Advancing Methods to Trace and Contextualize Space-Time Interaction Patterns in Movement Data

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This research project will advance computational approaches to trace and characterize interactions and critical encounters between agents in a mobile network. Examples of networks include people in a city, a group of animals in an ecosystem, or a fleet of vessels. Despite the advances in tracking technologies, computational movement analysis methods remain limited in quantification and characterization of dynamic interaction patterns in large mobile networks. As the decade turned to the 2020s, society witnessed the widespread transmission of SARS-CoV-2 through respiratory droplets via close contacts and or lagged interactions between individuals. This led to a set of unprecedented non-pharmaceutical interventions including digital contact tracing to mitigate the spread of the COVID-19. However, current techniques are inefficient for tracing and detecting critical or risky encounters or temporally lagged interactions between healthy and potentially infected individuals. Using movement observations, this project will provide data-driven results about interactions between moving agents. The results will enhance contact-tracing technologies for examining potential human exposure to health risks or infectious agents. More generally, the methods to be developed will enable scientists to model social behaviors in human and animal networks. The project will create open-access/open-source analytical tools which will make spatial data science more accessible to researchers, educators, and students in geography and other fields. The project will provide training and research experiences for graduate students.This research will develop and evaluate novel context-aware time-geographic analytical methods through optimized computational algorithms to (1) trace dynamic interactions and measure the duration and frequency of encounters between individuals using large movement data sets, and (2) to contextualize encounters, concurrent interactions, and lagged interactions to better identify critical or risky contacts. The research will investigate three overarching research questions: (1) How can we best leverage statistical approaches and time geographic methods for better estimation of contact through movement? (2) Given large movement observations, how can we effectively and efficiently trace and identify 'risky' or 'interesting' encounters between individuals? (3) Can interaction analytics be used to understand collective movement patterns in social networks of humans and animals? A set of case studies and open analytical tools will be developed to demonstrate the efficacy of the analytical framework using real GPS observations of people and animals. The analytical methods to be developed in this study will be generalizable to understanding interaction in both social and ecological systems, contributing new knowledge about social behavior of humans and competition of keystone species.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.