We chose to do a sketch using the dataset that deals with Fuel Economy, supplemented by 2018 starting price from Kelly Blue Book (www.kbb.com), and propose a sketch with the following characteristics:
Goal: to encourage people to buy more fuel efficient cars.
Audience: current car buyers who are not experts about different features and parts in cars (and have some 1990s video game nostalgia). We envision this being an ad or feature on a car-buying website, but not one that targets gear heads (i.e., Kelly Blue Book or Carfax, not Car & Driver).
Story: In addition to the type of car you select, the choices that you make about the internal features of your car also are essential to the fuel efficiency.
To define the story, we had to choose how to best market full efficiency to our audience. We identified three options of main arguments to convince our audience: 1) “buy it because it’s eco-friendly”; 2) “buy it because being fuel efficient, it’s also friendly to your budget”; 3) “buy it because being fuel efficient, it’s also convenient to your schedule by saving you frequent trips to the gas station.” We decided to explicitly focus on the second and the third arguments in our sketch to make the abstract concept of “efficiency” directly applicable and more personal to individual buyers. At the same time, we use ‘green’ as a double entendre to also hint at making choices that have better environmental impact.
In our sketch, we decided to present our narrative using the visual language of character selection screens from various video games to playfully present our argument and rules for a exploratory participatory data game. We use side-scrolling 8-bit text to explain the surprising finding that choices like drivetrain and transmission type can have an effect on average combined MPG. This introduction is presented in this sketch as slides, but would be ultimately animated.
After walking through this story and instructions on how to play the game, we invite users to select the car type and features they have been considering to see how that configuration stacks up against similar ones in terms of median combined MPG (which we’ve mocked-up here: http://web.mit.edu/rdshah/www/car/). Once users submit their choice, a pop-up would inform them how their configuration compares to other similar configurations. If it’s not the most fuel efficient configuration within its car type, the game challenges users to try again to find the best configuration (taking some witty inspiration from the BBC Youtube Chemistry game, The Biggest Bang:) .
We also provide price and fuel efficiency information for the two to three most fuel efficient models in the 2018 range of cars with that configuration (to push even stubborn buyers to consider more fuel efficient options).
The features and criteria that we were able to include in the app were constrained by what was available in the dataset (Model, Vehicle Class, Fuel Efficiency, 2WD or 4WD, Fuel Type and Transmission). In future iterations of this sketch, we would try to find and incorporate more features that are relevant to average car buyers (such as number of seats, storage space, sunroof, horsepower, etc.) to make the tool more realistic and helpful in the car buying experience.