Julia’s Data Journal for 2/8/16

Below are the activities I did that generated some form of data collection on Monday, February 8th, 2016. It was a relatively normal day, and when I was forced to think about it I was surprised at the number of activities for which I generated over the course of an average Monday.

  • Used iPhone (texted, checked email, browsed web) | Apple and Gmail app collected information on geographic location, metadata on email communication, metadata on web clicks
  • Used iPhone (Google Maps) | Google Maps app collected data about my geographic location
  • Bought coffee, Charlie Card, sandwich (used credit card) | Charles Schwab collected financial information about where/when I made purchases
  • Rode MBTA (swiped Charlie Card) | MBTA collected data about my location/transit riding
  • Entered library at Tufts (swiped Tufts ID) | TUPD collected data about when I entered buildings on campus and my geographic location
  • Logged onto Tufts wireless network | Tufts IT department collected data about my computer (i.e. IP address, network usage)
  • Browsed the internet | Companies collected information about my preferences based on what I clicked on
  • Listened to Pandora | Website collected information about my music preferences to tailor my station
  • Called an Uber | Uber app collected information about where I was and where I was travelling, and also credit card information
  • Watched TV | TV companies collected information about my TV viewing habits

Gary Burnett Data Log 2.6.16

Android Smartphone | Constantly tracking position, communication, etc.

Media Lab study | Jawbone constantly tracking my sleep and daily activity

Fraternity Elections | Obtained a new position. Data logged into meeting notes

Tap into MIT Buildings | Info from my ID logged into MIT systems

Ride on the T | Data from the Charlie Card in my MIT ID logged into T system

Purchase Movie Ticket | Credit Card information logged into AMC system

Purchase Food from Verde’s | Used a TechCash gift card, so no personal data

Uploaded Cover Photo for Facebook Event | Data about events I’m attending

Iris’s data log: 2/6/16

  • Throughout the day: sent/received emails and texts, logged content and date/time by app software
  • Throughout the day: water and electricity usage, logged by water/electricity meters
  • Took the T: tapped RFID, logged account and value on account by MBTA
  • Ate at Boston Burger Company: paid with credit card, logged payment information by Boston Burger Company, and location by credit card company
  • Entered the Broad Institute: tapped RFID, logged name and time of entry, as well as elevator usage by the Broad
  • Entered dorm: tapped RFID, probably logged things but not entirely sure what was logged? by MIT
  • Watched TV: logged viewership by show network

Felipe’s Data Log for Saturday, 2/6/2016

As part of the CMS.631 Spring 2016 class, I decided to log all of the “data-generating” actions/activities that I did throughout the day (data-generating being defined as an activity that generates data that is being actively collected at that moment). Here is a log of what happened from the moment that I woke up until right now when I am about to go to sleep:

Format: Activity ; data sent ; entity that logs that data.

  1. Turned my iPhone’s “Do Not Disturb” feature off ; Data about my phone settings; Apple iPhone log.
  2. Sent a few texts with my iPhone ; Text-based message files ; Apple software and phone service provider.
  3. Signed into apps like GMail, Notability, Google Maps, etc ; User information like location and app-specific action data ; Application provider.
  4. Presented a ticket for a beer/wine tasting event ; User information such as name and ticket ID ; Tasting event manager who is verifying that only paying customers enter.
  5. Used my credit card to pay for lunch at a restaurant in Chinatown ; Personal financial information such as credit card # ; credit card payments processing company + restaurant.
  6. Used my credit card to add value to my T card ; Personal financial information such as credit card # ; MBTA.
  7. Used my T card to tap into Park Street metro stop ; Personal location + basic info ; MBTA.
  8. Used my MIT ID to get into MIT buildings ; Personal location + basic info ; MIT.
  9. Took a picture with my phone ; Picture file + associated meta-data ; iPhone log.
  10. Sent emails to various people ; Content-specific information ; Google.
  11. Sent a few texts with my iPhone ; Text-based message files ; Apple software and phone service provider.
  12. Modified a Facebook Event ; Content-specific information ; FaceBook.
  13. Communicated with parents via FaceTime ; Meta-information about video stream ; Apple/iPhone log.
  14. Used my credit card to pay for dinner at a restaurant in Kenmore Square ; Personal financial information such as credit card # ; credit card payments processing company + restaurant.
  15. Sent emails to various people ; Content-specific information ; Google.
  16. Sent a few texts with my iPhone ; Text-based message files ; Apple software and phone service provider.
  17. Modified a Facebook Event ; Content-specific information ; FaceBook. 

    It is very interesting to see on even just a somewhat superficial level how much data is created during normal day-to-day operations due to the technology-based and data-rich communication and interaction systems that we have today. It is also a bit scary, and brings to clear light the necessity for ethical use of the data and the importance of data privacy and protocols.

