Category: assignments

Tiffany’s log Feb 8, 2016

  • Cell Phone: Constantly tracking emails and other notifications
  • Water usage: washing face, brushing teeth, showering
  • Food: Bought from La Verdes and swiped in dining
  • Availability: Filled out a doodle form for a meeting 
  • Computer: Used to check emails, check Facebook, use Stellar for classes, filled out availability for meetings
  • Dorm: tapped into front door, security worker logged in time I entered dorm
  • Tech Shuttle: shuttle driver documented what time and how many people entered shuttle
  • Electricity usage: charged computer and phone, used lights in room and bathroom

Andrew Mikofalvy Sources of Data log 2/8/16

  • Number of steps vs time; data taken from both smartwatch and smartphone
  • Heart Rate throughout the day; taken from smartwatch
  • Credit card purchases made throughout the day; gathered from online statements
  • Websites visited throughout the day; gathered from chrome web history
  • Computer usage throughout day; logged through special time tracking computer application
  • Number of people interacted with; tracked by social interaction application
  • Food consumed throughout day logged by time; tracked by calorie counter smartphone app
  • Water consumed throughout day logged by time; tracked by calorie counter smartphone app
  • Motion During sleep; taken from smartwatch

Jane 2/8 Data Log

  1. Made a phone call • Tracked by AT&T
  2. Texted throughout the day • Also tracked by AT&T
  3. Carried phone around throughout the Day • Location tracked by Google
  4. Paid using Debt Card • Tracked by Bank of America
  5. Used MIT ID to tap into Dorm • Tracked by MIT
  6. Paid using Tech Cash • Tracked by MIT
  7. Streamed Music on Spotify • Tracked by Spotify
  8. Did a Google Search while logged into Google • Tracked by Google for smart article suggestions and frequent searches
  9. Tracked the package I ordered last week • Tracked by UPS
  10. Collected Aluminum Can Pressure Data in Lab • Kept track in a course spreadsheet
  11. Watched Netflix • Tracked by Netflix
  12. Posted a picture on Instagram with geotagged location • Tracked by Instagram

Phillip Graham’s Data Log – 2/7/16

Data created by me for the day. (To the best of my knowledge)

  • Guest Swipe on a BU Student’s Meal Plan for Breakfast: A guest swipe was used up by my friend which is stored on their system
  • Paid the fare/Swiped onto the T at Kenmore: Fare was deducted from my charlie card
  • Used my student ID to swipe myself in as a guest to Burton Conner
  • Made a purchase with a credit card at La Verdes Convenience Store
    • Data from card, and for internal store purchase
  • Watched the Super Bowl on our TV: viewership data
  • General phone data created from:
    • Snapchat
    • Facebook messenger – sending messages
    • Phone call
    • Sent emails from phone and text messages
    • Tinder
  • Accepted an invitation to an event on Facebook

Data Log Michelle 2.8.16

  1. Android smartphone (calls and texts) | data logged by phone software/at&t
  2. checked gmail |  content specific info by google
  3.  took multiple busses |location and charlie card data logged by mbta
  4. tapped ID to get into massart building |student ID # recorded, time, location
  5. tapped ID to get into massart print shop | student ID # recorded, time, location
  6. sent in my taxes | tax info/ ID info recorded by federal, state goverments and turbotax
  7. checked voter registration | ID info recorded on gov website
  8. bought theater ticket| credit card information/ email contact into recorded by ticketmaster
  9. listened to pandora | likes, dislikes, skips recorded by pandora to track and personalize stations
  10. browsed internet | data recorded on what I look at to better advertise to me
  11. facebook | data collected to better advertise to me
  12. watched tv | viewership data collected by networks
  13. bought curtains on amazon | credit card and shipping info collected by amazon

Katie’s Data Log

Data created on February 7th:

  • Health app on iPhone tracks distance/steps walked and stairs climbed
  • Ski Tracks app tracks one of my skiing runs
  • sent text messages which are both stored on my phone and by AT&T
  • bought lunch with my credit card – restaurant and my credit card company have information
  • took photos with my phone
  • posted a photo to Facebook with a tagged location
  • listened to music on Spotify
  • entered information into the Calendar app on my phone
  • responded to emails
  • Nike Running app tracks the distance and speed of my run
  • swiped my ID card to get into Baker Dining
  • watched an episode of Late Night with Steven Colbert on CBS.com
  • wrote down this list in the Notes app on my phone

data.dump()

