2016 Data Storytelling Studio @ MIT

Ending Veteran Homelessness

Team Members: Aneesh Agrawal, Reem Alfaiz, Jane Coffrin, and Michelle Thomas

The data say that in 2015 there were more than 47,000 homeless veterans across the United States. And while this number has decreased from the more than 78,000 homeless veterans in 2007, more needs to be done to end homelessness for our veterans. We want to tell this story because we value the service of these veterans have provided for our country and we want to continue to encourage decreasing the number of homeless veterans by asking for support from states and cities to help get these veterans out of shelters and streets by providing the support they need to find permanent housing.

Our audience is the citizens of the United States who care about our veterans and want to make an impact on their local communities. We will focus on reaching this audience through social media. Our goals are:

1. To praise City Mayors for joining the Mayors Challenge to End Veteran Homelessness

2. To encourage Mayors, that haven’t already, to join Mayors Challenge to End Veteran Homelessness

3.To bring awareness to the number of Homeless Veterans within a viewer’s state.

In 2010 the Obama Administration released Opening Doors, the nation’s first strategic and aggressive plan to prevent and ultimately end homelessness. One of goals was to end veteran homelessness by 2015. In an effort to call officials into action, First Lady Michelle Obama issued the mayors challenge to end veteran homelessness which calls on mayors across the country to pledge to take steps towards the 2015 goal. Since 2010 the number of homeless veterans in the United States has decreased every year, but with more than 47,000 homeless veterans in 2015 there is a ways to go before veteran homelessness will end. We believe that more needs to be done to help end Veteran Homelessness. Although over 600 mayors have joined the Mayors Challenge to End Veteran Homelessness, we hope to continue to get more Mayors onboard to help eliminate veteran homelessness.

We used a map to show the number of Homeless Veterans per Capita in each state from 2007 to 2015. We think that this aligns with reality: veteran homelessness has been reduced since 2007, but there is still more work to be done. Our map is color coded by percentage of Homeless Veterans per Capita from the HUD Homelessness Data and Yearly State Population Estimates. It is also interactive and allows the user to scroll through time (from 2007 to 2015) as well investigate their own state via drop down menu to learn more about homeless veterans in their state. From there they are able to see the list of Mayors for the selected state that have already joined the Mayors Challenge to End Veteran Homelessness. If their Mayor is on the list, great! The user will be prompted to send their Mayor a pre-written thank you note for Joining the Challenge. If their Mayor is not on the list, the user will be prompted to send a pre-written letter encouraging their Mayor to pledge to end veteran homelessness.

You can check out the website here.

Our data sources were:

HUD Homelessness Data → 2007-2015 Point-in-Time Estimates by State
2000-2010 Vintage State Population Estimates
2011-2014 Vintage State Population Estimates
2015 Vintage State Population Estimate

Creating More Beds for the Homeless

Team Members: Gary Burnett, Phillip Graham, Katie Marlowe

Finished Map

The data say that states that have a higher ratio of beds for the homeless to the amount of homeless people more frequently had a decrease in the number of homeless people from 2014-2015.

We want to tell this story because the homelessness epidemic is a big problem. There are 564,708 homeless people in the United States, and transitional housing is helping to lower this number.

This data would be presented at a convention about ending the homelessness epidemic, so our audience would be people attending the convention, who are most likely eager to help this issue. Our goal is to tell them that transitional housing can help be part of the solution, so that we can build support for transitional housing.

When looking at the data, we found that states with a decrease in homeless population tended to have more transitional housing. Specifically, they had a higher ratio of beds available to the number of homeless people.

There were, of course, some outliers. South Dakota had an increase in the homeless population by 17%, whereas no other state had an increase more than 10%, and they also have a high ratio of beds for the homeless. In general, the states in the North East also tended to not fit the trend. New York and New Hampshire both have a high ratio of beds, but had an increase in their homeless population.

We decided that a map would be a good representation for this data for a couple of reasons. First of all, this would be displayed at convention where a lot of people would walk by and look at it, so a map is an easy way for someone to locate their home state and see how they stack up to other states. It is also nice to see how different geographic regions compare. As stated earlier, the North East does not exactly fit the trend that most of the rest of the country follows. The South has, for the most part, seen a significant decrease in their homeless population, where the West Coast has seen a decent increase in their homeless population.

