Income Inequality Data Visualization

Source: https://youtu.be/QPKKQnijnsM

I (re)watched this video on income inequality recently, and found it to be both compelling and informative. Its narrative is clear: that income inequality is an issue is common knowledge, but the magnitude of income inequality is far greater than the public believes.

The video displays three charts: one demonstrating what people want the income distribution in the US to be, one showing what people actually think it is, and one of what it actually is. Personally, I was taken aback at the true magnitude of the income gap, and how staggering the difference between the poorest and wealthiest Americans actually was. The video’s strategy of reducing the US population to 100 people made it a lot more relatable—for many, it’s a lot easier to picture a room of 100 people than to talk about generic percentiles.

However, I feel that the video could do more than merely presenting the information in a provocative manner (which it does well, both visually and psychologically). Instead, the video should end with a call to action, perhaps by providing links to organizations dedicated to fighting the income gap. Even though I had just watched the video, I felt no more empowered to actually do something about the problem.

Data Log 2/15 (ashwang)

  • 12:00 AM – Watching the Netflix show ‘Freaks and Geeks’ – my stopping point is saved, and Netflix records my ‘TV show preferences’.
  • 10:00 AM – iPhone
    • iPhone Alarm app saves my settings for a wakeup alarm, keeps track of how many times I snoozed.
    • iPhone Health app keeps track of how many steps I’ve taken, flights climbed, and whether I was running or biking.
  • 10:20 AM – Facebook, Gmail, etc
    • Facebook messenger tracks my conversation history, which messages I haven’t read/seen yet, etc.
    • Gmail’s Inbox app also tracks which messages I haven’t read yet, which ones I’ve started to respond to but haven’t sent yet (as drafts), and which ones I’ve labeled as ‘pinned’ or ‘later’.
  • 11:00 AM – Package addressed to me arrived at the New House desk. Its location was stored, an automated e-mail was sent out.
  • 1:00 PM – 6.813 Lecture
    • nanoquiz results submitted using Google Forms
    • Their Stellar site logs the last time I visited the site, as well as my answers to the reading questions.
    • Microsoft OneNote records notes I’ve taken throughout class
  • 3:00 PM – 18.065 Office Hours
    • WolframAlpha stores my last few searches
    • Gradescope records the pictures/pdfs I upload and when I upload them.
  • 5:00 PM – swiping into Dorm using MIT ID
    • Security camera captures image of me going in through the door
  • 7:00 PM – added myself to German House late plate list online
  • 11:00 PM – Used Hubway key in at the closest Hubway stop to check out a bike. The # bikes, # docks information changes on apps like Bos Bikes, and Hubway keeps track of my biking time.
  • 11:30 PM – Used Credit Card at Shaws to buy food.

Christian´s data log of 2/14/2017

08.00 Woke up.

Checked my Harvard and two private email accounts.

Checked Facebook, Twitter, Slack and WhatsApp accounts.

09.00 Sent WA messages in team group for European Conference.

09.15 Put on Nike AppleWatch with GPS sensor tracking how much I move.

 

09.30 Bought a coffee at Starbucks using my Debit Card.

09.45 Logged into Harvard Wifi network.

10.15 Used Maps on IPhone to figure out best way to MIT

10.30 Add value to my Charlie MBTA card using my Debit card.

 

11.00 Bought a coffee at MIT student center (Debit Card)

11.10 Logged into MIT Guest network, surfing the web (various news sites)

11.30 Shared a picture I shot earlier on my fellowships´s Slack channel.

14.30 Data story-telling studio: surfing the course´s blog for data viz homework.

16.15 Purchased book at MIT Coop with Debit card.

 

16.30 Book a trip with Uber paid with German credit card. Driven route is stored by company.

17.30 Logged into Harvard Wifi network. Posted tweets via Iphone app.

 

21.00 Bought movie for class with AmazonPrime, streamed it to my laptop.

23.00 Updated information on panel for European Conference via shared Google docs.

23.30 Watch German news broadcast via app.

 

In addition to that, I am pretty sure there is a lot of data collection that I don´t think about. I was recorded by CCTV cameras. Multiple apps on my phone collected data and exchanged with there servers. File on my laptop are also in iCloud or Google Drive.

