How Much Food Is In Our Trash


The data say that the US wastes a LOT of food. We want to tell this story because most of people do not aware the food waste problem. Through a playful data storytelling, we aim to help older elementary school students to better understand the problem of food waste and value of food saving using an interactive classroom activity.

DATA SOURCES: food wasted from EPA. food/person/yr from EPA. Food weights from quick google.

How does the game work

Students in the interactive classroom settings will be asked to volunteer. First, a student need to drag an apple from the trash to the compost to get started. The design concept of this action was to engage student with a mindset of food composting. After “composting” the apple, students will see a big number of “132.9 Billion” pounds of food wasted each year in the US, which counts 31% of total US produced food each year. A pie chart which demonstrates the percentage will give students a more intuitive understanding of this food waste problem.

Our goal of this data storytelling is more than just publishing numbers. Students can click “what can I do to help” to further explore how their actions of food saving can contribute to reducing food waste. Furthermore, we designed a “what is your favourite food” session and translated the abstract number concept to food cartoons. Students can select their favourite food and check the amount of their favourite food, equivalently, would be wasted if people do not take actions in food saving.

Design Ethos

Before starting the narratives design, we as a group spent time discussing how to better engage students of young ages. Specifically, we wanted to lower the barrier of conceptual understanding of data for children, and design activities that allow more students to engage in an interactive classroom setting. To be able to achieve the goal, we used analog of different children-loved foods, big pie charts, and cartoons to intrigue the interests and curiosity of students.

What can be done more?

Due to time constraints, we were not able to iterate our design. Many of details would be better attended of if there were more time. For example, the contrasts of years and days may lead to confusions for students if they do not pay attention to read line by line. We would also like to iterate the interaction design between each actions to make the storytelling more cohesive and compelling.


Julie Ganeshan, Sarah Von Ahn, Berlynn Bai

Data Footprint 02/23-24

I recorded my digital data footprint from Saturday, Feb 23rd Sunday Feb 24th, a weekend. During this weekend, I engaged with multiple activities, and logged my data through:

Time: Saturday Morning

Activity: Working from home

  • Spotify: listened to music
  • Gmail: sent emails

Time: Saturday Noon

Activity:  Online shopping

  • Credit card: online payment

Time: Saturday Night

Activity: Dine out & grocery shopping

  • Uber: travel record & payment
  • Credit card: dinner bill
  • Credit card: grocery bill

Time: Sunday Morning

Activity: video chat with friend

  • WeChat: social network communication record



Can Statistics Learning be Intuitive?

Can statistics learning be intuitive? How can learning experience be more creative? Here is an interactive data storytelling example that I found both accurate and engaging: Seeing Theory.

Seeing Theory is an interactive data visualization that makes statistics more accessible. It was built using D3.js, by a group of students from Brown University. By decomposing classic statistics theories to step-by-step playful interactions, Seeing Theory visualizes different statistical events to allow users to better comprehend the seemingly complicated logic behind statistics theories.

Seeing Theory has a wide range of audiences – from high school and college students who just entered the field of statistics, to professionals who want to have a brief understanding of statistics to help with decision-making but nothing too deep. It does a great job in combining both playfulness and learning objective, and tackles the very pain-point of traditional statistics learning experience – where the theories are counterintuitive and difficult to comprehend.

The methods that this visualization applies are groundbreaking and worth-scaling. Thinking about the common methods that embedded within our day-to-day learnings and teachings, are there opportunities can be further explored in order to make the process more engaging and enjoyable? How human-factors can better inform the design of data storytelling? Seeing Theory offers us an alternative of how future learning might look like.