An End to Stop and Frisk

Team Members: Catherine Caruso, Mike Drachkovitch, Kendra Pierre-Louis

(Click the photo to read the article)

Stop and Frisk_Final

The data say that the Boston Police Department conducted 152,230 stop and frisk actions from 2007 to 2010. Of those, 89,219, or 61.28%, were conducted on black people. Given that according to 2010 Census data, only 24.4% of Bostonians are black, blacks are 2.5 times more likely to be the subjects of a stop and frisk than their numbers would suggest. We want to tell this story because despite the evidence that black communities are disproportionately targeted by the practice, there has been little traction in reducing it in Boston, or in the many cities where the it occurs. Lower income communities of color like those targeted in Boston lack the political power to end the practice. Consequently, our audience is white Bostonians who have more political power and can act as allies on behalf of those communities. Our goals are 1) get them to empathize with the embarrassment and the disruption of being routinely stopped and frisked without cause and 2) recognize the absurdity of the practice, to a degree that they’re willing to learn more about it and take action

Summary: The Boston Police Department (BPD) engages in the controversial practice known as “stop and frisk,” where officers stop, stop, question, and frisk people for weapons, drugs, and other contraband without probably cause. Many consider the practice a violation of the fourth amendment which protects against unreasonable searches and seizures. In Boston most stop and frisks occur in low income, majority black neighborhoods, which suggests biased policing, and leads to a negative impact on those communities.

“I’m talking about feeling safe,” said Charles Franklin who has experienced stop and frisk repeatedly in a 2015 Marshall Project. Franklin noted, “The police driving up on us, because of some hearsay, and jumping out, that don’t make us feel safe. The police smelling every drink I drink, looking in my bag every time I come out the store, that don’t make me feel safe.”

The problem is that those who directly experience stop and frisk practices are often those with the least political capital to effect change. In a 2015 Demos Report, Heather C. McGhee notes that “…a campaign system dominated by a narrow set of donors who are overwhelmingly (at least 90 percent) white diminishes the importance of communities of color to our elected officials as a whole.”

When drugs were primarily seen as an issue rooted in the ‘deviance’ of inner city communities of color, drug policies were punitive – a 2010 Economist article noted that non-violent drug offenders were punished more harshly than perpetrators of armed rape. But as drug addiction moved to white, middle class communities, there was a movement towards less punitive measures, and a relaxation of drug laws. Perhaps if middle class white communities experienced stop and frisk, citizens in those communities would help to end the invasive practice.

Posing as Black Lives Matter based social justice organization, we created a satire Onion-style article targeted at middle class, white Bostonians. We took the 2007-2010 BPD frisk data, mapped it, and flipped the map based on census income data, so that the most frisked communities were no longer lower-income black ones, but higher income white ones.

We felt that creating an inverted map was a powerful way to challenge how readers implicitly condone stop and frisk practices. By flipping familiar geographic patterns, we hope to upend our readers’ understanding of the issue and subvert their expectations.

Our goal with the article was to use comedy to challenge people’s expectations of acceptable practice while also getting them to consider two key questions:

What if stop to and frisk victims were white and affluent?
Why are we so concerned about one kind of crime (drugs) and not another (mass fraud)?

Stopped.

By: Argyro Nicolaou, Jyotiska Biswas, and Tiffany Wang

The Boston Police Department and the ACLU (American Civil Liberties Union) commissioned a report about Boston “Stop and Frisk” incidents that was released on June 15, 2015. This report contained data about so-called FIO (Field Interrogation and Observation) incidents between 2007 and 2010, and many of the findings point out the disproportionate amount of African Americans that have been stopped on the streets. We used the Boston Police Department FIO data, focusing on the years 2011, 2012, 2013 and 2014, in order to show that despite the decrease in FIO reports between 2008 – 2013, the disproportionate targeting of young male African Americans, especially in certain areas, continues. Our audience is the residents of the Mattapan, Dorchester and Roxbury neighborhoods and anyone that frequents Blue Hill Avenue – a major road connecting all three. Our goal is to embody the experience of being stopped and frisked and also to humanize the people who make up the statistics in the BPD data.

