Mobile geolocation data provide new opportunities to understand processes of neighborhood change and the dynamics of community connectedness, but introduce significant social, ethical, and computational challenges. Our work in this area leverages large-scale data to identify, model, and analyze mobility behavior and emergent community networks, while developing privacy-preserving approaches to geolocational analytics. We study disparities in evacuation and recovery patterns during and after natural disasters, public-space and park utilization and its influence on community well-being, neighborhood change prediction and new measures of integration, and real-time population dynamics to supplement existing survey-based census 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 at-risk communities; (2) measure and evaluate the extent and effectiveness of social (physical) distancing over time and 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 aggregated travel patterns between neighborhoods and communities.