Sam’s Data Log for 2/15/17

Ongoing throughout day:

Sending/receiving emails

Social media visits (Instagram, Facebook)

Text messaging

Google searches

Google docs/drive modifications

MIT website visits

 

Other activities

8:00 AM – Tapped MIT ID to get into Z-Center gym

8:50 AM – Tapped MIT ID to exit Z-Center gym

8: 55 AM – Purchase record at LaVerdes

10:00 AM – Downloaded Arduino

11:50 AM – Delivered tuition check to MIT Student Financial Services

11:55 AM – Purchase record at LaVerdes

1:30 PM – Accepted two Google calendar invitations

3:00 PM – Took ecological footprint survey online at http://www.footprintnetwork.org/resources/footprint-calculator/

4:00 PM – Tapped MIT ID to get into Athena cluster

4:15 PM – Used MIT printer

4:30 PM – Joined Piazza site as a TA for 15.S50

5:00-7:00 PM – Constant internet use, various Google apps use, communication via text/email

7:00 PM – Attendance recorded in evening class

9:30 PM – Team GroupMe set up

9:55 PM – Checked MIT Saferide app and took Saferide (Maybe data collected?)

10:30 PM – Watched Netflix

Most used throughout day: Email, Google Calendar

GiveDirectly – send money directly to people living in extreme poverty

Source: https://www.givedirectly.org/

In the field of development economics there are two main points of view on how to most effectively lift poor people out of poverty.  One school of thought is that the only way to help them is to give them access to resources like livestock, housing, food, etc.  Another more controversial point of view is that the most efficient way of helping them is to give cash directly to them and allow them to help themselves.  This website, GiveDirectly is one particular organization I came across recently that allows donors to give cash to the extreme poor with the push of a button.

 

Its home page displays several images and quotes from recipients of cash transfers, and it also displays several data-based graphics.  One pie chart shows the percentage of every donated dollar that actually ends up in the hands of the poor.  Two line graphs show the annual donations and the households enrolled in the program from 2012 to 2016.  The photos, quotations and data visualizations on this page clearly targets individuals in developed nations who have a passion for alleviating world poverty and have the capacity to donate money.  As can be seen by the big green “Give Now” button in two locations on its home page, the main goal of the site is for every visitor to donate money.  This goal is supported by both qualitative images, as well as quantitative data.  The qualitative information appeals to the emotional side of the visitor.  We see real faces, read real quotes, and can watch real live video of the people who we are helping.  The quantitative information appeals to our desire to see the result of our actions.  What percentage of my money is going into their hands? How many households are involved? How well is the program performing as a whole in comparison to other similar programs?  These are the questions that the graphs and chart answer. The upwards trend of the graphs along with many big “+” signs and even the choice of green for the text all give the viewer the idea of growth and money.  This is likely what the creators of the graphics had in mind.  The viewer is emotionally and logically driven to donate money because of the imagery and data that indicate that their money will improve lives and promote growth out of poverty in an efficient manner.  I therefor find the infographics on this page very effective.

 

Space Exploration in our Solar System

Source: http://www.sciencealert.com/this-glorious-map-helps-you-keep-track-of-every-space-mission-in-the-solar-system

I really like science posters, but this one has a special place in my heart. It contains a massive amount of information, but still presents a relatively simple narrative of the wonder of space exploration.

This poster lays out information about all of NASA’s space missions from 1959 to 2015. On the top half, the flight paths are shown in thin lines, recording which celestial bodies the spaceship orbited. The bottom half of the chart contains more information such as the target of that spacecraft, the purpose of that mission (such as a flyby, orbiter, or landing), and what the spaceships looked like.

The main focus of the poster is on the spacecrafts, not the correct spatial orientation of our planets and their moons. As a result, the primary audience for this poster infographic is a space exploration enthusiast, someone who would enjoy knowing ridiculously specific trivia about NASA’s space missions.

Notably, this poster also focuses primarily on NASA space missions, and doesn’t mention any probes sent by European or Asian countries. In addition, flyby missions are still represented with a flight path that orbits the entire planet or moon, exaggerating the flight path a little. This suggests that the Pop Chart Lab that created this poster primarily wanted to celebrate NASA’s achievements in space exploration. In addition, the sheer scale of this poster was probably meant to inspire wonder and awe in NASA’s accomplishments.

Personally, I felt that this poster was effective for me, although the bottom half takes up too much space. However, the general public may be overwhelmed by the data overload.

 

Almaha’s Data Log for 13/02/2017

9:00 am
Woke up and checked WhatsApp, Messenger, and Snapchat. Replied on messages and shared my morning snowy view in Snapchat.

9:30 am
Started browsing for good egg recipe, found a great one!
10:30 am
Used BBM to video call my friend and family.

12:00 pm
Started interviewing new graduate students that want to join our group using appear.In, I highly recommend this app if you want to conduct group meetings.

