2017 Data Storytelling Studio @ MIT

Steps to a Better Environment

Tricia Shi, Nina Lutz, Sharlene Chiu, and Zachary Collins

The data says that our collective mindset towards the environment will have a serious impact on how global carbon emissions will trend over the next few decades. If we take care of the environment, they can level off somewhere very near our current rate. If we don’t, they are expected to increase quite rapidly. We wanted to tell this story because the effects of our actions today, although not necessarily making an immediately noticeable impact, could have drastic consequences on the state of our environment just a few decades into the future. This will not only determine the world future generations live in, but even the majority of us today.

Our data visualization project is a staircase that will help people understand the impact our everyday actions can have on our carbon footprints. The shape of the staircase maps our past global carbon emissions as well as two projections for carbon emissions over the next few decades. One projection assumes we live in a world in which, “people pursue personal wealth rather than environmental quality” while the other is a world in which we place a, “strong emphasis on community initiative and social innovation to find … solutions.” Essentially, what happens if environment protection becomes a societal passion versus something we just brush under the rug. The differences are drastic.

To display this data, we wanted to create a visualization that could be interactive with the viewer while still being able to reach our audience passively. We decided that a staircase offers many metaphorical and engaging traits that would help us hammer our major points home. From a passive perspective, the shape of a staircase could easily be molded to align with the line graph representation of our data. Observing it from the side allows the viewer to make out the trends in CO2 emissions and presents it in a form that makes comparing the two models easy. The ability to view the data from various angles allows the different projections to be highlighted.

Climbing the stairs presents a very clear opportunity to get the audience engaged and quite literally feel a comparison between the two. Along the way, we present the climber with numerous facts about both past, current, and future carbon emissions. The first half of the staircase, which follows CO2 transmissions over the past few decades, presents facts related to what actions have led to the current levels and incident climb that we are making. As soon as they reach 2020, the staircase splits into two halves, one for each projection. The audience can feel the different steepness of the two set of steps and this is representative of the rising CO2 levels associated with them.

The two sides are meant to act as a parallel and present facts that demonstrate what actions we take as a society that impact environmental conditions. The half that follows the path toward high levels of CO2 showcases facts like “3 billion trees are cut down annually” and “13% of U.S. greenhouse gas emissions come from the production and transportation of food”. Things that we don’t think about impacting our global ecosystem so heavily but might observe everyday. The second half that follows the path to a better environment presents paralleled ways we can counter the other half. Facts like “planting an individual tree can help consume up to 48 lbs of carbon dioxide from the air per year” and “Eating locally grown foods avoids the high carbon output created by transporting long distances”

The ideal setting for our data visualization would be a museum. Having this staircase lead to two paralleled exhibits could further compound the comparison these two models attempt to make. The staircase that follows the projections of high CO2 levels could lead to a display on what the rising pollution levels could change (i.e. food quality, climate, and disease). The other could lead to a display of things that reducing pollution and CO2 levels would instead protect. We imagine setting this up in a museum would have a powerful effect on the people there. Going to a museum fosters a sense to explore and engage with what’s around you. Having that mindset will really maximize the potential for people to understand the information the staircase offers, both passively and interactively. Our target audience consists of children, young adults, and middle-aged adults, many of which we would find in this setting. Since museum trips are often done with families, the onus to prepare for future generations — their children’s generations — are more heightened and will allow these paralleled projections to be more profoundly felt. Placing positive connotations around acts that are good for the environment could help reach children who climb and interact with the stairs.

Sources:

Policy Tower

Team Members: Almaha Almalki, Lisa Woo, Jingxian Zhang, and Siyang(Autumn) Jing

We set up the scenario in a Mayor’s Summit, World Cities Summit or G20. The mayors of metropolitan areas all over the world will discuss the future solutions for the environmental problems, which including the air pollution as one of the most important topic. As the air pollution

Phenomena of Beijing draw the wide attention, the mayor of Beijing will show other policy makers how the policy will help control the air pollution problem.

Based on the Beijing pollution data from 2008 to 2016, we found the quality of the air had huge relationship with the holding of the big events such as Beijing Olympic Games, National Day March, APEC, etc. Behind the blue sky, there is strong policies to affect the air quality.

So we choose the year of 2014 and 2016 to see how different combination of policies could affect air pollution. The policies are categorized to long term policies and short term policies. Long term policies are renovation of heating systems, vehicle traffic restriction based on plate number, license-registration lottery, vehicle traffic restriction based on exhaust etc. Short term policies are vehicle traffic restriction based on plate number, close some factories in Beijing and its neighbor provinces temporarily , stop using X% of buses, and shut down construction sites temporarily etc.

