The Choking Effects of CO2

Team Members: Almaha Almalki, Krithi Chandrakasan, Erick Friis, Aina Martinez Zurita

Our analysis of data in the World Bank CO2 emissions dataset showed a clear increase in the level of emissions around the world between the years 1990 and 2013. During this time frame worldwide emissions doubled with five countries responsible for over half of total global emissions. Additionally, to highlight the real impact of these rising emissions on society we integrated data showing a positive relationship between CO2 emissions and asthma rates. Between 1990 and 2013 asthma prevalence increased from 4.3 to 8.4%.

Our goal is to raise awareness about the harmful effects on human health that have come about as a result of increased CO2 emissions. We decided to present our data using a short video showing an arm reaching out and gradually choking a person as total C02 emissions rose around the world. The sleeve of the arm splits the emissions of the US, China, and the rest of the world. The width of the boxes change dimension to convey how their emissions have varied over time. We use captions above the arm to narrate the story with key dates and to tie in the important relationship with increased asthma rates.

Our sketch is targeting a general audience, as this is an issue that has the potential to affect all of us. The video with our data is purposely short and to the point, as it is intended to be shared in social media and understood even if the sound is off. While the video is somewhat humorous, the asthma data together with the image of suffocation sends a powerful message. We expect the audience to come away contemplating the impact of rising emissions.

Are You A Pollution Champion? Find Out!

Team: Nina Lutz / Nikita Waghani / Christian Feld

We worked with the World Bank dataset showing CO2 emissions on a country-by-country basis for the years 1990, 2000, 2007-2013. There are plenty of possibilities to analyze the data. If for example you look at the ranking of countries with the highest pollution over time, you see how China overtakes the US, because more and more industrial production is shifted there.

But is that a story that really engages people and sparks critical thinking? We wanted to give our audience the opportunity to explore what role their country plays. We wanted them to have a personalized experience.

Our interactive chart lets users pick one country and drag it to the left side of the screen. One option could also be that the site picks the user´s home country as default. The size of the circle indicates the annual CO2 emission. To compare, users can drag multiple countries to the right half creating a bubble chart. By doing so, the circle on the left side fills, until the added emissions on both sides match. To give the audience additional value, a bubble´s color indicates in which region of the world the country is located.  

This approach turns boring numbers into a story. By playing around with the circles, the audience can better understand what their country contributes to the problem of CO2 emissions and global warming. Through the colors showing regions the users might explore additional correlations. To compare the situation in an historical context, a slider lets users switch between years.

The chart is embedded in a website. Its little introductory article gives context and pulls readers into the story by using a narrative: “For people in the Maldives global warming is theory but an existential question. Their situation is influenced by all of us, around the globe. Find out what your countries impact is.”

The Hidden Emissions of Major Polluters

By Paul Choi, Miguel Garrido, Autumn Jing, and Tony Zeng

Most geographic comparisons of carbon dioxide emissions are based on the countries and regions where the pollution occurs. Hence, an integral part of the current debate about the blame and responsibility for climate change focuses on the emission rates of different sovereign states and geopolitical entities. The U.S. and the European Union, for example, have placed more blame on China in recent years since its emissions have increased steadily (China is now the world’s biggest carbon dioxide emitter).

However, an alternative way to compare emissions is by attributing emissions to the countries where the polluting companies are headquartered. Since companies (not governments or individuals) account for a majority of carbon emissions around the world, it is useful to compare emissions based on where the world’s biggest polluting countries are headquartered. This view reveals “hidden emissions” based on a different geographic lens.

To compare these two pictures of carbon dioxide emissions, we created the sketch below to tell a story. The bottom half of the sketch shows the reported emissions based on the geography of pollution activity. This is the most common measure cited in news stories and reports, and shows that China accounted for 27% of the world’s CO2 emissions in 2010, compared to 16% for the U.S. and 12% for China.

The top half of the sketch, however, reveals the alternative picture based on the geographies of the polluting companies’ headquarters. The data here show that in contrast to the bottom picture, Europe and the U.S. account for a larger share of the world’s CO2 emissions (20% and 19%, respectively) relative  to China (15%).

We believe this sketch tells a compelling story because it contrasts two views of the same dynamic (carbon emissions) and illustrates that there is more than one way to analyze a given problem. We believe the sketch is appropriate because it uses the analogy of a smokestack to show the viewer the relative proportions of carbon emissions. It is also effective because it guides the viewer with an annotated narrative and filters the information down to the most essential bits (six data points in total). We believe infographics like this one can provide powerful evidence-based stories to inform people and make meaningful contributions to public debates.

