#GoGreenBoston2030 – Margaret Yu, Tina Quach, Divya Goel

The data says that there are people from many different parts of Boston that have all questions about how Boston’s transportation system can be more sustainable and environmentally friendly by 2030.  We want to tell this story because we believe that highlighting the common theme across the various parts of Boston can foster empathy and a sense of community–one that can push for environmentally-friendly change in Boston’s transportation system.

Our audience are the adult commuters of the Boston community–whether they use private or public transportation, whether or not they walk or bike or drive or take the train–they each lead different lives that often does not leave room for empathy. Our goal is to encourage empathy between diverse adults of the Boston community and foster thought about how Boston make its transportation system and infrastructure environmentally friendly and sustainable and demonstrate. We want to people to be able to empathize with others who do not share the same neighborhood that they do and be able to identify differences and similarities about the questions people ask.

Our data came from Go Boston 2030’s Question Campaign, which asks people in 20 different Boston zip codes to share their questions about getting around Boston in the future. The data, consists of the textual representation of the questions organized by region or zip code, and labeled with a relevant category (e.g. sustainability/climate change, experiential quality, safety, access, innovation). We filtered the questions to keep only questions related to sustainability and the environment to focus on the specific theme of climate change.

We’ve designed and sketched an installation that allows people to hear others in the Boston community voice their questions–rather than just reading them–and localize where each question comes from. We envision that each Boston county will have a copy of the installation, located near a transit station. The installation will consist of a big touch-enabled screen depicting a map of Boston. The experience begins with a short video of questions from around Boston being voiced, while its origin is highlighted on the map. Then, the installation becomes interactive–people can tap areas they are interested in hearing from and listen to questions from these areas. Furthermore, they can also choose to contribute, sharing their own questions about the future of getting around sustainably in Boston by hitting a button to record their question.

We also wanted the observer to be able to understand the context behind some of the questions, and to continue thinking about it after leaving the booth. So, in the interactive portion at the end, after a user hears a question from an area, they will be presented with a QR code linking to an article or video about someone’s commute that led them to ask one of the questions just played. The observer can then scan this and experience the story on their journey/commute from the booth to their next destination. For example, the QR code on the right would give the backstory to this question from Dorchester: How do we make public transportation inviting so that people prefer taking it than driving their cars? The QR code on the left would give the backstory to this question from East Boston: Can we give more space to pedestrians and cyclists to make the choice to walk/bike for short trips the best option?

This installation is an appropriate and effective way to tell the data story because it is an open, flexible way for someone on the go (or someone on a leisurely walk) to hear other perspectives from around the Boston community. It is also appealing to people trying to go places (our audience!) because it uses a map. Hearing each question voiced by a real human, promotes the idea of really listening to one another as well as letting your voice be heard. Empathy is fostered because each question reflects a particular perspective and twist on the topic of sustainability.



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

What Would It Take To Turn Blue/Red States Red/Blue?

Over the course of the last two years, I have become much more interested in politics, particularly the election strategy process. For example, there are so many small ways candidates/parties can influence outcomes by targeting their messaging by demographic, or working to increase/decrease voter turnout in certain regions. In learning more about political issues and this process, I’ve turned to fivethirtyeight, as they often have amazing interactive graphics and aggregate data from many different resources in their models, but target an audience with some education about data analysis.

This interactive visualization allows users to adjust voter turnout percentages and political leanings by demographic, and shows the resultant electoral college map, based off of 2012 election data scaled for population changes. When I came across this graphic a few days ago, I thought it was the perfect tool to test my assumptions about certain demographics. I did this with my thoughts on my home state of Michigan, which usually goes blue, but went red in this election. I decreased black voter turnout a bit, and increased the republican leaning and voter turnout for non-college-educated whites, the two factors which are being most attributed to Trump’s winning of Michigan’s electoral votes. This was just enough to flip Michigan to red, and flipped Pennsylvania and Wisconsin as well, both key states that went red in this election.

This interactive is probably most targeted towards people with a good grasp of the electoral college and specific hypotheses they want to test regarding this past election. It does provide a clean interface for learning about the demographics, both in this aggregate method and on a state-by-state basis further down the page. However, I think it would be more effective with some more demographic factors such as age range. The interactive was also published in October before this election outcome, so having a revised section post-election with the updated data on these percentages of voter turnout would create more relevance and make it more accessible to a wider audience.