ReeSPOT: Reeb Graph Models Semantic Patterns of Normalcy in Human Trajectories

TL;DR

ReeSPOT uses Reeb graphs to model and detect deviations in human movement patterns, providing a topological approach to understanding normalcy and anomalies in GPS trajectories.

Contribution

The paper introduces ReeSPOT, a novel Reeb graph-based method for modeling and analyzing deviations in human trajectories, with linear complexity and practical case studies.

Findings

ReeSPOT effectively captures spatial and temporal deviations.

The method has linear complexity with respect to trajectory data.

Case studies demonstrate its applicability in real-world scenarios.

Abstract

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.