Shutterstock Top Color Trends of 2015

Top Color Trends of 2015 – Shutterstock [source]

Shutterstock, a company that provides millions of royalty free stock photos, illustrations and vectors, has published a collection of visualizations that provide an analysis of color trends from the year 2015. By matching pixel data with image downloads, Shutterstock was able to identify trends in the fastest growing colors across their entire collection and the most popular colors by country. With stunning and beautiful professional quality photos, this data visualization acts as an eye-catching lure to the shutterstock website for commercial purposes. The bottom of the page leads you to a signup link and an email address for press to contact the creators of the visualization.

Fastest Growing Colors

fast

The first visualization displays the four most popular colors of 2015 based on image downloads. The colors are given in hex value (#01B1AE, #2E4A17, #40A1AC and #1F2A44) along with high resolution example images that are predominately that color value and consist of simple geometric and repeating patterns. All four images are deep blues and purples.

Colors Around the World

world_colors

The second graphic is interactive and displays the most popular colors of 2015 by country represented on a pixelated map. Colors by country (also given in hex RGB) vary greater than the fastest growing colors; Colors by country consist of peachy skin tones, forest greens, rocky grays, and ocean and sky blues. Clicking on a single country reveals the top three colors from that country along with some example images that are predominately those colors.

For all of the hex color values and example images Shutterstock’s search engine provides similarly colored photographs. The infographic demonstrated the power of the Shutterstock’s search engine and the high quality images that Shutterstock provides for its customers. This visualization acts as a beautiful, productive and convincing argument to use Shutterstock for your next design project.

 

The Zika Virus Explained

Vox, the news site that markets itself as the go to destination for explainer journalism, or journalism that breaks down the broader context of the news issues currently topping headlines, recently did an explainer article, using 6 charts and maps, on the zika virus.

Zika is a mosquito borne virus which, until recently had been of limited import. Though the Aedes aegypti and Aedes albopictus mosquitoes that carry the virus have long existed in the West, including in the southern parts of the United States, zika only migrated to the Western Hemisphere in 2013. And, unlike malaria which is also carried by Aedes aegypti and Aedes albopictus mosquitoes, zika is rarely deadly. In fact, people often don’t even know that they’ve infected either because they don’t show symptoms, or the symptoms – fever, rash, joint pain, or pink eye – are easily confused with that of other illnesses. Symptoms, if they occur, generally clear up within twelve days.

Still, Zika has spread incredibly rapidly in 2007 there were 14 cases diagnosed worldwide to an estimated 1.5 million in 2015. And in the areas zika has spread, so too has the increase in microcephaly in newborn babies. From the article:

The country has seen an unusual surge of Zika cases over the past two years — possibly after the virus arrived with World Cup travelers in 2014. Last year, more than 1.5 million people were affected.

Over that same period, Brazil has seen more and more newborns born with microcephaly, a congenital condition that’s associated with a small head and incomplete brain development. Normally Brazil gets several hundred cases a year, but since October 2015, health officials have reported more than 3,500 cases.

graph of incidences

According to the CDC, microcephaly is linked to seizures, a decreased ability to learn and function in daily life, feeding problems, hearing loss, vision problems, and developmental delays.

tiny head

As images of children with microcephaly has swept the web, panic has followed, with some countries telling women to delay pregnancy by as much as two years, and people – including women are who are not pregnant and don’t plan to be soon – cancelling planned vacations to affected areas.

The VOX article serves to temper those fears, with facts. The use of a drawn image of microcephaly  is good because it helps to visualize the issue without stoking panic. They also do a good job of pointing out that what is known is less frightening, and very narrowly impacts specific populations who should.

I also appreciate their tempering of the issue while pointing readers in a direction they should be concerned: everything suggests that at least in the United States currently, zika is manageable. There have been no domestic transmissions, but climate change is what likely allowed zika to spread so rapidly, and other more deadly diseases may be lurking behind it. Climate change is what we should really be afraid of.

zika countries

climate countries

 

2015 Year in Music

At the beginning of each year, Spotify refreshes its “Year in Music,” where it ranks the top artists and albums of the year and also publishes interesting statistics, such as how many tracks and how much time we’ve listened to music this year. The content can be the sum of all Spotify users, but can also be customized specifically to the user him/herself. Below shows an example screenshot, where Spotify totals that the world listened to 21 million different artists in 2015.

changycj_yearinmusic

Spotify 2015 Year in Music

On the website, the user can scroll through multiple panels, each of a different statistic. As the user browses through the website, different songs will play depending on the context. For example, on the Top Tracks panel, the song “Lean On” by Major Lazer plays, as it is the top track in 2015, according to Spotify.

Spotify publishes this content for its users to summarize global trends and also expose music habits and preferences the user him/herself may not know about. Overall, I think the statistics are very interesting, as they are very relatable, but I thought Spotify could have made it even better by having better visualization of these data. For example, a line chart could be drawn for the number of tracks we’ve listened to over years, instead of simply a line of text saying that we’ve listened to “167,493 more than last year.” Similarly, pie charts could be constructed comparing the top genres/artists across seasons, instead of separate panels with one-liners, “We loved Ellie Goulding, Wiz Khalifa, and Major Lazer this Spring” or “We finished the year strong with a lot of Justin Bieber, The Weeknd, and Drake.”

