It’s a Mysterbee: Methodology

We took bee colony data from 2016 and 2017 from this dataset:

Specifically, the numbers we represented were from the Number of Colonies, Maximum, Lost, Percent Lost, Added, Renovated, and Percent Renovated with Five or More Colonies datasets, and we took the data from October-December 2016 and April-June 2017. We focused on data related to Massachusetts so that individuals would have a more personal connection to the data. We selected the data to exhibit the drop in colony count over a six month period. In particular, we really wanted to emphasize how rapidly colony count can decline over a short amount of time. The data was already very clean for our intended purposes, so we do not have to perform any additional cleaning. We did not have to complete any additional analysis (aggregation, etc.) because the numbers related to the story we wanted to tell are already in the dataset.


Once our data was actually selected and prepared, we deliberated on the best way to present the data. We knew that the story we wanted to tell was that bee colonies are rapidly declining (and can quickly drop going forward). We considered many different strategies for portraying this message including: a weight based participatory data game (where the weight of certain objects corresponds to the value of the data), a geographically oriented participatory data game (where the geographical location of the relevant data is at the core of the story), a physical data participatory game (where the physical properties of objects/entities associated with bees are at the core of the story).


Ultimately, we decided that a physical data participatory game, in which the physical properties of objects/entities associated with bees are utilized to tell the story, was best. We believed that this editorial decision would enhance the personal connection that people make with the data and therefore improve both impact and memory for participants. We also thought that a simple, efficient participatory data game would improve people’s willingness to engage in the game.


From there, we had to decide which physical objects that are associated with bees we could utilize in our participatory data game. Several came to mind: hives, trees, flowers, other plants, honey, etc. We decided that honey was the optimal choice because many people are familiar with honey and immediately associate honey with bees. Other objects, such as flowers, have less direct relation with bees, which might create some ambiguity for participants. Beyond this, honey has several well defined and recognized physical properties that we could exploit for our data game such as: viscosity, distinct color, and enjoyable taste. All of these factors contributed to our decision to utilize honey to tell the story of declining bee populations.


Sticking with the minimalist design that we thought would increase participant engagement, we chose to fill two opaque jars with honey. The amount of honey in each jar directly corresponded to the number of colonies in the respective time period (2016 or 2017), which are not initially visible to the viewer. From there, we wanted to incorporate the physical properties of taste and viscosity into the experience. So, we gave each participant two crackers and instructed them to dip their crackers into each jar. Immediately, participants see a significantly less amount of honey on the cracker dipped in the 2017 jar. From there, the participants have the option of eating the cracker, at which point the sense of taste becomes involved in the data experience. From this minimalist experience, participants are quickly oriented around the topic of bees and educated on the rapidity and immediacy of their decline through a very tangible, real, and memorable medium.

If participants want more information, we provide them with a brochure that gives more thorough context and understanding. In particular, we encourage participants (since they are MIT students) to engage in or support MIT research on the topic. This is explained more thoroughly in the impact blog post.


It’s a Mysterbee: Impact

Bee Colony Collapse Disorder has been known for a while, but there is still a lot of mystery around the causes of this phenomenon. There have been numerous approaches to solving the issue, from robotic bees to redesigning of hives. We believe that a solution can be found only if people from unconventional fields decide to take on the problem and explore new methods to answer unconsidered questions.


We decided to specifically target MIT students for our story, as we believe that the MIT community has little to no background knowledge about bee colony collapse disorder. By informing them, we’re hoping to inspire to take action, whether it’s get involved in bee research, joining bee-related extracurriculars, or starting their own projects. After they interact with the cups, we invited the audience to read a pamphlet that listed our more bee colony collapse disorder facts, and listed current MIT bee research and the MIT beekeeping club.

We designed our surveys such that we measured prior knowledge of issues by asking if they thought bee research was a pressing issue. We then follow-up in post asking if the exhibit changed their minds or no (1 indicating no change, 5 most change). We also ask if MIT should be more active in this area, with a 1 being no change, and 5 an emphatic agreement.


We ran an in-person test of this survey on the 5th floor of the student center, near the beehives that the MIT Beekeeping club has. Being able to have a direct connection with the space we were in was helpful in maintaining participants interest beyond the initial appeal of free food.


We got 5 responses, 4 of which responded that bee research was “maybe” important, and one saying that it is wasn’t. In the post survey, we asked for their opinion change, getting two 5s, one 4, and two 3s as responses (5 being a paradigm shift, 1 being no change). In addition, when asking if MIT should be more active in this area – our call to action – respondents gave one 3, three 4s, and one 5 as an answer.


It is worth noting that all of our survey takers were undergraduate students involved in Computer Science (3A-6, 6-3, and 18C majors) and only one was male. We suspect this is just a fluke of our small sample size. In the future iterations, we hope to increase this impact to graduate students and more departments.


In addition to these data, we also noticed that participants engaged deeply with the exhibit, often leading to a thoughtful discussion of current research and future avenues. Of note, many participants seemed to really enjoy the taste of honey (more so than we were expecting) which reaffirmed our belief that tasty food would be a great way to engage and attract participants. This was even true for participants initially disinterested in the project. However, the impact and interest in the exhibit did seem to depend on the participants’ prior knowledge of the bee decline issue.