Source: https://link.springer.com/journal/10584

Ginkgo – more than just a smelly tree

Team: Ashley Wang, Jingxian Zhang, Sam Resnick, Lawrence Sun

The data say that there is a high number of ginkgo trees in New York. This number has only increased throughout the years, even outpacing the average rate of increase. We want to tell this story to find out why people would continue planting these stinky trees, and what makes them so popular.
We started by looking at the NYC tree census data and were overwhelmed with tens of millions of data points.  However, after scrolling through the data, one particular tree caught our eye.  This was the Ginkgo tree!  Most of us had smelled this particular tree and were disgusted by it and wondered why this tree was so prevalent in NYC.  After looking at the tree census data over two decades, we noticed that the number of trees in NYC were increasing by a certain percentage, but the number of Ginkgo trees increased at a greater rate.  This piqued our curiosity.  After researching the Ginkgo tree, we learned that it is an ancient and hearty tree with a rich history and is extremely popular in Asian cultures for cooking and medicinal use.  With this knowledge, we looked for data on the increase in Asian population in NYC to see if there was a correlation.  By graphing the increase in Ginkgo trees in each city borough and overlaying the increase in Asian population in each borough, we saw a trend: boroughs with a large percent increase in Asian population also saw a large increase in Ginkgo trees.  Although this does not necessarily indicate causation, we did learn that there is a well known “scavenger economy” in NYC amongst Asian communities where people pick and gather Ginkgo fruit.  This increase in demand for the fruit could have lead to the planting of more trees. Moreover, as a hardy tree, its low maintenance cost may also be a reason lead to its increase.

Figure 1 Increase rates for Ginkgo and all trees in NYC
Increase rates for Gingko trees and Asian population in Brooklyn, Queens, and Manhattan

We liked the idea of presenting the data visualizations on a sketchbook, flipping pages to reveal more information. A sketchbook is commonly used to present a study of a specific object, especially one found in nature. If we pursued this project further, we might create a video in which a person is drawing on a sketchbook while the story is being narrated.  
Since our narrative is a playful one, we decided to present our data in an a whimsical way as well. This was the reason behind the bar charts made out of trees, and the timeline chart, which slightly over inflates the importance of the ginkgo tree.  Our story is a story of human interest intended to build appreciation of the Ginkgo tree as well as to describe a possible reason for its popularity in NYC.  Because of this, we decided that the best way to draw our audience in was to tell a story in an informal way that allows our audience to feel like they are discovering the story of the Ginkgo tree’s popularity on their own.

Link to presentation: Ginkgo – more than just a smelly tree 

Sources:
The 1995, 2005, and 2015 NYC Tree Census
The 1990, 2000, and 2010 US Census Data
Why do we keep planting stinky Ginkgos?

Hubway: Connecting College Campuses in the Boston/Cambridge Area

(Download full-size graphic on Dropbox)

By Tricia Shi, Sean Soni, Kimberly Yu, Margaret Yu

The data say that Hubway is often used to get from one college campus to another.  We want to tell this story because we believe that connecting college campuses promotes the exchange of knowledge and culture.  We also want to encourage people to bike, as there are positive environmental effects, and Cambridge and Boston are consistently ranked as top cities to bike in.

We pulled ride information from the public Hubway dataset for hundreds of thousands of Hubway rides in the Boston/Cambridge area from 2011 to 2013.  We then identified stations located on various college campuses and grouped them by campus.  After deciding to focus on 5 of the colleges in the immediate Boston/Cambridge area with on-campus Hubway stations – Harvard, MIT, Emerson, Northeastern, and Boston University – we examined traffic flow patterns between these campuses.

Our infographic contains several graphs, the first of which is a chord diagram. Our primary purpose was to show the relative flow of traffic among all five campuses, and a chord diagram works nicely for this, as it allows the reader to visualize the amount of traffic.  This chart contributes to our primary message by showing that people around MIT use Hubway significantly more than any other campus in the area.  We used MIT school colors to show MIT’s flow, and made the colors of other schools almost grayscale because a common problem with a chord diagram is that the colors are not distributed fairly.

The bottom bike wheel shows that a good portion of people use Hubway outside of school or work hours, which may encourage others to do the same.  We follow this with two simple pictogram charts. The first shows that MIT is the most popular campus as a Hubway destination, and the second provides some ideas about where a potential cyclist may bike to from a college campus. The radial bar graph shows the most popular destinations from MIT, which are possibly of interest to the reader.  Finally, the last graphic shows some routes that have never been taken, and dares the bold to try something new.

Introducing the Hubway Class of 2013!