Sunday (2/7) was pretty light for me in terms of data creation. I was traveling for most of the day, so most of the data I create regularly by browsing the web was eliminated. Here’s what’s left:

  1. A constantly running health app on my phone tracked and stored my location and some other sensor data.
  2. I was in a car for several hours, so I probably showed up on at least one traffic camera.
  3. I sent multiple texts throughout the day.
  4. Other passive data usage on my phone was also tracked.
  5. I participated in a singing competition which was recorded and will probably be posted online.
  6. I was in a group photo which will also likely be posted online.
  7. I listened to music on Spotify, which keeps track of what I play.
  8. I watched the Super Bowl, whose viewership, like other TV programs, is tracked.
  9. I watched a show on Netflix, which also keeps track of these things.
  10. I checked my email, informing the mail server that I’ve looked at the messages.
  11. I followed a link to a website that very likely tracks visits.
  12. I looked at some course websites to plan my week.
  13. I used electricity, which shows up on my bill each month through an electronic system.

I probably missed some things, but if a day in which I spent the majority of my time doing very little produces this, it would be a daunting task to enumerate every point of data I produce on an average day.

Visualizing Binary Data

Original visualization and Author’s comments

binvis.io isn’t so much a statement as an exploration of what files on computers “look like”. In essence, it takes a binary file containing arbitrary data and generates an interactive visualization which allows users to inspect regions of the file for values such as byteclass (useful for distinguishing text from other data, for example), local entropy, and byte-level details. Admittedly, the target audience of this presentation is likely technically-minded, but the key ideas apply to a lot of areas. To keep it short, I’ll go over just two of them.

BinVis

The first is the representation of parametrized data in 2D. Many visualizations try to present multidimensional data in 2D, often using 3D computer graphics and animation. This visualization tackles the opposite problem: presenting a 1D sequence of points in 2D. The author exploits spatial locality by clustering contiguous regions of similar type, but also allows simple sequential display of the data on the grid. He also explains the techniques he used right in the help menu.

The second feature is that information isn’t withheld. For credibility, a presentation should be verifiable by giving sources or making the raw data available. This visualization takes this idea a step further by showing the raw data directly alongside the visualization, albeit in readable chunks. An even cooler feature is that the data which the user is focusing on is highlighted in each of the components (see the screenshot above). This creates a visual mapping between the raw data and the presentation, a powerful technique for showing connections across levels of abstraction and inviting users to ask their own questions.

Maddie’s Data Log, February 7

  1. Bought a new journal at Paper Source: purchase and credit card information collected by Paper Source; purchase, location and time information collected by Bank of America
  2. Bought groceries at Roche Bros: purchase and credit card information collected by Roche Bros; purchase, location and time information collected by Bank of America
  3. Rode an Uber back to campus: location collected by Uber to personalize my experience, to display my history in receipts, to use for analytics at an aggregated level, etc.; purchase, location and time information collected by Bank of America
  4. Swiped into the dining hall: time and location collected by Wellesley Fresh to track dining hall traffic
  5. Wasted time on Facebook: information about how I use the app and who I am collected by Facebook for advertising targeting
  6. Bought some Super Bowl snacks at CVS: purchase and credit card information collected by CVS; purchase, location and time information collected by Bank of America
  7. Watched the Super Bowl: my party’s viewership counted by CBS, Nielsen, etc.
  8. Took photos on my iPhone: location and time collected by iPhone (Photos app)

Kalki – Data Log – 2/5/16

  1. GPS location – continuously tracked by smartphone
  2. Amazon.com history – I ordered stuff
  3. Phone logs – every time a call is made/received
  4. Email – every time an email is sent/received
  5. Web browsing (laptop and smartphone) – recorded by websites, my browser, internet service provider
  6. Electricity usage
  7. Water usage
  8. Text message records – every time a text is made/received
  9. MIT Stellar – every login with MIT certificates
  10. File backup version history – Time Machine backups
  11. Automatic software usage monitoring/reporting
  12. Shipping parcel tracking – My Amazon.com order

Just having 12 datasets being created seems too little. I expected that in 2016 more of my activities would be recorded in some way or another.