Our campaign involves a site-specific intervention all along Blue Hill Avenue. We decided to focus on Blue Hill Avenue, where 6% of all stop and frisk incidents between 2011 and 2014 happened. Using the maps that illustrate the specific locations of FIO reports (people that have been stopped), we envisage placing big blue dots on the sidewalk, each representing a stop & frisk incident from 2011 – 2014, on the very spot that the FIO incident happened. These dots would feature some basic information for each FIO subject: race, date, age, and gender. On these dots is also a QR code that is scannable and will lead to the campaign website, where the ACLU Report along with other data will be featured.

We created several maps using Tableau to visualize the distribution of stop and frisk incidents amongst certain races; we focused on three main races: African American, Hispanic, and White. We did not include 2015 data since the data for 2015 in the FIO data set only contained a couple of months of stop and frisk data. In the distributions we mapped for the four years, it is pretty clear to see that there a significant amount of African Americans being stopped, even though only 25% of Boston’s population in 2010 were African Americans. Furthermore, the maps show that S&F incidents have not decreased, and that they continue to be distributed in pretty much the same way.

As seen on the maps from each of the four years, there were two streets that clearly had more stop and frisk incidents. This is what motivated our choice of Blue Hill Avenue for this campaign.

Future Improvements include: (1) Normalize/control data results for crime rate, gang membership, previous offenses and other variables (2) Replicating the experience on Dorchester Avenue (3) Think about a further call to action when people get on the website.

See our slideshow here.

Opening Up Stop and Frisk

By: Maddie Kim, Julia Appel, Felipe Lozano-Landinez, and Iris Fung

The data show Stop and Frisk incidents and crimes reported in Boston in 2012 from the ACLU, reported crimes from the City of Boston Open Data Portal from 2012, and demographic data from the American Community Survey from 2007-2011. We created a scrollable op-ed piece piece similar to one you might see in the New York Times Upshot section, or on the Boston Globe’s website. (The title of our newspaper is “The Boston Times.”)

To that end, our intended audience is informed and politically engaged readers of a major Boston newspaper. Our goal is to communicate our findings about the incidence of Stop and Frisk incidents and crime reporting, and to convince readers that timely release of data on Stop and Frisk is imperative for maintaining accountability among the Boston Police Department for fair and just policing practices. With a policing practice as controversial as this one, and as open to potential racially and demographically motivated abuse as this one has proven itself to be in other cities, clear and transparent accountability via open data release is an absolute necessity. Our call to action is to for readers to demand that Stop and Frisk data be open to the public. It is a bit more subtle than in past projects, because we felt like the medium in which we were working (a large, trusted, objective news source) would not run an op-ed piece with a very explicit call to action (like, for example, signing an ACLU petition). Rather, we tried to let the data speak for themselves via the maps and captions, and then guide the reader towards our call to action with the accompanying text. We think this is an appropriate way to tell this story because it combines the visual impact of a map with the context provided by our accompanying analysis and opinion text. This feels especially important with this dataset, because of the sensitivity demanded by policing activities, and the nuance involved with parsing, cleaning, and combining the three datasets. We felt a cohesive news story would be the best way to give the topic the context, ethical integrity, and thoughtfulness it deserves. Further, we felt that formatting our project as an opinion piece allowed us to communicate our goals and call to action to an audience that would likely be receptive to it.

The first part of our project development process was completion of background research on Stop and Frisk; we looked at other cities that have successfully utilized open data on Stop and Frisk to make positive changes to policing practices. Next, we coded all the Stop and Frisk incidents included in the dataset by neighborhood using the BPD District ID code from each report, and mapped each incident. Then we downloaded, cleaned, and coded crime report data from the BPD according to the same scheme as the Stop and Frisk data, and plotted the two data sets onto the same map. The results were surprising: there were some discrepancies in incidences of crime and Stop and Frisk incidents. Stop and Frisk is a policy that is meant to make policing more efficient, so we expected to see correlations between crime reports and Stop and Frisk incidents. From there, we chose two neighborhoods, one with high Stop and Frisk incidents (Mattapan), and one with high crime (West Roxbury). We compared the demographics of each neighborhood — racial composition, median income, unemployment — to see if those metrics had any correlation with the discrepancies we noticed. In our article, we bookend our maps and charts with text that expresses our article’s thesis: open data on Stop and Frisk will make the Boston Police Department more accountable to the City’s citizens, and help enforce policing practices that are not racially or demographically motivated, but rather are motivated by actual crime incidence.

