Category: assignments

Girl Scout Cookies

I chose to review a data infographic about the very popular and delicious Girl Scout cookies. Anyone who is a fan of these cookies would enjoy this infographic. It doesn’t have an agenda or stance but rather just provides lots of fun facts and history. The end goal is to build interest. The infographic is long, so I’ve clipped out the best parts.

The image above appears at the top of the infographic. I like the way the green ties together the number and the banner. It would be even more impactful if the number was directly related to the data in the green box. Otherwise, the actual layout of the top is busy and not very visually appealing.

The picture above shows how much each girl scout cookie contributes to their total sales and includes how many calories worth was sold. The bar graph below shows how many boxes of each cookie were sold. It uses two colors but doesn’t identify what the different colors mean. Plus, there is another row of cookies below it that have no discernable purpose other than to add clutter. Also it would’ve been nice to know how many calories each cookie was because without knowing that, quantifying them in calories is impossible to interpret. This can technically can be calculated from the provided information, but it just makes me think that there must’ve been a better way to coordinate the information in the two main graphs. I also really don’t like the colors. They all clash with each other and it makes me eyes hurt to look at the infographic for the amount of time it took to write this.

Later on in the infographic, it has pictures of old, discontinued cookies as well as a recipe for the original cookie at the very bottom. The information is really fun to know. It is worth noting, however, that the graphic has an odd jumble of information. The focus on calories in the first half made me think it was going to have some sort of message about health. Then, by the end, it was telling me to make cookies. Thus while the infographic is interesting, it has no overall purpose and the parts don’t work together effectively.

http://www.dailyinfographic.com/the-business-nutrition-of-girl-scout-cookies-infographic

Exploring Sustainable Energy Policies Around the World

The internet is littered with poor data presentations on climate change. The storytelling in the charts is often ineffective, and the connection to human activity is often absent. In many cases, the “so what?” and the “call to action” are missing from these data presentations.

One example of the effective presentation of data is a new tool by the World Bank to rate sustainable energy policies in more than 100 countries. Referred to as “RISE” (Regulatory Indicators for Sustainable Energy), the tool is a scorecard that grades countries in three areas: energy access, energy efficiency, and renewable energy.

As shown below, the map enables users to explore a country’s policies and regulations in the energy sector, and also compare scores (more than 25 indicators are tracked) across countries. Users can also download the underlying data.

Source: http://rise.worldbank.org/

Source: http://rise.worldbank.org/

The intended audience for this tool is policymakers, since it can help them identify policies and regulations to expand and improve sustainable energy. However, anyone who is interested in sustainability or climate change will find the tool valuable.

The RISE tool is effective because it is interactive and communicates the data visually. It also displays relevant information and filters it down to the most essential bits. Most importantly, it provides actionable information, enabling the user to make data-driven decisions.

It can be improved in a few ways, however. For example, enabling the user to change key parameters easily and run simulations would provide greater transparency and insight into the specific actions that would be required to reach specific goals. The tool could also be improved by adding functionality to recommend policies or actions to take based on a country’s profile (geography, resources, demographics, regulatory/legal framework, politics). Still, RISE is a remarkable tool that can serve as an example of effective data presentation on climate change.

Data Log 2/21: A Day Observing My Data

In this day and age, it is very difficult to go through one’s daily routine in a networked economy without generating digital data in some way. Indeed, you almost have to go out of your way to consciously avoid certain activities, tasks, and behaviors that are routinely tracked and captured in digital form.

The following activity log illustrates both the frequency and breadth of the data that I generated in a single day.

  • 7AM: Wake up. My smartwatch senses that I am moving and provides a summary of my sleep.
  • 7:30AM: Turn on cable TV while eating breakfast, and select and save programs to watch later.
  • 9AM: Go on the internet and read news articles, check and send email, and update my calendar.
  • 11AM: Walk to MIT campus. My smartwatch provides a summary of my walk.
  • 1PM: Listen to music on my smartphone. I receive recommendations based on my listening habits.
  • 2PM: Order grocery delivery online and receive automatic email notification.
  • 2:15PM: Purchase books on Amazon and receive automatic email notification.
  • 2:30PM: Play guitar and record my playing on an iPad app, which sends the information to the Cloud.
  • 6:30PM: Exercise on treadmill, which tracks my workout (along with my smartwatch).
  • 7PM: Use CharlieCard to board the T and travel from Cambridge to Boston.
  • 7:15PM: Visit store and use smartwatch to purchase items.
  • 7:30PM: Visit convenience store and use credit card to purchase items.
  • 8PM Use Uber app on my smartphone to request a pickup to go home.
  • 10:30PM: Change thermostat setting in my apartment.
  • 11PM: Watch TV and update my saved programs.

