Dynamic Data Visualization

Sometimes it's difficult to present large sets of data in a meaningful way. Numbers and percentages which make sense to us, the holders of the data, may not resonate with those unfamiliar with the concepts or those who are more visual learners. So we create the pie charts, graphs, and other standard visuals that we learned in grade school computer class. These fixed snapshots have been the standard in the world of data visualizations. But lately we've seen technology and computing power open the door to more dynamic presentations that allow us to see and process information in new ways.

Take for example this data visualization created on flowingdata.com. The data set: daily activities of 1,000 Americans representative of the population from the American Time Use Survey from 2014. The setup: A clock, 17 activity categories (color coded), and 1,000 dots (one for each person). The experiment: Run the clock minute-by-minute and move each dot based on what the individual in the survey is doing at that time. The result: Awesomeness.

What this program accomplishes is a means to see both big picture and individually at the same time. I can see that at 5:06 PM 28% of Americans are enjoying leisure, 2% are sleeping, and 14% are still working. I can also track one individual's entire day by following one dot across the 24 hour timeline. I can also (and this is probably the most beneficial) easily see the trends and times which are most notably transition times for different activities.

The author also notes some other unique ways the data was presented, such as plug and play charts that give the user the ability to compare themselves to the data set, or plug a time, gender, and age in to see the activity percentages for that profile. All of these provide cool and interactive ways to understand (in this case) how the average American day is spent.

While this particular model may be more useful for a sociologist than an engineer or physicist, the fact remains that these types of dynamic visuals are becoming increasingly helpful and important in science and engineering, especially as we try to tackle the many challenges and opportunities of "big data". The better we can visualize and understand the information we capture, the more we will be able to share it and use it powerful ways.