where it can be fun to see how wages differ across regions, and in which cities certain occupations tend to be concentrated. Of course that wage information is in isolation and no relative cost-of-living numbers are provided. Also, the bubble size, which indicates number of people currently in an occupation, doesn't tell much on its own as NYC usually has the biggest numbers, simply because it is working off of a larger population base. A more valuable visualization would consider a given occupation's "Location Quotient", a number also provided in the Bureau of Labor Statistics data. A value > 1 here implies that the percentage of local workers in that region is greater than the national average. A well understood example would tell students that if they really want to be an actor, moving to Los Angeles may be helpful. A less obvious one indicates that, at least among the 11 MSA below, Washington D.C. is the place to be for historians.