The resulting activity log is interesting in several regards. First, since it captures only the activity that can be tracked digitally, it can result in an inaccurate portrayal of how one’s time is spent (or more generally, how a system functions or behaves). In my case, for example, I spent more than five hours during the day reading and studying, yet that activity was not captured digitally.

Second, the data that is generated can be categorized in many different ways: location tracking, motion detection, and transactional, for example. Data can also be captured for different purposes: health, entertainment, operational efficiency, convenience, surveillance.

A less obvious attribute of the data log above, however, is the degree of awareness associated with the capturing of data in each activity. Some behaviors (such as requesting an Uber ride) are active and require more consciousness and explicit consent about the data that is being tracked. Other activities (such as walking around in areas that have IP surveillance cameras) are more passive and subconscious with regard to data, and consent is usually implicit. In all cases, however, vast amounts of data are being captured digitally.

 

Paul’s Data Log – Living a live with Google

Android smartphone, Chrome browser, Google Maps, and Gmail. Regardless you recognize it or not, using an iPhone or Galaxy; Google is near to us more than we are expecting. Even Google is playing a crucial role to make our lives much easier, many people are unsure how much and how often they are using it and which kind of data are provided for. A simple way to check is the Google Dashboard where we can see our data and account activities. I want to illustrate how my typical day looks like with Google services.

9 AM – Managed schedule (Google relation: Google Calendar, Android phone)

10 AM – Ordered food visiting Instacart (Chrome browser)

11 AM – Watched YouTube videos (YouTube, Android phone)

11:30 AM – Visited Dell.com to check my order status (Chrome)

12:00 PM – Checked email (Gmail, Android phone)

1:00 PM – Visited Accounting-simplified.com to learn Finance languages and added the site to the bookmark bar (Chrome)

2:00 PM – Searched the route to a Japanese restaurant in Alston (Google maps, Android phone)

3:00 PM – Made a call to confirm dinner (Contacts, Android phone)

4:00 PM – Ordered battery for smoke alarm replacement (Chrome)

5:00 PM – Read Study.net articles (Chrome, Android tablet)

6:00 PM – Went to Japanese restaurant using Uber (Google maps, Android phone)

9:00 PM – Visited Stellar (Chrome)

9:45 PM – Updated apps in my phone (Play Store, Android phone)

10:30 PM – Updated course folders downloading new materials (Chrome, Google Drive)

Willie Zhu Data Log

iPhone

My iPhone is constantly on, and it’s probably both a) the device I use the most, and b) the device I have on my person the most. It tracks:

  • My sleep—when I sleep (based on when I stop using it)
  • Location—even though I’ve turned as many permissions off as I can, it’s still tracking my location. Good when I need Find My iPhone, but otherwise…
  • My contacts—who I call/text/message, and when
  • App usage—it knows all the time I’ve wasted on Reddit
  • Cell network—every cell tower I connect to
  • Apps—the apps I use definitely track my actions. My most used: Inbox, Spotify, Reddit, Messenger
  • Wifi—my phone is a walking data beacon that spews probe requests, uniquely identifying myself to anyone willing to listen

Computers

My Macbook, Windows desktop, Athena workstations, and so on

  • Operating systems—Windows 10 collects all kinds of data without my permission, and macOS probably does too
  • Intel ME—pretty much all of my computers have Intel processors
  • SSH logs—connecting to remote computers leaves logs behind
  • Athena—MIT probably tracks just about everything I do on Athena

General

  • Browsing history—this goes without saying. I’m pretty unique, according to https://panopticlick.eff.org/
  • Security cameras—MIT has cameras everywhere
  • Card taps—Similarly, there’s all kinds of places I need to tap to get in. It goes without saying that MIT records all of them
  • Purchase history—every time I buy something with a credit card or TechCash

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.