Rethinking smart cities

Rethinking smart cities

DAAP doctoral student develops open source tool for urban data collection

A large part of urban planning involves navigating how people experience a place. That kind of insight can require collecting data at a level of detail that large-scale datasets cannot provide.

Henry Levesque is a doctoral student in Regional Development Planning at the University of Cincinnati's College of Design, Architecture, Art and Planning (DAAP). He is exploring a different approach to smart-city data collection by developing an open-source tool designed to make that process more accessible. His research centers on an open source, low-cost system that allows people to collect highly localized information about their environments and retain greater control over how that data is gathered and used.

Rather than relying exclusively on large data sets designed to describe an entire city or region, Levesque's tools are designed to answer questions at a much smaller scale: What is the temperature on this particular block? What are the conditions along this sidewalk? How does a person respond to a particular environment?

"If I only want temperature data around a very specific block or intersection or neighborhood, I don't need a data set of an entire city or county," Levesque said. "I could instead collect my own data set that could have better granularity."

Levesque's data collection platform uses inexpensive, off-the-shelf components to collect information about an environment and the people experiencing it. In its current configuration, the device can be worn on a hat or attached to a helmet and captures images of both the user and what the user is viewing. It can simultaneously record GPS location and can be configured with additional sensors to collect information such as temperature, humidity and air quality.

Levesque combines various retail electronics to create tools for data collection.

Making urban data more accessible

Levesque's primary data collection device can be worn on a hat or helmet. In its current configuration, it captures images and records GPS information. Additional sensors can be added to measure factors such as air quality, temperature, humidity and light.

The system uses inexpensive, readily available components rather than specialized equipment. Levesque has also designed the system so that the data remains accessible to the people collecting it.

Instead of producing data locked into a proprietary format, the system can generate image sequences and timestamped data that can be opened and analyzed using commonly available software. A collection could include an image every five seconds, every minute or at another selected interval, along with information such as location and temperature.

For Levesque, that accessibility is particularly important when research is intended to inform decisions about communities and urban environments.

Henry Levesque DAAP doctoral candidate

"I hope to contribute more accessible methods of understanding urban environments," Levesque said. "It's cheaper, it's easier, and it doesn't require a lot of specialized equipment or expertise."

The system can also be reconfigured depending on the research question. A device could be mounted in a stationary location, for example, and used to collect environmental measurements over time. GPS could be removed if location tracking is unnecessary, while a solar panel or additional sensors could be added.

That flexibility is central to Levesque's approach to urban data.

From research to the classroom

Levesque has tested the complete workflow with students, many of whom had little or no previous experience with coding or physical computing. During a four-week master class, students built sensor packages, identified phenomena they wanted to study, collected data and analyzed the results using additional tools developed by Levesque.

One project examined facial expression estimates during career coaching sessions. With participants' permission, a student used the sensor system to capture images at regular intervals during a coaching session. Levesque's open source analysis tool then processed the image sequence and generated estimates across seven facial expression categories at each timestamp.

The resulting data gave the student coach another way to examine how facial expressions shifted over time, providing information that could be difficult to observe while simultaneously conducting the coaching session.

The project is part of Levesque's doctoral research, which combines open source data collection and analysis with AI-assisted methods. His broader work explores ways to transform unstructured information, including text and images, into structured datasets that can be analyzed and verified by researchers.

Levesque's work also reflects a broader interest in making emerging technology more accessible. His publicly available research tools include an AI-assisted analysis platform and a Raspberry Pi-based data collection system.

His approach places technology within a broader community-centered research process, where the people experiencing an environment can play a role in determining what should be measured and what questions the data should help answer.

In that sense, Levesque's research offers a different way to think about what makes a city "smart." The question is not only how much data a city can collect, but also who has the tools and knowledge to collect it, understand it and use it to shape change.

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Featured image at top of an aerial view of the DAAP building. Photos provided

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