Visualization is all about portraying information to a user in a clear, comprehensible, easy-to-digest format. As covered in lecture, a good list of rules to follow include Edward Tufte’s graphical excellence guidelines, which include:

Taken from lecture slides by Julie Kientz, 2017.

We took this into account when completing our sprint in studio section.


The activity that we did was centered around how to use tableau and creating different types of visualizations from the same data. For the practice, we worked with bike stand data from Seattle. Taking the skills that we learned from this activity, we created visualizations for 911 incidences in Seattle from 2014–2015.

We were first asked to figure out a user and a research question. Looking at the data, and potential uses for it, me and my partner’s first intention was to make our visualizations target potential homeowners in Seattle. Our visualizations could help them see what types of incidences are common in which areas of Seattle, which would ultimately influence their decision to buy a house or not. This led us to our research question: “What areas in Seattle are safest?”

Our first visualization was a sort of “heat map” of Seattle showing number of incidences per latitude/longitude. This shows areas of Seattle that are more prone to incidences in general, and where they are concentrated.

Our next visualization showed number of incidences over time for each district/sector — which allows our targeted user to understand which areas of Seattle have consistently low or high incidence rates.

The last visualization we created was a graph that showed distributions of prominent crimes in each district/sector. This shows exactly WHICH crimes are prominent, not just which sectors have more incidences than others.

A more interactive version of these graphs are available at:!/vizhome/VisualizationDeliverable_19/Analyzing911Calls

So What?

This activity was especially fun because of the level of creativity that we could employ not only into what data that we put into the visualization but how we portray it. I enjoyed how it was clear to see how different design choices could highlight different aspects of the data that we are portraying. For example, in my last visualization, I originally had planned to include the proportions of every type of incidence per sector — however, I realized that it was much more useful to include only the most prevalent ones — making the visualization easier to understand and interpret. I also enjoyed using Tableau— which made it extremely easy to tailor the visualization to exactly how you want it to be.

Now What?

Visualization seems to be the one design skill that I can take on with me no matter what I end up doing in my career. Knowing good design skills and understanding how to make data easy to understand is a useful tool when making presentations, posters, proposals, and more. Knowing how to make clean and engaging displays is a good quality to have in the engineering field as well, because it will allow more people to understand what you have created and it’s importance. I will most definitely use the skills I have learned in this sprint further on in my college career and beyond.