Mitchell Myers, Caroline Liu, Arturo Chavez, Alicia Ouyang

Boston’s natural gas pipes are prone to corrosion and leaks due to their age, so we want to raise awareness in our local community. We looked at the natural gas dataset, and decided to focus on “lost” leaks and “found leaks. “Found” leaks are natural gas leaks that have been are recorded by the natural gas utilities through the years as unrepaired. In contrast, “lost leaks” are leaks that were recorded as “unrepaired”, but disappeared in following years from the records without being recorded as “repaired”. We want to tell the story of “lost leaks” because natural gas leaks are often colorless, yet have major consequences by contributing to greenhouse gases, creating fire hazards, and increases financial costs on residents. Our goal in creating our combined game of minesweeper and Boston/Cambridge maps is to teach local residents about the “lost leaks” problem, and lobby for stricter accountability measures on utility companies.

Our audience would encounter Leaksweeper through social media sharing, by the advocacy group or petition signers. We choose to use small maps around specific districts and neighborhoods since people know the areas around where they live or where they work. We encourage interaction through the minesweeper interface overlay, where the mines represent the natural gas leaks. Since we have different types of leaks, flags already mark the “found” leaks when the game begins. The goal of the player is to flag all the “lost” leaks while avoiding clicking on them. A consequence of natural gas leaks are explosions, and we wanted the audience to explore the map, so we believed representing the leaks by exploding mines in a game of minesweeper is an effective way to bring our message across. At the end of the game, we urge the player to take action by visiting petition website, , and share the game.

Trees of New York – One City, Two Life Conditions (Summary)

Group members: Kalli Retzepi, Sofia Reinach, Olivia Brode-Roger, Alicia Ouyang

The datasets we focused on are the tree census and traffic volume of New York City. As the initiative to increase nature in cities pushes forward and population of cities grow, the number and health of trees are growing as the number and duration of cars in New York, and are sometimes are odds with each other, as more residents means the need to find space for buildings.

We decided to tell the story of the tree health and traffic volume of two neighborhoods in Manhattan, Midtown and Upper West Side, because we wanted to compare the growth of two areas that are less than a mile apart, but have very different vibes and values. Midtown is the location of many offices and tourist attractions while the Upper West Side is more of a residential and cultural location. We choose to make a scrolling visualization because we wanted the viewer to focus the numbers and relate to the story of the individual feature. At the end, we created a bar chart so the viewers can see the overall comparison, as well as bring the two neighborhoods back together as part of one city.

The map above would serve as the background of all the components, and provide context. We also intend to include more visualization types with the numbers, such as stacking bars or pie charts, as show in the our handwritten sketch below:

We hope that in the end, the visualization will provide more context of the health of the city and perhaps inspire improvement.

NYC’s 2015 Tree Census:

NYC’s 2012-2013 Traffic Volume:

Alicia’s Data Activity Log: 02/18/18

I wrote this in diary style because I found that the most amusing. I would also post the picture that was taken, but privacy request. Sorry!

10:00 AM I check the weather app in NYC using the wifi in my boyfriend’s apartment. I’m sure somewhere the internet provider can identify my device

10:20 AM I check Google Maps where the restaurant is. I might have freaked out for a solid minute when Google Maps says it’s closed, but then calm down when the website says it’s open. Google Maps obviously has my GPS coordinates.

10:21 AM Facebook Messenger notes that I’m active as I relay to my cousin. He then promptly freaks out because he made the reservation for the wrong day.

10:35 AM The restaurant sends me a text to say I’m confirmed on their waitlist. There’s data from texts, right?

10:40 AM I use the Yelp app, which takes my GPS coordinates, and put myself down on a restaurant’s waitlist. Five minutes later, take myself off.

11:40 AM Finish brunch at Empanada Mama. I use my credit card to pay for my part of brunch. Discover notes that I’m now in NYC, which is not far away enough from Boston to consider fishy.

12:15 PM Leave my phone in the coat room of a museum. There goes 30 unread emails, and maybe a text from my mom wondering why I haven’t been active on Messenger for three hours.

12:20 PM The museum is spy-themed, and gives us wristsbands that signify our admission. The lady checks that I bought our tickets online (which were only $1 for processing fee, free for students!!)

12:40 PM For some reason, when doing the activities that need to be activated by the wristband, the kiosks greet my cousin and I with just a “Hello” while they greet my boyfriend with his name. Hmmm. They keep track of our progress while we go through activities to build our “spy profile”.

12:45 PM We do the surveillance activity, which has a lot of CCTV footage of the museum. I don’t know if they’re recorded?

12:55 PM We get our results. Apparently I’m most suited to be an Intelligence Analyst. The display says they would email me, but I’m not sure it’s working.

3:50 PM I hit my 30 minutes of exercise. Apparently 180 more calories to go and 8 more times to stand. Thanks Apple Watch!

7:00 PM My cousin takes a selfie with us and sends it. Facebook now has a picture of us.

7:30 PM We’re at UCB theater and get our electronic tickets scanned.

9:00 PM Transportation is hard. While the ticket machines don’t work so we can’t get on the bus, I’m reluctant to use ride-sharing apps, though I did open it, so Lyft figured out where I was. End up walking and using the subway again. MTA makes me really thankful for MBTA’s Charlie Card.

10:00 PM Buy tickets for spring break, finally! Also reply to airbnb messages (yay I got the booking!) and link my airline rewards to hotel rewards. Somewhere, data is recorded that I’m traveling and trying to be money savy.

11:00 PM Attempt to watch xfinity and it tells me I’m not on campus. I’m sad.