2:30 pm
Created a list of all the things I need to get from Star Market using iPhone reminders app.

4:00 pm
Worked on my research and did a bit of coding with the help of my dear friend Stack overflow.

6:00 pm
Chatted with my friend so he can teach me a new spaghetti recipe through Messenger.

8:00 pm
Watched a lecture and took notes using Notes app. Then, solved some programming problem sets while listening to a playlist in Spotify.

9:30 pm
Used my Apple watch to track my workout session.

10:30 pm
Binge watched Sherlock season 4.

As an overview of my activities through the day, I noticed that I heavily depend on apps and technologies to practice my daily needs.

Divya’s Data Log – 2/12/17

In approaching this data log, I first tried to think about what qualifies as data “created by me.” I decided that this data would have to be recorded as a result of my activities, or continuously in the background regardless of my actions (i.e. apps collecting background information).

12 am: Pay for dinner

Order recorded in restaurant system & credit card transaction recorded by restaurant as well as my bank

1:30 am – 7 am: Drive to Boston

Spotify saves data on music listened to, songs skipped

Google Maps saves data on my route, how fast I’m driving, when I stop

EZPass stores time, location data on my transponder each time I drive under an electronic tolling station

Gas stations record credit card transaction, amount of fuel used to fill tank, type of gasoline used

Car records mileage and speed, and uses these to calculate real-time fuel efficiency

Longer term, I’m contributing to broader carbon emissions data

7 am: Enter dorm

Scanner records MIT ID number, security camera records entering the dorm and allows security guard to verify identity before allowing access

Turning lights on and off – recorded by energy companies

2 pm: Wake up, get ready

Phone records number of times snooze is hit before alarm is disabled

Amount of water used to brush teeth, shower, etc. is recorded by utilities companies

3 pm: Do laundry

Amount of water used recorded by utilities companies

Time machine is started and length of cycle stored in LaundryView tracking system

4 pm: Work on computer

Documents on Microsoft Office & Google Docs save versions at various times, file sizes, word counts

Texts/messages/Snapchats saved on servers, along with time and recipient data

Websites save data on page views & IP addresses

Google searches saved to optimize future search results

9 pm: Walk to meeting

Phone records number of steps taken/equivalent flights of stairs climbed, as well as location changes

11 pm: Watch TV on computer

Number of views, points where program is paused/skipped/rewinded are recorded by xfinity

Data Log

I broke down the data recorded by device, as most of the data I generate during the day is through my use of electronic devices. An “Others” category at the end shows any data I generated by other means.

Phone/Tablet
  • Alarm Time: I use my phone’s alarm to wake up. The phone stores the alarm time, and possibly as well the number of times I snooze my alarm.
  • Email: One of the main things I do throught the day is use my email, either on phone or on my laptop. This records the emails I send, receive or delete.
  • App usage: Both phone an tablet record the apps that I use every day, as well as the time I spend on each.
  • Reading interests: I frequently use the Reddit app and the BBC News app in my phone/tablet to read content online. Hence, which stories/posts I read are recorded.
  • Location: My android devices passively record my location throught the day.
  • Messaging: When and what the contents of the messages I send throught the day, usually through apps like Facebook.
Laptop
  • Browsing History: Most of my internet browsing (either because of work or lesuire) is done on my laptop. My browser will record the pages I visited, and possibly how long I spend on each site.
  • Download History: As well, both the browser and my downloads folder will have a record of the files I downloaded.
  • TV Show tracking: I use an online service to track the TV Shows I am currently watching.
Other
  • Credit Card History: When I use my cards through the day, that will create a record of what I bought and when.

Tony’s Data log for 2/13/2017

 

Nearly everything we do that interacts with technology is logged and recorded somewhere – either personally or by the company that owns the technology.

  • Emails – Sent a few emails during the day.

  • Messaging – Sent messages through Facebook Messenger, Slack, and iMessage.

  • Internet – Responded to a Google docs IM sports survey, completed Doodle for 18.657 office hours. Browsed the internet adding to my search history. 

  • Work – Printed and read papers for my UROP.

  • Payment – Used credit card to pay for an Etsy purchase, dinner, and an Uber. Recorded by bank and credit card company. Charged people on Venmo for meal from a few days ago. 

  • Games – Played Pokemon Go. App records location and event data to send to Niantic.

  • Exercise/Health – Ran 1.5 miles, treadmill also displayed time spent running and calories burned. Fitbit recorded steps, sleep quality, etc.

  • ID – Used my student id to swipe into dorms and to print from an Athena cluster.