How Can AI Address Climate Hazards in Urban Areas?

THE PUBLIC’S AWARENESS and understanding of artificial intelligence (AI) has exploded in recent years. Many have explored the benefits of these tools personally and professionally, including those in the public health sector. AI has the potential to improve the field’s ability to promote the health of all people in various communities.

In 2023, Alex Quistberg, PhD, MPH, associate research professor with the Urban Health Collaborative (UHC) at the Dornsife School of Public Health (DSPH), received a grant to study the health effects of climate change in two Colombian cities, Bogotá and Barranquilla. This data, currently being collected, will inform policymakers and advocates on evidence-based mitigation and adaptation strategies. It will also contribute to machine learning and artificial intelligence models that can be used to address climate hazards in similar urban areas throughout the global south.

Quistberg and his multidisciplinary team at DSPH, Universidad de los Andes, and Universidad del Norte are gathering neighborhood-level data, street-level images, and audio recordings from citizen scientists to provide a rich dataset that links environmental and social characteristics, climate data, and health outcomes. The team is providing data from Bogotá and Barranquilla’s informal settlements – traditionally under-researched areas that face disproportionately high exposure to climate hazards compared to other urban neighborhoods.

“Despite at least a quarter of urban residents living in informal settlements in Latin American cities and these populations being more vulnerable to the effects of climate change, there is scarce data to quantify their risk,” Quistberg said.

By collecting comprehensive health, environment and other data in partnership with informal communities and other city stakeholders, researchers can identify potential climate adaptation and mitigation strategies to reduce their risk to the effects of climate change. They will also be preparing traditional data and collecting new data from street-level and satellite imagery for gathering further insights from the data through AI algorithms, such as identifying informal settlements from these images that can be implemented in other cities.

"By engaging with our partners, we observe urban health in other contexts, while also finding novel ways to address similar issues in Philadelphia and elsewhere in the U.S."

The team at the UHC is joined by Olga L. Sarmiento, professor in the department of Public Health at the School of Medicine, Universidad de los Andes in Bogotá, and Natalia Hoyos Botero, professor in the department of History and Social Sciences, Universidad del Norte in Barranquilla. The UHC has collaborated with these two universities previously on research initiatives and student learning opportunities.

“Global collaborations enable a bilateral exchange of learning and deepen our understanding of urban health challenges,” Quistberg said. “By engaging with our partners, we observe urban health in other contexts, while also finding novel ways to address similar issues in Philadelphia and elsewhere in the U.S. For example, Bogotá has long been an example of mobility innovation, like bus rapid transit and open streets, which are being adopted and adapted by many U.S. cities.”

This work is funded by a grant from the Lacuna Fund, an organization that supports the creation of open data sets from low- and middle-income countries to contribute to machine learning and artificial intelligence models that better represent traditionally underserved populations.