Stop and Frisk is a controversial policing policy: proponents view it is a proactive way to patrol the streets and decrease crime, while objectors see it as racist, and a plain violation of human and civil rights; both a cause and an effect of a corrupt and unjust criminal justice system. Regardless of your opinion on the policing practice, one thing is certain: transparent compilation of data is an absolute necessity to ensuring that the public has accurate information and can hold the Boston Police Department accountable for their actions.

Here’s another link to our article.

Marathon Map

By Judy Chang, Andrew Mikofalvy, Eric Lau, and Kenny Friedman

The Setup

Screen Shot 2016-04-25 at 10.40.23 PM Each year, people from across the country travel to Boston to run in the
marathon. By grouping runners by state, and then averaging the times of the runners per state, it is possible to compare the running ability of each state. For this project, our group has done just that.

Screen Shot 2016-04-25 at 10.40.46 PM We have three goals in mind for this project. First, and most generally, we hope the map increases excitement about the Boston marathon. Second, and more tangibly, we want to increase state pride and state camaraderie. Marathon running is a very individual sport, which can at times feel isolating and lonely. By grouping runners by state, we hope to introduce a local-area support network. We hope runners from a given state will help each other and increase a sense of community. Third, and most concretely, we want to show runners how well their state performs and provide them resources to help them increase their state’s performance. For example, a link might be provided to a local marathon to practice and meet other runners from the same state.

Screen Shot 2016-04-25 at 10.50.48 PM Therefore, our audience is marathon runners who have not yet run the Boston marathon, or running enthusiasts, from all 50 states. Our Call to Action is to improve the user’s state-average by providing resources to help runners improve their time and join local runners. Our call to action leverages viewers’ aforementioned sense of state pride by encouraging them to learn more about and potentially join a local marathon. There, they will hopefully qualify and join other runners from their state in next year’s Boston marathon.

The Map

Our creative map is part of a website, available here. When a user first goes to our site, they are asked to enter their state acronym. Next, the user is presented with a map outlining the Boston marathon. There are nodes that are shown moving down the marathon. Each node represents a state, and the amount of time that state takes to complete is shown as a race between the states. Then, once the animation is complete, information about how well the user’s state did is displayed along with a link to a local marathon in which the user could participate.

Future Additions / Improvements

Of course this is a rough sketch, and there are always improvements to be had with more time. Specifically, we would want to add many more resources for local runners to meet up and help each other train. Then, we would want to augment the map with more qualitative information, such as the elapsed time as you are watching the animation. Lastly, we would like to add a second, US map, in which a user could hover over a given state. Hovering over the state would highlight the corresponding node would highlight, and vise versa. We discussed these ideas and many others, but were time limited.

Sources

We fielded data from a variety of sources, including:

Our map of the Boston Marathon: here
The statistics of the 2016 Boston Marathon: here
Average & Finish Times of the Boston Marathon: here
List of Marathons in a Given State: here

Our site

Tough Choices: The Reality of Refugee Policy

By: Jyotishka Biswas, Kalki Seksaria, Mike Drachkovitch, and Felipe Lozano-Landinez

The data says that there are thousands of refugees entering the European Union every week. The current massive migration of refugees to the EU presents both a moral and resource-constraint issue to the countries receiving the influx. Decisions about how to balance the inherent trade-off are made by political leaders in all of these countries in a real-time, imperfect information environment. We want to tell this story to unpack the static-ness of the numbers and show the human decisions that underly a country’s response to try and best manage this challenge. The country that we decided to go with for this game prototype is Austria.

Our audience for this project is one placed within an educational setting, with the idea being that the participatory data game serves as a simulation of the types of decisions that a political leader would have to make during this crisis and will help students (who are immediately affected in some way by the EU refugee criss and would find themselves in similar tough situations in their future careers) better understand the complexities of the issue at hand for a government decision-maker. As an example group of this abstract decision, we have chosen University of Vienna Political Science Masters Students.

For this project, we imagined that we were the International Organization for Migration, and intergovernmental body focused on addressing migration issues throughout the world. With that lens, our goals for this project were to 1) Help students better understand the underlying complexities of the EU refugee crisis challenge in a more visceral, interactive manner.

