Note: This is an ongoing document. I will add more ideas here as things come up
Daylight All Over The World
Time is the measure of change. When nothing changes, time ceases to exist. The sun rises, shining light onto a sliced portion of the earth that was previously dark. As the sun sets, an equally sized sliced portion of the earth goes into darkness.
Although the geographical area that is currently lit by the sun is more or less constant at any given time (let’s pretend there is one universal time), the number of people who are experiencing it is fluctuating (due to the geographical unevenness of the world’s population).
I would like to create an “instrument” that displays the number of people that are experiencing daylight at any given time.
Distilled Summary in Color
Time can be linear or cyclical depending on the scope we’re looking at.
Assume we’re following the gregorian notion of time. The hour of the day goes up and up, never decreasing. Except for at the end of the day when it goes back to zero again. The day of the month goes up and up, never decreasing. Except for at the end of the month when it goes back to zero again. The month of the year goes up, never decreasing. Except for at the end of the year when it goes back to zero again. But the year, it goes on and on and on. The buck stops with the year.
I want to create a distilled summary of each “cycle” (hour, day, month, year). One of the ways I can do this is to take a snapshot (picture) at specific moments of time. That picture represents the “state” at any given time. Ultimately, each snapshot can be distilled again to a single color (or maybe a series of dominant colors).
My plan is to take snapshots from a couple of public webcams all over the world over time, distill them, and display them in a series. Each “cycle” will have its own “summaries” which will, in turn, be used to “summarize” the greater cycle. Creating the color palette of time in a particular place.
Sirenes in New York City
New York City is extremely loud. We have police cars, fire trucks, and ambulances going around at all hours of the day.
My apartment is facing a pretty busy street (4th ave in Brooklyn). I would like to track how many vehicles with sirenes pass by my apartment over time.
My plan is to train a machine learning model to recognize a siren from a vehicle. I would then run it in a raspberry pi with a mic to detect each time a sirened vehicle pass by my apartment.
The final result would be a visualization of this generated data.
Half-Baked Ideas
- Something with tracking kairological time in chronological time vice versa
- Using flight tracking data
- Something with git commits
