Heat exposure is a growing concern in the United States, particularly in urban areas where extreme heat events are becoming more frequent. High temperatures contribute to a wide range of adverse outcomes, including heat-related health issues, stress on energy supply, and damages to infrastructure systems. Urban environments are especially vulnerable due to high building density, limited natural vegetation, and unequal distribution of cooling centers. As a result, an important challenge for research and planning is to characterize the heat exposure patterns. A number of existing assessments measure heat exposure based on environment features, which overlook the fact that people regularly move across various environments.

During Summer School 2026, this team adopted an activity-based perspective by integrating urban heat data with human mobility flows to estimate the individual-level heat exposure in an urban environment. They constructed human mobility networks and capture the heat exposure patterns based on these mobility patterns. Spatial AI techniques were used to analyze, interpret, and predict these patterns, as well as identify important environment features, such as green spaces. The output illustrates how people move around different thermal conditions, offering a data-driven understanding of urban heat exposure through the lens of movement.