When it comes to a â€śtypicalâ€ť day, most of us are too wrapped up in our own routines to think about what others might be doing. Thankfully, this fascinating visualization by statistician Nathan Yau shows exactly how America runsâ€”right down to the minute.

This simulation can be found at FlowingData, and it captures the representative ebbs and flows of a thousand peopleâ€™s average day. Itâ€™s based on data from the 2014 American Time Use Survey, which asked Americans what they typically did during a 24-hour period.

â€śMore specifically, I tabulated transition probabilities for one activity to the other, such as from work to traveling, for every minute of the day,â€ť noted Yau. â€śThat provided 1,440 transition matrices, which let me model a day as a time-varying Markov chain. The simulations...come from this model, and itâ€™s kind of mesmerizing.â€ť

The simulation, which can be run at slow, medium, or fast speed, starts at 4:00 am when most people are asleep, and it gradually builds from there. Daily activities are clumped into 16 groups that are arranged in a clock-like format. As activities change, the dotsâ€”which represent a proportionate segment of the populationâ€”migrate to different nodes.

â€śYou see people head to work, run errands, do housework, take care of the kids, commute, relax, and eat at almost designated times during the day. I stared at these dots longer than I care to admit,â€ť writes Yau.

Here are some screen caps.

At 2:00 in the morning, the vast majority of us are asleep, though 2% have to work, while 3% find time for leisure.

The transition to 7:00 am is dramatic, as a sizable portion of people wake up and assume their daily responsibilities. By this time, around 14% of us are already working.

By 10:30 am, many of us at at work, school, doing leisure activities, among other things.

The evening commute is another major transitionary time.

As the day winds down, we flock to leisure.

Because itâ€™s hard (if not impossible) to follow a single dot, Yau also created â€śstaticâ€ť versions to keep track of where people were going. To do so, Yau drew lines to show the paths, and assigned colors to represent paths ending in a particular activity. Hereâ€™s one example, taken at 5:00 pm.

More at Flowing Data.