With these visual changes, I think Spotify can make its Year In Music incredibly relatable and interesting for its users, as they could explore their own taste but also compare it to the rest of the world’s.

Fact Checking Politicians

The New York times recently put out a chart rating each presidential candidate for their accuracy of statements. The statements were grouped into six categories, ranging from pants-on-fire lies to true. The post is aimed at a wide audience, from adults who are trying to choose a candidate to the candidates and their teams. With this graph the New York Times both urges average citizens to think more critically about what candidates say and pushes candidates into having to be more careful with the way they present data.

truth-lies

New York Times article- All Politicians Lie.

The chart on a whole is relatively clear and easy to read. The color scheme chosen both helps to differentiate between true and false and I appreciate that the author steered clear of loaded colors within the two party system. It is easy to find general summary information located along the left and right side, and to get a sense of the candidates general ranking. However, there are a few problems with this representation. The first being that, although the criteria used to place statements into categories is listed, it is still a pretty arbitrary metric. In addition, some of the candidates have vastly more statements that have been checked, and this information is not presented clearly.

I feel as though this presentation is effective in sparking an interest people to fact check candidates and think more critically about what they are saying. There has also been a strong response in campaigns around fact-checking journalism. The graph uses humor (title of categories) as a way to engage readers, however, I also feel that this takes away some legitimacy of the information being delivered, which is exactly what the chart is trying to fix.

The Fallen of World War II

Screen Shot 2016-02-04 at 11.11.58 AM

The Fallen of World War II is an interactive, data-driven documentary about the casualties of WWII. The documentary’s punchline, however, is that despite contemporary sentiments and contrary to what the media may make us feel, we are in fact living in a period of ‘long peace’. What this means is that we are now less likely than ever before in the recorded history of mankind (!) to die in battle.

The documentary’s central data visualization tool – the bar chart – is simple and accessible. The beauty of this piece is that it uses the easily legible bar chart in exciting new ways that really drive home some of the most insightful points that the documentary makes. For example, in order to highlight the extent of military casualties of the Soviet Union, the video slowly follows a new bar that rises up for almost a whole minute, eventually towering above the equivalent bars that detail German and French military casualties. This toggling between micro and macro views of the data on offer helps the viewer realize both the overall human life cost of WWII but also how each country fared comparatively to one another. What’s more, the sound effect used when each bar is presented – which alludes to casino chips falling – highlights the way civilians and soldiers alike were often used as pawns by their respective governments.

Besides the inventive use of the bar chart, the documentary is effective for many reasons. Together with a cross-country comparison, the documentary offers the number of casualties from each country involved in the conflict across time. The interactive bar chart (seen in the image below) allows for multiple variables to be taken in at once: month and year, nationality and even specific battles/events are available for the user to browse through.Screen Shot 2016-02-04 at 12.52.05 PM

Another effective technique in this documentary is the presentation of the same data in different ways. For example, when talking about the number of Jews killed during WWII, the documentary first arranges that data per country, and then arranges it again by cause of death (gas chambers; mobile killing squads etc).

Screen Shot 2016-02-04 at 11.10.28 AM

Beyond data visualization tools, the documentary uses narration and still photography in a way that (1) ensures the audience understands the data presented and (2) adds a ‘human’ and historic element to the story.

Screen Shot 2016-02-04 at 12.51.42 PM

 

 

World AIDS Day, 25 Years Later: What Have We Learned?

AIDS_Report_Infographic_800px_wide1

This is an infographic produced by the ONE Campaign, an international campaigns and advocacy organization dedicate to ending extreme poverty and preventable disease in Africa, that tells the story of the progress made against HIV/AIDS over the past 25 years (this post was published in 2013) and what’s needed to rid the disease from the planet.

 

The data shown next to the image of Africa illustrates the progress select African countries have made against AIDS by measuring the ratio of people newly infected over the people newly added to treatment (assumed an annual measure) and introduces the “tipping point” as the moment when the total number of people infected is equal or less to the number of people newly added to treatment. They then categorize the select African countries into four buckets: (1) Reached the Tipping Point; (2) Close to the Tipping Point; (3) Acceleration Needed; and (4) Progress Reversed. From the data presented, it’s clear that while many countries have made great progress or are on there way to reaching the tipping point (21 countries), there are still 16 countries where acceleration is needed or progress has been reversed. To illustrate this difference, the infographic compares the state of HIV/AIDS in two countries: Cameroon and Ghana.

The audience for this report U.S. policy makers and international development officials.

The goal of infographic is to drive home the message that while progress has been made, significant challenges lie ahead. And the use of data around HIV/AIDS deaths is a powerful reminder of the difference that smart, effective and accessible interventions can have.