Learning to Use Data in Research

Creating Data Visualizations with Annotations

This week, I learned the basics of a powerful tool called tableau in order to create data visualizations to help combat research questions. The question I sought to answer was, “where in the U district is best for students with profound sleeping problems to live, and when should they sleep in order to adapt their circadian rhythms to this busy area?” Consequently, I created three data visualizations using 911 call data from the city of Seattle: a map showing incidences of nighttime noise complaints, a bar graph showing the frequency of disturbances by hour, and a map showing the areas of the most frequent 911 calls. Along with these visualizations, I wrote up a memo stating my user group of interest, the research question I wanted to combat, and an annotation for each visualization justifying its use and explaining how it adhered to the graphical excellence guidelines established by Edward Tufte.

a few of the data visualizations I created using Tableau

Most Enjoyable Part?

Although learning the ins and outs of a software like tableau was frustrating at the outset, the appreciation I gained for its salient data visualizations was rewarding enough to make it worth the trouble. What I most liked about creating the deliverable for this project was playing around with the color settings and different formatting settings. This aspect of it was pleasing to me because I enjoy creating art and playing with color, and this desire was fulfilled in formatting each data visualization and choosing the most effective color scheme.

pair programming in studio was essential for learning how to use Tableau

For Future Reference

Data visualization being ubiquitous in the professional world, I can easily envision myself taking part in a project like this one in the future. One example might be having the task of presenting the monthly trends of sales for a particular product using a software like tableau to convince the audience either that a change needs to be made or that some other improvement is possible. However, any endeavor that involves very little data or none at all would not lend itself as well to such an approach, such as the physical design of an industrial product, which focuses more heavily on principles of structure and appearance and does not concern itself with showcasing and synthesizing data.