Food through the Interface is part of a year-long research and design exploration of food systems, across scales ranging from the molecular to macroeconomic. Based on an understanding of food as oriented around the consumer interface, the project aims to challenge expectations about distribution and consumption — reshaping both eating habits and production models.

As a team, we first set out to explore the food system. Our research explored industrial agriculture, food processing and production as well as local farms, markets and restaurants. We spoke to farmers, business owners, government officials and everyday consumers.

Beginning with a basic assumption about food as a closed system of production and consumption, we rapidly found that it get a lot more complicated — the food system contains many elements and diverging paths.

And while we uncovered a very complicated system with many problems, we kept returning to the most basic food experience for most people in the US: the time right before food purchase, typically in front of a grocery store shelf.

Faced with staggering amounts of choice, how do consumers decide what to eat? Our research revealed that many people struggle to eat cheaply and healthily, particularly those with limited means or facing decision fatigue after long work days. Visits to stores like Dorchester’s Daily Table revealed the even in the best contexts, shopping well is hard.

Mapping the system in and around the moment of decision helped us to understand the many interactions involved. Even places like Boston public schools became key points for promoting food availability, through their school lunch and weekend take-home programs.

Finding the right place and way to intervene

Deciding that we wanted to work at the interface of food, we sketched a broad range of ideas. These ranged from healthy food vending machines, smarter refrigerators, augmented grocery store experiences, or even new value-added tax programs that would return benefits to consumer.

We felt in the end, however, that what was needed most was a digital assistant for grocery shopping: a low-touch information service that would be specifically designed for low-income consumers on nutritional assistance programs, but open to use by all consumers. We wanted this service, Fare+Square, to provide the right information at the right time.

Finding the right place and way to intervene

Deciding that we wanted to work at the interface of food, we sketched a broad range of ideas. These ranged from smarter refrigerators, augmented grocery store experiences, even new value-added tax programs that would return benefits to consumer.

We felt in the end, however, that what was needed most was a digital assistant for grocery shopping: a low-touch information service that would be specifically designed for low-income consumers on nutritional assistance programs, but open to use by all consumers. We wanted this service, Fare+Square, to provide the right information at the right time.

Designing for an augmented intelligence

Looking to a near-term future, we designed Fare+Square as an intelligent digital assistant, determining the types of data it might leverage to better understand a consumer’s preferences and needs.

DETAILS
Columbia Graduate School of Architecture, Planning and Preservation (GSAPP)
Measure
AFTER

Published in Columbia GSAPP’s Abstract 2016.

The system monitors circulation, digital productivity and ambient environmental factors in one studio of Avery Hall, and visualizes this data to enable new design interventions. A step toward the Internet of Things, Busy Bee represents one part of a broader feedback loop, where architecture and the space it defines share a dynamic and reciprocal relationship.

The physical computing process in Busy Bee involves real-world data collection and digital data processing. Within the studio, onboard cameras in Raspberry Pi units record human movement (OpenCV-enabled computer vision), while other sensors monitor sound, humidity and temperature (Python interpretation of GPIO input); studio workstations track file generation and associated metadata (Python scripting). Collected data is transmitted via digital cloud services (Google Sheets, Dropbox and Flux) to Grasshopper. The resulting visualization — presented in both three spatial dimensions as well as over time — offers a dynamic and interactive interface for exploring the data.

Busy Bee was implemented over six weeks as part of a half-semester course. Within the team, my focus was on sensor data collection as well as data processing and visualization creation.

The Event Detection Interface was developed as one component of a broader suite of online web tools developed to collect, manage, analyze and visualize built environment data, for educational and institutional clients. The tool needed to accommodate interested but novice stakeholders as wells expert researchers, allowing all levels of users to analyze time-series data from environmental sensors and building systems — producing concrete insight in the form of possible efficiency and optimization measures. 

Through an iterative process, and in collaboration with potential end users, I took an existing proof-of-concept and designed a complete workflow and interface. I also established a graphic langauge and design patterns that could be extended to the rest of the software suite.

The design of the Event Detection Interface prototype included first-time onboarding, data loading and multi-faceted search options — including an advanced search feature that allowed for boolean logic and graphical descriptions. The GUI adopted the concept of a central “canvas,” on top of which users could place rows of time-series data. Floating panels provided access to search parameters, results and a history of saved searches.