Team Members: Brandon Levy, Tina Quach, Mikayla Murphy, Lisa Woo

Link to presentation:
https://docs.google.com/presentation/d/1yDLBzar1jGO5Fz6SXmD9QTVfEKjNu1borJMKfu3pJFY/edit#slide=id.p

 

Summary statement: The data show that Hubway bike B00490 (“Queen Bee Bike”) was used more times than any other bike from Hubway’s launch in 2011 through the end of the 2013 regular season, and bike B00552 (“Night Owl Bike”) was used between the hours of 11 pm and 2 am more often than any other bike. We want to tell the stories of these two bikes because each Hubway bike has its own story, formed by a variety of Hubway bike users, and highlighting interesting, data-based facts about certain bikes via high-school-esque superlatives and creative bike designs encourages potential users to contribute to these stories by riding Hubway bikes.

For our sketch, we used data provided by Hubway that included every trip taken by a Hubway bike between July 28, 2011, and July 24, 2013. We first calculated the total number of uses for each bike and designated the bike with the most uses as “Queen Bee Bike” due to its popularity. To represent the story of Queen Bee Bike, we constructed a chart in which the horizontal positions of Queen Bee Bike and the average bike represent the total number of uses for each. We chose this type of chart because we wanted to make a visualization that looked like a race between the two bikes, with Queen Bee Bike way ahead of the average bike.

We also determined the number of uses for each bike between 11 pm and 2 am and named the bike with the most uses in this time span “Night Owl Bike.”  To tell the story of Night Owl Bike, we constructed a graphic in which the amount of moon visible represents the number of trips taken in that late-night time span by Night Owl Bike compared to the average bike. We designed the chart in this way because we thought it was a fun and easily understood way to represent this piece of data, especially since the average number of late-night uses was roughly half the number of late-night uses for Night Owl Bike, so we could easily represent the former with a half-moon and the latter with a full moon.

Ultimately, we envision the end product of our data visualization to be a yearbook for the “Hubway Class of 2013” in which specific bikes are highlighted and given superlatives that relate to some interesting factoid about them, and in which a creative chart is used to compare each highlighted bike to the average bike on the relevant metric. We envision having several additional bikes featured in this “yearbook” in addition to the ones used in this sketch. Some of our ideas include Lothario Bike (highest proportion of female users), Bombshell Bike (highest proportion of male users), Adventure Bike (most unique Hubway stations visited), Marathon Bike (longest single trip over the course of one day), and Hardworking Bike (longest streak of consecutive days used).

 

Hubway data source: http://hubwaydatachallenge.org/

Cut a Tree — Make a Difference

Cut a Tree, Make a Difference

Divya Goel, Meghan Kokoski and Zachary Collins

Link to Flyer: https://www.dropbox.com/s/lw01r50y6vglqre/Cut%20a%20Tree%2C%20Make%20a%20Difference.pdf?dl=0

 

When looking through the data displayed by the World Bank, we saw an undeniable increase in carbon dioxide emissions and a drastic decrease in global forest size. We saw these two developments as very related issues, even if not necessarily correlated. The natural recycling process that plant life does with carbon dioxide is very important for reducing our global footprint. The damage we are doing at both ends of this process is concerning, and so we wanted to make a visual display that would connect these two problems in a different yet still very effective manner.

 

We displayed our data with a very satirical approach. It took the form of a flyer produced by a fictional company whose platform advocates deforestation and increased carbon dioxide emissions for the purposes of eliminating fresh air and increasing climate change. Quite immediately it becomes clear that this advertisement is satirical, however this perspective adds much more weight to the arguments we use our data to make.

 

We first propose the current issue — that trees are one of the largest reducers of carbon dioxide emissions. We highlight to the audience the effect they have on clearing the air and then turn attention toward global deforestation. We highlight the massive reduction in forest area describing it as “a great step toward increasing net emission.” Taking it in from a satirical perspective hits the audience in a much stronger manner as something that clearly shouldn’t be having much success is proving to be quite effective. We then turn it into a call to action, encouraging readers to “take the fight to their own backyards” and chopping down local trees. Highlighting the damage they can do actually highlights the positive impact they can have (i.e. planting and protecting local trees).

 

The major chart that we implement is an area graph displaying increased carbon emissions coupled with a tree infographic that displays the damage done to tree populations. The most flawed chart we critiqued in class was the flipped area graph displaying “Gun Deaths in Florida” created by Christine Chan. It’s deceptiveness and confusion caused it to be a very ineffective way of telling the story the author intended, however, we believe that the major fault of this chart was a lack of context and a confusing background. If those were mended, the interesting features this layout contains could be effectively used to convey a story. We decided to take a page from Nigel Holmes and inject humourous and contextual images that would make the graph’s intentions clear and give the reader the motivation to correctly understand what the chart was displaying.

