Category: data log

Data Log – 25th Feb, 2019

Following are the data I created on 25th Feb, 2019

Iphone

Alarm Clock

Weather Forecast

Calendar – Both schedule for today / to do list for the week

Battery usage

App engagement (Wechat, Whatsapp, Instagram, Cloud Music, Messenger, Ebay, Taobao etc)

Website I visited (Google & Google attachment, Hupu etc)
Emails received and sent

Laptop

Website I visited (Google & Google attachment, Stellar, MyHarvard, Harvary Libray, Ebay, Hupu etc)

Photo modification in Photoshop

Sending photos via wetransfer

Homework assignment

Financial Calculation

Grocery Shopping at Market Basket (-$33.13)

Winning auction at Ebay (-$89.99)

Diecast model sold on Taobao (transfer RMB into USD on calculation, +$238.9)

Dudley House lunch & Dinner (-$25.2)

School
Portrait photography shots for 13 ADPD students

Class attendence

Health
6733 steps on foot

3.1 miles on bike

Others
Water usage

Electricity usage

In just one day, when sick at home, I generated the following digitized data. If all of this information were to be aggregated (much of it is), it could be used to track someone’s every move, and predict their future movements. It’s quite alarming how much information that is saved in digital forms, when we are not even trying to generate data.

Health Information

1. Temperature Readings
2. Medicine Dosages
3. Calories / Dietary Intake
4. An almost constant GPS location

Writing & Communication

1. Assignments
2. Text Messages
3. Audio Clips / Video Clips
4. Research Notebook

Entertainment

1. The time, order, volume, precise location, artists, and song information that I listen to on Spotify. Additionally, how much each song is played for.
2. A list of websites (browser history), what I did on those websites (cookies), what I looked at on those web sites (ads, Google/FB), Key strokes (Microsoft, Samsung), Mouse movements
3. Video playlists
4. The lengths and times that my phone is on

Finance

1.  Bank account transaction info
2. Credit card transaction info
3. Tip amounts
4. Itemized Receipts
5. Referrals to purchase orders
6. Times / Types of items purchased

Intentional Data – Data that is created with explicit intent to create data

1. Homemade Movie animation frames.

Following are the data I created on a day;

iPhone/PC
Screen time
Battery usage
Data usage
App engagement (Facebook, Instagram, Spotify, Slack) – time, in-app activities (likes, messages, etc.)
Music preference
Location
Words I searched on google
Website I visited
Emails received and sent
New events on my calendar

Health devices
Sleep duration and quality
Heart rate and Respiration (rate, variability, inhalation/exhalation time)
Number of steps
Weight, Body fat, and BMI

School
Class attendance/participation
TechCash usage

Exercise
Check-in time for a gym
Distance and duration I biked, and calories I burned

Others
Water usage
Electricity usage
Food I ordered
Payment information by credit card

Roughly chronologically, this past Sunday:

1. My iPhone logs when my alarm went off and how many times I pressed the snooze button.
2. I send conversation data to Apple via iMessage. I also generate data about my location and device usage, which is possibly anonymized and sold/distributed to third parties.
3. I adjust the temperature on my thermostat and take a shower, which sends data to Eversource to tell them how much energy I have used and how much they should bill me.
4. I also use a shower speaker to listen to podcasts in the bathroom. My podcast app collects data on what I’m listening to, for how long, etc and shares it with third parties.
5. Does the act of being alive count as a data point? By consuming resources and expelling waste, I am also producing data (e.g. environmental studies monitoring the production of CO2).
6. When I take the bus to Allston, I share data with the MBTA about my (or, my CharlieCard’s) whereabouts.
7. On the bus, I am also captured by the CCTV cameras. On my walk to/from the bus stop, I’m sure I am captured on other cameras as well.
8. I’m sure I open my social media and email apps numerous times during this trip. Here, I create data that can be turned into information about my interests, my network, how I spend my time, where I am, and so on.
9. I visit a coffeeshop where my partner works. I don’t give transaction data (because the meal is free) but, as my order is entered as a comp order, I generate data about the # of free meals being given out that day. That is also stored by the third party point-of-sale service.
10. More cameras in the coffeeshop. Perhaps this footage goes to the owners, but I suspect it also goes to some sort of hosting service/server on its way back to those folks.
11. Back at home, I open up my browser to look up a recipe for beef stew. My machine and browser collect data on me, as does the website I visit and any third-parties I bump into.
12. I go to the grocery store and give Market Basket my transaction data as I buy some food. I also give my bank data about this transaction – perhaps they share that with third parties, too?
13. I cook and give more data away, thru the same podcast and energy routes as before.
14. While I eat and watch Netflix, I am creating data about my watch and browse patterns.
15. Going to bed, I set my alarm for Monday morning – generating more data to be stored in my iPhone and passed on to Apple and others.

Over the course of a Saturday in Cambridge, I create the following data simply by living and conducting my regular activities:

• How long I sleep and what time I wake up, based on when I set and turn my alarm on the previous night, and what time I finally stop the alarm in the morning
• News and public information I consume, given that I usually start my day reading the latest on Twitter, LinkedIn, and Apple News.
• Which friends I check up on and engage with, based on whose Instagram pictures and Stories I view and ‘like’, as well as WhatsApp and Slack messages I read and respond to
• Based on my email reading, sorting, and responses:
  • Topics and senders I think are important by marking them with ‘star’ in Gmail
  • Topics and senders I think are unimportant by immediately putting them in ‘Trash’ in my Gmail
  • Topics and senders I am deprioritizing / procrastinating on by reading the message and then later marking as “unread” in Gmail
• Where I tend to spend my time, based on the location of my iPhone
• My exercise, since my phone must have tracked my 22-story walk up my apartment stairs when the elevator was down
• My spending patterns, after buying items and food throughout the day
• My eating patterns, after logging food consumption / calories in my MyFitnessPal app
• My television preferences, based on Netflix viewing in the evening
• My musical preferences, based on Pandora playlist listening, song ratings, and time spent listening throughout the day
• My attachment to my phone / anticipation of information from my regular checking of the lock-screen to see if a new text message appeared, or checking Instagram, Facebook, Slack, and Email to see if new messages appeared throughout the day
• My transportation movements and price sensitivity via Uber and Lyft checking, requests, and tips
• My movements around the MIT campus, via student ID card swipes and iPhone location tracking