Build it Better

We chose to do a sketch using the dataset that deals with Fuel Economy, supplemented by 2018 starting price from Kelly Blue Book (,  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: 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.

Jay’s Sunday, in data

Direct interaction with phone/computer

  • Called my father to wish him a happy birthday
  • Web history of Google/Youtube searches, browsing the news, meandering through social media feed(page requests)
  • Analyzed data and aggregated results for research tables
  • Received emails and responded to one about an event
  • Saved the event into my calendar application
  • Wrote texts to friends to decide stay in touch across time zones, make plans to meet, and talk about how good Black Panther is
  • Paid for goods (Black Panther tickets via Venmo) and services (Lyft ride) using my phone
  • Point-to-point travel data recorded while taking Lyft that I requested
  • Pushed completed problem set to Github
  • Watched a couple episodes of tv on Hulu

Data collected through someone else’s device

  • Single-point travel data recorded when I got on the bus
  • Scanned paper ticket into women’s hockey game
  • Watched the Olympics on TV at a friend’s party
  • Purchased food at Clover using my debit card
  • Utilization of heat, water, gas captured in my apartment

Data created in the real world

  • Wrote out a to-do list for the rest of the weekend
  • Left footprints in the snow
  • Conversed and told jokes at the party; left a first impression on a few new friends and likely reinforced what old friends already know about me

The Strength of Opiods

As opioid abuse has risen across the United States in the past few years, the epidemic has been covered and humanized in a number of ways, from deep personal narratives to more character-driven reviews of innovative solutions.  One data visualization from the Washington Post from October 2017 takes a different approach for its readers, by comparing different opioids that are currently being abused with one that is more commonly-prescribed: morphine.

The visualizations equate each box with a single dose of morphine.

One-by-one, the article ties information on the uses, historical prescription patterns, and role in the current wave of abuse for drugs such as oxycodone, methadone, heroin, and fentanyl with simple graphics to compare their strength to that of morphine. In doing so, the article attempts to visually reinforce the gravity of each drug’s potency, both on drug users and the state of the crisis.

This goal is most clearly illustrated in the final drug profiled by the article, carfentanil. The article breaks its established physical structure of visual-followed-by-text, and embeds two striking stories of carfentanil’s use as an elephant tranquilizer and bio-weapon over the visualizations 10,000 pink squares (to represent that it is 10,000 times stronger than morphine).

This graphic takes up more than twice the space of the rest of the article to effectively convey carfentanil’s extreme and dangerous effects.

While the visualizations effectively conveys its point using striking colors and a simple symbology, it misses its mark by assuming its readers have some understanding (and perhaps experience) with the strength of morphine. In fact, at no point in the article is ‘strength’ precisely defined, although it is mentioned that this unit of comparison is borrowed from medicine and law enforcement. However, for those without a clear baseline of the pain-mitigating and euphoric effects of morphine, it is difficult to fully understand the scale of comparison.

Source: “See how deadly street opioids like ‘elephant tranquilizer’ have become,” by Dan Keating and Samuel Granados, Washington Post, October 25, 2017.