Mobile geolocation data provide new opportunities for near real-time understanding of local population activity, but raises significant social, ethical, and technical challenges. This project uses large-scale locational data to understand mobility behavior across the United States and develop privacy-preserving approaches to geolocational analytics. We are working to understand evacuation and recovery during natural disasters, public-space and park activity and utilization, neighborhood change and socioeconomic integration, and a real-time population census to supplement existing survey-based methods.
Currently, our research is focused on the COVID-19 pandemic. We are developing computational models derived from these data to (1) estimate exposure density across a range of temporal and spatial scales, which will enable public health officials and researchers to evaluate and predict transmission rates in a particular area; (2) measure and evaluate the extent and effectiveness of social (physical) distancing efforts over time and comparatively within and across neighborhoods and cities, as well as understand the disparate impacts on vulnerable communities and populations; and (3) measure the extent of disease spread based on movement and travel patterns between neighborhoods and communities, which will support predictions of the spatial-temporal patterns of disease outbreak and identify at-risk locations based on aggregated mobility.