Sketch 3: Rent the Raceway

Project Title: Rent the Raceway

Team Member Names: Haley Meisenholder, Olivia Brode-Roger, Alicia Ouyang, Mitchel Myers

Description: The data says that there are vehicles which are much more fuel efficient than others. These more efficient vehicles produce less harmful emissions and result in monetary savings for users. We want to tell this story because it is relevant to the environment, quality of life, and spending capacity of vehicle renters. Our audience is car renters. Specifically, we hope to place our participatory data game at car rental kiosks (such as at the airport). We hope that through the game, car renters will become more cognizant of the trade-offs between their different vehicle rental options. Ultimately, we hope to use subtle/specific features in the game play to nudge renters into renting/utilizing more fuel efficient vehicles.

In this project, we utilized data from the “US Fuel Economy Measurements” data set, Hertz rental car data, and US map data. We believe that our game is effective for increasing renter awareness and encouraging renters to drive more fuel efficient vehicles.

In our participatory data game “Rent the Raceway”, car renters approach a car rental kiosk and find a screen (iPad, TV, etc.) with the game ready to play. The game is a car racing game with some added fuel efficiency game play components. Car renters are able to select a vehicle among the rental options to race with. In the vehicle selection screen, car renters are able to see the stats of the various vehicles (speed, mpg, etc.) and also the various “power-ups” that their vehicle will be eligible for in game. Once the renter selects a vehicle, the race begins and the renter will race their selected vehicle against other potential rental vehicles that the renter did not select. Some key game play features are: 1) the renter’s vehicle has a dynamic “fuel” gauge that is drained overtime depending on the MPG of the vehicle; if the renter runs out of fuel, the vehicle stalls out for several seconds; to prevent running out of fuel, the renter must drive over “fuel-up” icons on the racetrack, 2) each vehicle has “power-ups” that they are eligible for (e.g. a “cool” convertible can pick up  speed boosts and handling boosts, but not all cars can pick-up every power-up), 3) there is a running tally of money saved in the UI that shows the driver the money saved/lost due to fuel efficiency differences, 4) following race conclusion, the comparative race stats between the various vehicles are displayed and the renter has the ability to select/rent a vehicle.

We believe these mechanics are effective for communicating our message because 1) the user is constantly reminded throughout the game (due to power-ups and fuel gauge) the impact of their car decision on their journey/money/fuel efficiency, 2) the game play itself places fuel efficiency at the forefront by creating a more forgiving/easier game experience for fuel efficient vehicles, 3) placing monetary savings as a core piece of information for fuel efficient vehicles, 4) allowing the user to get detailed/relative statistics regarding trip performance at the conclusion of the race, and 5) providing a bridge to a car rental decision at the conclusion of the game.

Sketch 1: In Manhattan

Title: In Manhattan


Team Member Names: Haley Meisenholder, Rikhav Shah, Mitchel Myers

Summary: The data says that trees take up a small fraction of land area in Manhattan. We want to tell this story because trees should have an equitable share of space. Further, the addition of more trees to Manhattan could greatly enhance its livability.

For our data, we compare the land area of trees in Manhattan to several other groups: people, cars, parkland, and all of Manhattan. For information on trees, we utilized the NYC tree data set from 2015 available on the City of New York website. This data set contains the chest height diameter of every tree and the borough which allowed us to calculate the land area of all trees in Manhattan. For information on people, we used census data to determine the population of Manhattan. From there, the land area of people was calculated using the assumption that each person takes up roughly 2 square feet. For information on cars, we used data from the New York City Economic Development corporation to determine the approximate number of cars and taxis in Manhattan. From there, we used the dimensions of a standard four-door sedan to estimate the land area of a single car. With this information, we calculated the total land area of cars and taxis in Manhattan. For information on parkland in Manhattan, we used data from the New York City Department of Parks and Recreation which provided a precise measure of park land area in Manhattan. For information on the total land area of Manhattan, we used data from the City of New York website.

We believe that our presentation is effective at communicating the un-equitable share of land area that trees possess in Manhattan. This is because the visual presentation: 1) is simple and elegant, 2) is effective for quick comparisons, 3) utilizes recognizable images that allow for rapid recognition of key parties and data sets, 4) utilizes scale to portray the magnitude of the discrepancy between the land area of trees and other groups, 5) is consistent with the symbolism of “tree rings”, 6) uses a slideshow to progress through the data visually. Beyond this, the text and narrative component of the presentation orients the viewer around the specific location in question (Manhattan) and provides the viewer with specific quantitative values for comparisons being made in the visual.

Data Log 02/21/2018 – Mitchel Myers

Data Produced by Me on 02/21/2018

  • Transaction data – Dunkin’ Donuts, Chipotle, Verdes
  • Location data – phone tracking, Uber transactions
  • Photo data – Snapchat
  • Video data – street video cameras
  • Browsing data – internet usage, searches
  • Messaging data – text, emails
  • Phone call data – calls, timestamp, location, people involved
  • Future plans data – airline reservation, train reservation
  • Food preference data – restaurant orders
  • Sport team preference data – based on internet searches
  • Shopping preferences/interests data – Amazon searches and purchases
  • Spending patterns data
  • Swipe-in/swipe-out data (with MIT ID card)

Infographic on Infographics



What Data is Being Shown: This graphical presentation contains a large amount of information regarding the characteristics of infographics. The data set is a randomly selected collection of 49 infographics. The graphical presentation contains key information specific to this data set such as: chart styles in the infographics, fonts, colors, and theme.

Who I Think the Audience Is: The audience is people who are interested in and analyze a lot of infographics. Graphical designers and companies that produce infographics professionally are likely very interested in this sort of information.

The Goals of the Presentation: The goal of this presentation is to educate the reader/viewer on the common characteristics or themes in the infographic realm. I believe that another goal associated with this presentation is to help graphical designers understand how to design unique and differentiated infographics relative to industry norms and standards. This presentation also helps the reader/viewer to understand which countries, regions, and professions utilize infographics as a communication tool.

Whether It is Effective or Not and Why: I believe that this is a somewhat effective infographic because it is largely just presenting information, which it successfully does. However, there is not much of a “story” to this infographic or the establishment of a “problem”. This can easily result in a disinterested reader/viewer. Beyond this, since there is no established “problem”, there is not really a “solution” presented in the data presentation. Therefore, the reader/viewer is left wondering: “what is the point of this” at times.

Mitchel Myers