June 3 (Day 4) — Clara

I started the day by watching some of the videos on cyberlearning. Connected Worlds featured an interactive learning world with six ecosystems connected by a single body of water. The goal of the project was to have people draw out complexities in environments as visitors would have to use STEM cells in the exhibit (observation, experimentation, engineering). Using Data Visualizations to Empower Informal STEM Educators attempted to make an application for younger students to have a self-paced experience, driven by personal interests, to learn data visualization. It made use of “data dashboards” because of their ability to make data “easy to understand”. DataSketch: Making Data-Driven Visualization Accessible to Middle School Youth was an attempt to develop data visualization literacy in a younger audience as well. The application allows kids to make visualizations that are animated based off of data. However, though it was a creative idea, it didn’t appear to have a way to check the accuracy of a child’s visualization.

After watching these videos, Anne and I tested our line making application on the multitaction — it didn’t work. We then changed a few of the functions from being mouse-reliant to touch-reliant, which made our application work on mobile phones and iPads. Still, it didn’t work on the multitaction. We think this is because the multitaction only renders touch feasibility if the code uses the Cornerstone SDK API. If this is actually the case, it’s fairly disappointing because p5.js was proving fun and useful. There is a possibility that we can use the drawLine() and drawPoints() functions of Cornerstone, but the entirety of the Cornerstone library is much more limited than p5.js so it would be a shame if we couldn’t get p5 to work, though we are going to look more into understanding already existing javascript code for multitaction applications next week.

We decided to extend our line drawing application using p5.js anyway. We restricted people from drawing points that were not making the line “grow” (aka the x value needed to be greater than the last x value). Additionally, we had a comparison feature where the slope of your drawn line would be compared to the given line.

When I was exploring some fun visualization analyses, I found a study on emoji trends on Instagram. Essentially, using machine learning results they discovered certain clusters of emojis are often found together while many emojis are replacing words. I thought it was a fun study, one fairly applicable to a popular social network.

Additionally, I discovered this company Qlik which is able to provide a variety of analyses and generate pre-determined visuals when given appropriate data. I was impressed because their generated data dashboards were highly interactive and often offered a very extension selection of options to narrow down certain aspects of the data.