 

We labeled our upside down area as carbon dioxide and gave it a distinctive coloring. Moreover, we inserted iconography related to forest area reduction to the bottom portion of this graph. Having movement and action occurring in this section makes it very clear that this isn’t what is being plotted in the graph. The gaseous nature of carbon dioxide makes it very clear why it may be situated in this inverted form as every object in our display has some relation to the physical item it represents. The interaction between the trees and carbon dioxide creates a clear metaphor that forests are protecting us from it, and provides meaning to the trends within the graph. Using these tools, we were able to capture the reader’s attention and display our data in a way that highlights and provides immediate meaning to both the problem at hand and the information being showcased in our graph.

 

Our target audience is the general individual who may consistently hear about these problems but has become desensitized to the usual and common arguments. Millennials would resonate well with the satirical nature of the flyer. Choosing to tell the story in this type of context focuses on what has went wrong, providing negative reinforcement rather than just the potential to do good. Because millennials are more likely to change their attitudes and habits moving forward, this visualization will have a more persuasive aura among them.

 

CO2 Emission Data and Forest Area Data: http://data.worldbank.org/topic/climate-change

Tree Carbon Consumption Info: http://www.americanforests.org/explore-forests/forest-facts/

Planet Tree Total: http://news.yale.edu/2015/09/02/seeing-forest-and-trees-all-3-trillion-them

 

Trees: Saving Lives in NYC

See our presentation here!

Sharlene Chiu, Margaret Tian, Kevin Zhang

According to a 1994 study of air pollution removal by trees in urban areas, trees only remove 0.09% of fine particulate matter. This amounts to every tree absorbing about 8 lbs annually, based on the 1995 New York City Tree Census. At first glance, 8 lbs may seem negligible, but we were excited to discover that 7.6 lives are saved each year, thanks to the removal of particulate matter by trees!

Our data sources are listed below:

  • NYC Environment & Health Data Portal, http://a816-dohbesp.nyc.gov/IndicatorPublic/BuildATable.aspx#
  • Urban Tree Effects on Fine Particulate Matter and Human Health, https://www.fs.fed.us/nrs/pubs/jrnl/2014/nrs_2014_nowak_002.pdf
  • Air pollution removal by urban trees and shrubs in the United States, https://www.fs.fed.us/ne/newtown_square/publications/other_publishers/OCR/ne_2006_nowak001.pdf

We decided to represent our data with a stacked bar chart and highlight the different layers of information with the zoom features on Prezi. The stacked bar chart predisposes the audience to expect that the percentage of pollution removal by trees is great enough to be easily spotted on the bars. However, we headed in the opposite direction by zooming into a minuscule portion of the bar. The most striking part of our narrative is that such a small percent reduction (0.09%) can have such a substantial effect (saving $60 million and around 8 lives).

We begin by showing the entire bar, which represents all the PM2.5 produced. Then we emphasize how seemingly insignificant the amount absorbed by trees is by slowly zooming into a tiny piece of the bar.  At this point in the presentation, we expect the audience to feel that trees have a trivial effect, but we then demonstrate that trees are indeed important by zooming out and showing relevant (and big) benefits. This theme of zooming in and out to showcase scale is repeated to the end of our creative chart presentation.

Siyang Jing’s Data Log

8:00_wake up and check Facebook, WeChat, news on the phone and chatting with my friends on line for a while.

8:30_turn on a Reading book App while doing the exercise. 4

9:00_checking the email while having breakfast

10:00_Checking out the book in the library

12:00_Watch the TV show while having lunch at home

14:00_Use the Harvard App to Check the M2 shuttle to go to MIT

15:00_From a discussion group by using google PPT, google Doc and other group chatting softwares.

18: 00_go to the Gym running while listening to music

19:00_Using an food making App to teach me prepare for the food for tomorrow

20:00_Chatting with my team through wechat and sometimes facetime with them

21:00_Doing homework and searching the materials on line

23:00_make a phone call to my mother or friends.

00:00_Check Facebook, WeChat and News online

Most often used: Wechat, google chrome, office 365

Niki Waghani’s Data Log

9am – Added an event on my Google Calendar. Was surprised when I realized someone else’s travel calendar and schedule was showing up on my phone, just because we had been traveling together earlier.

10am – Looked up all sorts of information on movies about the Oscars on Safari. Google stores my last few searches.

11am – Tapping in to the EECS lounge with my card. They probably keep track of how many people come in and out. It’s possible they could even keep track of who.

12pm – Listened to music on Youtube. It now knows what kinds of songs I like and makes suggestions based off of that.

1pm – Facebook: Tracks where I like to online shop and the times of day I’m most active on the Internet

2pm – Allowed Google Maps to use my location.

6pm – Uber: Tracks where I am and where I’m going.

7pm – Uploaded video for UAT to the class website. Have also uploaded other psets to class websites earlier in the day.

9pm – Netflix knows the last show I was watching and kept track of where it stopped. It also still remembers the names and viewing information of a few friends who shared my account nearly two years ago.