Then, if our goal was to be successful, we had two calls to action for the students: 1) Push them towards advocating for better data collection capabilities by the European Union AND/OR 2)Encourage them to help the efforts of the IOM by working with the organization (internships, full-time, etc.).

Our data source for this game was the Refugee Arrivals along the Balkan Route data set from the UNHRC (link here). It presents information on number of refugees arriving every day to multiple countries from October 1st, 2015 through today.

The biggest aspect of the data, our data story, that we wanted to highlight was the inherent uncertainty and incredible difficulty inferencing anything about the future with past data (ex. What will be the number of refugees coming in next week based on what we know now?), and how that results in an incredibly complex decision-making challenge for a political leader.

Our choice of country meant that we segmented the information just for Austria. We focused on using the data from the first seven weeks (so from the first week of October 2015 through the third week of November 2015), and calculated a confidence interval of 95% for the # of incoming refugees for each of the weeks. This was meant to represent the data set that a political leader would be looking at when making a decision about how many refugees to plan for in the future (mean and uncertainty in the numbers over the last week). In the game, we then have a leader make a decision based off of that range (we constrained the decision set to five possible choices), and then matched the leader’s choice to the real # of refugees that came in the next week, which we know from the data set.

The setup of the game was that a decision’s consequences were determined by the difference between a leader’s decision (# of refugees to prepare for for next week) and the actual number of refugees that came in. The consequences manifest in the approval rating of the political leader, which is meant to show the political reality of making decisions that, while morally good, take away resources from your country/constituency. The player has two main objectives for the game: To help as many refugees as possible while also managing their approval rating.

We think this is an appropriate and effective way to tell the data story because it is reflective of a process that has a high amount of uncertainty inherently and that has to deal with the political realities of situations, no matter how well intended the decisions were. Essentially, the result is not fully in your control, and you just have to do your best. This creates both empathy with the political leader’s role and shows the complexity of an issue like the refugee crisis in a way that can only be really seen when being part of the decision-making process. In addition, the game has the intention on focusing on human lives and not equating them to capital explicitly, which humanizes the numbers and respects the lives of the people that the numbers represent.

At the end of the day, players come out with a better understanding of the issue and a more human view of numbers that they may have heard on the news and/or seen on TV due to their immersion into the decision-making process.

You can see our presentation and simulation at this link.

Donate by Playing – A Fundraising Board Game for the International Federation of Red Cross and Red Crescent Societies

Group Members: Argyro Nicolaou, Reem Alfaiz, Phillip Graham, Gary Burnett

The data say that there are many people immigrating to Europe. In 2015 alone, more than 1 million people arrived to Europe by sea. The numbers are increasing every year. In the first 3 months of 2016 the number of sea arrivals was 6 times greater, than the same time in the previous year. This influx of refugees will put a lot of stress on relief organizations, as they will now be even more limited in the number of resources they have available.

The goal of our project is to increase funding for these organizations via a game. Our audience are donors attending a fundraising event for the International Federation of Red Cross and Red Crescent Societies. These people are at the benefit because they want to donate and have some level of investment in the cause. Our goal is to encourage the attendees to donate by eliciting an emotional response via game play. Our game puts them in the position of refugees and shows them how the money they donate to IRFC will directly impact the lives of people trying to immigrate to Germany.

We want to tell this story to highlight the impact that relief aid can have on the life of a refugee. The journey to asylum can be painful and exhausting. Often times it leads to separation from one’s family, and sometimes it can even lead to death. We want to show that this is not how it has to be. There are organizations out there that provide relief and make the lives of refugees more bearable, and donating to organizations such as IRFC can have a direct impact on the lives of real people.

One of the most powerful data sources we used for our project were personal stories found online, that were documented by real life refugees. This helped a lot in the creation of our characters and what sorts of events can occur during an immigration across Europe. These personal stories both contribute to the accuracy of the journey and also help the players sympathize with situation and feel an emotional connection to the player.

Another useful data source was the UNHCR database of the popularity of various routes across Europe. These helped with the design of the gameboard. There are many paths the players could traverse, however we only decided to include those that were actually feasible. To achieve this we removed routes that included borders that were closed. We also chose to only include paths that many people have travelled across according to the dataset, as opposed to less popular options.

