A time-geographic approach to quantify the duration of interaction in movement data

Interaction between moving individuals is a critical factor in shaping social dynamics and human networks. Recent advancements in trajectory analytics have resulted in promising methods to identify and extract spatio-temporal patterns of interaction using movement tracking data. However, methodologies to quantify the duration of interaction remain limited. In the present work, we advance the existing time-geographic based approach that mainly relies on potential path area computation and polygon intersection to quantify the duration of potential concurrent interactions (i.e. synchronous interaction in space and time) between mobile individuals. Two case studies using real human GPS tracking data in California reveal that in general, the proposed time-geographic based approach outperforms the proximity-based approach which is commonly used in digital contact tracing technologies. Our method is more effective in the identification of potential continuous interactions, especially when individuals do not move together. In addition, the results show that the proposed method can estimate the duration of contacts more accurately and can identify more complete interactions over a continuous time period, while the proximity-based approach underestimates contacts which may result in more intermittent interactions with shorter durations.