Rethinking smart cities: An open-source tool for urban data collection

Urban planning often requires detailed insights into how individuals experience their surroundings. However, collecting such granular data can be challenging with large-scale datasets. Henry Levesque, a doctoral student at the University of Cincinnati's College of Design, Architecture, Art and Planning, is addressing this issue by developing an open-source tool for urban data collection.

Levesque's research focuses on a low-cost, open-source system that enables people to gather highly localized information about their environments. Instead of relying solely on large datasets that describe entire cities or regions, his tools aim to answer questions at a much smaller scale, such as temperature on a specific block or conditions along a sidewalk. This approach allows for more precise data collection tailored to specific research questions.

The system utilizes inexpensive, readily available components to capture images, record GPS location, and measure factors like temperature, humidity, and air quality. The primary device can be worn on a hat or helmet, capturing both the user's experience and their surroundings. Additional sensors can be added to gather further environmental data. Levesque's system stands out for its accessibility, as the data generated can be easily accessed and analyzed using commonly available software.

One of the key features of Levesque's platform is its flexibility. The hardware can be reconfigured based on the research question, with optional components like a solar panel or additional sensors. This adaptability ensures that the tool can be tailored to various research needs, whether stationary measurements or more dynamic data collection.

Levesque has successfully tested this workflow with students, many of whom had no prior experience with coding or physical computing. In a four-week master class, students built sensor packages, identified phenomena to study, collected data, and analyzed the results using tools developed by Levesque. One project involved capturing facial expression estimates during career coaching sessions, providing insights that were difficult to observe during the actual sessions.

This open-source approach not only democratizes data collection but also aligns with Levesque's broader interest in making emerging technologies more accessible. By involving the people experiencing an environment in the data collection process, Levesque's research emphasizes the importance of community-driven research and the use of technology to inform and shape urban environments.