The rules of the game are simple. You are a refugee from Syria trying to get to Germany. Along the way you encounter various obstacles but also different kinds of help. Each player will be assigned a character. The characters are: Malika – a 26-year-old nurse from Aleppo, Adnan – a 10-year old boy from Latakia, Youssef – a 30-year-old man from Homs and the Alsouki family – a family of 4 from Damascus

All players start with 10 stamina points. You draw a card at every location, starting from the common starting point that is Syria. The card has 2 kinds of information on it: it tells you where to go next, and it also tells you how each transition affects your stamina points. Some cards give the player the option to purchase stamina points. These real-life donations all go towards the IFRC fundraising effort to help the national Red Cross and Red Crescent societies in Europe to deal with the unprecedented number of refugees and migrants arriving from the Middle East and Africa.

We want the players to empathize with the obstacles and the hardship that migration involves.

We want people to encounter obstacles in the game that will motivate them to donate small amounts of money that can make a big difference.

We did not want to create a competitive game – buying stamina points benefits everyone on the table, so to speak.

Below are Dropbox links to the pdfs of cards we used for the game:
First Half of Cards
Second Half of Cards

Also, the PDF of Game Board

Playing Game Example Donate By Playing Game Board

How Much Do You Actually Know About Commuting In Boston?

By: Catherine Caruso, Judy Chang, Kendra Pierre-Louis, Tiffany Wang

For this assignment we created a Buzzfeed quiz. You can take the quiz here.

The data say that since Hubway’s inception in 2011, ridership has increased. In fact, earlier this month Hubway had its 4-millionth ride. However most of its riders were born between 1981-1986. People in their early to mid-thirties make up 38-percent of Hubway riders. Yet those born after 1991, currently only make up four percent of Hubway ridership. Getting young people to start riding Hubway is important for the system’s longevity because once people habits become entrenched it is difficult to get them to switch. This is why marketers target people during life transitions – marriage, pregnancy, etc – because it’s easier to get them to try something new while they’re already in transition. Similarly, we wanted to target young people as they made the transition from students to adults.

We wanted to tell this story because Hubway provides a vital service that complements mass transit systems, while reducing the carbon emissions associated with driving and improving the health and wellbeing of riders. In addition, Hubway doesn’t just benefit Hubway – it makes the streets safer for cycling generally and by proxy for pedestrians. Writes Emily Badger in a 2014 Washington Post article about the rollout of New York City’s bike sharing program, Citibike.

As more people bike and walk, cycling and pedestrian fatalities actually decline. That’s because the more people bike and walk, the more drivers become attuned to their presence (either on sidewalks or road shoulders), and the more cities are likely to invest in the kind of infrastructure explicitly meant to protect them (all of which further encourages more cyclists and pedestrians).

Our audience is 22-30 year old young professionals who live in the Boston area and need to commute to work. Our goal is to get more car and subway commuters using Hubway. Because it’s notoriously difficult to get riders to change habits, rather than an aggressive hard sell we thought we could get younger people to try Hubway through the game approach of a Buzzfeed quiz. We choose Buzzfeed specifically because according to its ad sheet:

It has 200+ Million monthly uniques visitors 50% are 18-34 years old
BuzzFeed’s in-house experts help the right audience discover a brand’s content across BuzzFeed and on social, specifically allowing us to target people within hubspots footprint through IP filtering
Brands can track content performance in real-time using BuzzFeed’s social dashboard, allowing us to assess performance, adjust content etc on the fly.

Our goals are to increase ridership among our specified market by 10% over YTD performance. For this experiment which has some limitations – namely that we’re not actually Hubway, that we’re using Buzzfeed’s community sharing profile as opposed to its viral advertising and don’t actually have Buzzfeed support, we’d like to have 500 views/clicks over the week in which we launched the quiz with a 50% completion rate. To date we have a total of 108 views.