The demo of the Policy Tower: In the tower, each cup represents an environmental policy. Different amount of blue colors, which represent strength of different policies, will be put in the cups. Yellow water (air pollution) will be poured from the top and go through all the cups. It will gradually turn green while drilling down — the more (and more strong the) policies, the more green the water will become. This is how policy tower works.

But we still need ask more question and do more work to complete this visualization. For example, we need more data for how much each policy contributes to the improvement of air pollution. Combining the cost of different policies to measure the feasibility of each of them, etc.

Link to Slides:

https://docs.google.com/presentation/d/1pzHCXqzz4h_wx1QeLg_dp0P0JZch3ghv_V3_xBeIY_Q/edit?usp=sharing

Grey Skies Black Clouds (Hui Tian Hei Yun)

Team names: Brandon Levy, Margaret Yu, Lawrence Sun, Ashley Yang

Summary sentence: The data say that the average concentration of small particulate matter (PM2.5) in Beijing’s air in January 2017 was 121.96 µg/m³, much higher than both the World Health Organization’s safe standard of 35 µg/m³ and the more lenient Chinese safe standard of 75 µg/m³. We want to tell this story because the air quality in Chinese cities is not even close to meeting the lax standards of the Chinese government, let alone the stricter WHO standard. Our target audience is Chinese politicians attending environmental conferences who have the power to reduce air pollution in their nation’s cities.

Our data come from the U.S. State Department’s “Mission China” air quality monitoring program. Specifically, we examined data from the years 2017 and 2016 that show the concentration of small particulate matter (PM2.5) air pollution each hour of each day in those years in Beijing. We ultimately settled on showing the monthly average across January 2017 (121.96 µg/m³) and decided to compare it to the standards established by the Chinese government (75 µg/m³) and the WHO (35 µg/m³), which we found in the WHO’s Air Quality Guidelines for Particular Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide: Global Update 2005.

In our prototype, we are physically representing the data as balls of newspaper suspended by string from a canopy. The idea for the final piece would be to set up a series of these canopies leading up to the doors of the building where the Chinese politicians are gathering for their environment-related conference. The politicians would have to walk underneath these canopies – and through these “blown up” PM2.5 particulates – in order to get into the conference. In front of each canopy it would say what that canopy represents (WHO standard, Chinese standard, January 2017 average, etc.). In our mock-up, each newspaper ball represents 4 µg/m³ of PM2.5. In a scaled up version, each canopy would be one meter by one meter and each string would hang down one meter from the canopy, thereby representing one cubic meter of space, and each hanging ball would represent 1 µg/m³ of PM2.5.

This is an effective way to tell the story of our data because it turns PM2.5 pollution into something that is not only easy to see but also something that can be physically felt. We hope that walking through these hanging bundles of newspaper balls will be a sobering experience for the politicians, making them think about how problematic air pollution is and how PM2.5 pollution in Bejing far exceeds even the Chinese government’s lax standard. Hopefully, this will encourage them to pass legislation to curb air pollution and/or enforce existing laws to bring pollution levels down to their government’s safe limit.

One Million Roots

Untitled drawing (1)

The data say planting trees has a measurable impact on a city, in human health and economic benefit. We want to tell this story because planting trees relies on people going outside and volunteering, and the first step to increasing public participation is education. I pulled most of my data from NYC’s MillionTreesNYC initiative, using the 1995 and 2015 tree datasets. I compared the population of trees, dividing the data by which streets the trees were located on. I chose streets in particular because they would be immediately recognizable to locals, and also because of the pervasiveness that New York’s geography has in popular culture. Originally, I had hoped to create more intricate graphs, detailing the exact tree makeups of each street. Unfortunately, the datasets were very different in terms of structure—CSV keys were different, data formats were different—and ultimately I was only able to compare raw numbers of trees.
For this assignment, I wanted to focus on putting together a flyer of sorts for the public. My goal was to quickly educate a reader about the MillionTreesNYC initiative, informing them of the success of the effort and telling them of the benefits. I wanted to help them feel a personal connection, so I included the facts and figures I thought were most compelling. Finally, I included a call to action at the bottom. The entire goal of the story was to get the reader to step up and take action, so I included a link to the MillionTrees webpage where they could find different ways to help.
Since I don’t know anything about graphic design, I would want to work with a graphic designer who could make my argument even more compelling. Right now, I feel that the graphic has too much whitespace, and by improving the graphic design, I could make my story even more compelling.

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/