Data Log (hsubrama)

This log runs from 7AM Saturday, 02/07/2015 to 7AM Sunday, 02/08/2015.

  • Snoozed alarm once and then woke up
    • My alarm clock app keeps track of my sleep schedule
  • Responded to a text message
    • My text conversations are stored in the iOS Messages app
  • Exercised
    • Treadmill recorded calories burned, speed, distance, etc.
    • Recorded exercise in fitness app, which collects data about my exercising and my meals
  • Showered and got ready
    • Water usage is recorded Cambridge Water Department
  • Did laundry and paid with MIT Tech Cash
    • Electric company records power consumption (of various electronic devices)
    • MIT records tech cash balance and transactions
  • Went to brunch and ate
    • Bon Appétit (provides MIT dining) records consumed food so they know what to cook and what to charge
    • MIT records that my ID card was used for food
    • I wrote a comment card because they served a brunch meal that I liked, Bon Appétit collects this information
  • Filled up water at filtered water fountain on campus
    • Fountain has LED display indicating total gallons of water poured
  • Worked on homework
    • MIT records connection to wireless networks (ex. MIT SECURE, MIT GUEST)
    • Google records some data from searches, calendar events, documents, email, etc.
    • Amazon Web Services monitors my running servers and collects various statistics on them
    • GitHub records my activity history
    • and all the other websites record information about my visit
  • Downloaded new podcast episodes
    • iTunes records my podcast downloads
  • Sent a few emails
    • Emails were archived
  • Left to go pick up gift for loved one, called a car to take me there
    • App records each time I call a car, and my location
    • Credit card company records charge when I pay
  • Picked up gift
    • Store security cameras record activity
    • Store records sales
  • Returned back to dorm and called brother
    • Phone company records call history
    • Swiped ID to enter dorm and use elevator, this is recorded by MIT
  • Worked on homework
    • Websites track my activity
    • Answered Piazza (MIT classes use this Q&A service) question and asked Piazza question – these were archived on Piazza
  • Factory reset corrupted tablet and reconfigured
    • Configuration info was sent to both HP and Microsoft
    • Downloaded apps are tracked by Microsoft
  • Listened to music
    • Spotify records my songs and various other data about my visit
  • Watched movie with friends
    • Netflix recorded our place in the video, when we started watching, and what we watched
  • Visited another MIT dorm
    • Front desk required ID, MIT recorded the access
  • Worked on homework
    • By downloading new C++ library, the hosting website recorded the download
  • Got ready for bed and made calendar events for next day
    • Google calendar recorded my calendar event and other data from my visit

Wealth Inequality in America

Wealth Inequality in America is a popular YouTube video by author “politizane” with a simple message:

Not only is the problem of wealth inequality in America worse than ideal, it’s worse than you could imagine

The video shows three wealth distributions from a Harvard study about what Americans think an ideal wealth distribution should be, what they think the true distribution looks like, and what it actually looks like.

politizane’s wealth distributions represented as stacked bars.

I believe what makes the video so captivating (over 16 million views) is the way politizane turns his data into a story about the victimization of his audience (lower and middle class Americans).

Politizane begins by displaying three stacked bars with overlaid animation to show that the ideal, expected, and actual wealth distributions are vastly different. In doing so, he makes it clear that wealth inequality is far worse than imagined. But he does not stop there. Instead, he animates a 100 small stick figures and places stacks of money on top of them. As he explains how the actual wealth distribution is worse than expected distribution, dollar bills fly from the poor stick figures to the rich stick figures. Although he’s ultimately just presenting histograms, politizane builds a story – we begin in a world of wealth equality until the rich take most of money away from the poor and middle class.

politizane’s “money histogram” showing dollar bills flying from the the poor to the rich and illustrating the difference between an ideal wealth distribution and the expected distribution

In this video, we see the difference between presenting charts and data storytelling. Stacked bars give us distributions and the main point, but politizane’s stick figures give us a protagonist (poor and middle class), an antagonist (the rich), and a plot (the rich have taken money from the poor) while utilizing a simple histogram as the basis. In about 5 minutes, politizane has shown his audience the data, made them feel victimized, and given them an enemy, thanks to his storytelling.