We’ve also received some support from Hubway itself. Last week Kendra tweeted out the link copying both Buzzfeed and Hubway on the link:

Hubway retweeted it,

it garnered:

Impressions	662
Total engagements	22
Link Clicks	15
Likes	2
Detail expands	2
Profile clicks	2
Retweets	2

To produce this data we used Hubways 2014 bikeshare data. In addition, we used a wide variety of additional data sets for comparison points. For example, to compare the cost of commuting in Boston by Hubway, T, and car, we used the current cost of a full-priced monthly T pass provided by the MBTA. For the car cost data we used 2011 AAA which put the cost for a medium sedan at 57.3 cents a mile, and calculated out the cost of that car commuting the average 7.6 miles per day for 50 weeks a year (assuming 2 weeks’ vacation). The carbon emissions data was based on. Calorie data was based on a bicycle calculator from bicycle.com and USDA calorie data.

We think this was an appropriate way of approaching the issue – the stereotype of the daily commute is one of a painful slog. We, instead, wanted to portray bicycle riding and Hubway use as the opposite of that – fun, whimsical, AND the smart choice. Testers within the target demographic responded that they laughed, and they learned something.

In addition, we felt like it was the right tone both for the audience we were seeking to target and for the data itself. Hubway data, unlike say the refugee data, isn’t particularly serious or fraught. And, on balance the story of Hubway so far is a positive story. People are embracing the system – we just want more people to embrace that system because doing so will make the system bigger and better as well as more enduring. Years ago, Kendra interviewed a woman working with the National Park system and she said that the fact that fewer young people are going to the parks and staying for less time when they visit is a problem. Without a connection to the park system they’re not going to vote for dollars to maintain it, or for politicians on the basis of how they feel about the national parks. The same is true for Hubway – we need young people adopting the system for its long term longevity. This isn’t to say that we don’t love and want to maintain our older riders – and the quiz does nothing to denigrate that audience.

Explore Hubway

Michelle Thomas, Katie Marlowe, and Jane Coffrin

The data say that people are most frequently using Hubway on weekdays and that some stops are more utilized than others, such as South Station and MIT at Mass Ave. We want to tell this story because if more people take advantage of what Hubway has to offer, it can lead to enjoyment, health benefits and environmental benefits . Our audience is existing Hubway users and encouraging new users to join (specifically targeting those who are college age). We use the data about their trips to encourage them to take more trips. Our goals are to encourage Hubway Riders to explore Boston and use Hubway on the weekends as well as for their daily commutes.

Our data comes from http://hubwaydatachallenge.org, and includes every trip taken through 2013. When we looked through the data, we saw that there were many more trips on weekdays than there were on weekends. From that we gathered that there are many people using Hubway for their daily commute to/from work or school, but they aren’t using it as much for other activities such as exploring a new neighborhood. The data also told us that there are many stations that are very heavily used and had 15,000 or more trips ending at them, whereas some stations were used very little and had less than 1,000 trips ending at them. From this we decided on a goal of encouraging Hubway riders to utilize Hubway for more exploration of Boston, which comes from incentivizing them to use Hubway for reasons outside of their daily commute.

We decided to create a concept for a mobile phone application that users would get for free with their purchase of a Hubway membership. The application, Explore Hubway, links to users’ accounts, and tracks the information about their rides. The features of the application include:

Social Networking: The application includes a social networking aspect, allowing users to see what their friends are doing. This serves as a form of extrinsic motivation, incentivizing users to bike more so they can show off to their network.

Earn Badges: Explore Hubway also incentivizes users by earning badges, which each are worth a certain amount of points. This is also a form of extrinsic motivation to get out and bike more.

Profile: Each user has a profile page, where they can view stats about themselves, a form of intrinsic motivation.

Rewards: Users can cash in the points they earn in for real rewards, which for many users, would probably be the biggest piece of motivation. These rewards come from the Hubway Bicycle Benefits program that is already in place. The rewards program also helps the businesses by getting bikers to visit their stores.

Leadership Board: Users can see how they stack up against other Boston riders.

Map: The map feature allows users to easily see where they can find a Hubway station nearby. Users can also search for stations by both station number and station name.

Because the app is linked to the user’s Hubway membership, it would be able to keep track of when you check bikes in and out of stations. This means it will keep track of badges for the user and knows when a new station used. It also means that at the end of the ride the app will send the user a notification on their phone and show up with one of several possibilities such as: the number of people who used the end station in the past month, the number of minutes the trip took, the number of calories burned, how the length of your trip compares to the average trip length. These notifications will serve as immediate feedback and another intrinsic motivation to continue to use Hubway.

Check out the other concept pictures here!