  This paper introduces ReeSPOT, a novel Reeb graph-based method to model
patterns of life in human trajectories (akin to a fingerprint). Human behavior
typically follows a pattern of normalcy in day-to-day activities. This is
marked by recurring activities within specific time periods. In this paper, we
model this behavior using Reeb graphs where any deviation from usual day-to-day
activities is encoded as nodes in the Reeb graph. The complexity of the
proposed algorithm is linear with respect to the number of time points in a
given trajectory. We demonstrate the usage of ReeSPOT and how it captures the
critically significant spatial and temporal deviations using the nodes of the
Reeb graph. Our case study presented in this paper includes realistic human
movement scenarios: visiting uncommon locations, taking odd routes at
infrequent times, uncommon time visits, and uncommon stay durations. We analyze
the Reeb graph to interpret the topological structure of the GPS trajectories.
Potential applications of ReeSPOT include urban planning, security
surveillance, and behavioral research.
