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.

翻译：本文提出ReeSPOT，一种新颖的基于Reeb图方法，用于对人类轨迹中的行为模式（类似于指纹）进行建模。人类日常活动通常遵循常态模式，其特征是在特定时间段内出现重复性活动。本文利用Reeb图对此类行为进行建模，将日常活动的任何偏离编码为Reeb图中的节点。所提算法的复杂度与给定轨迹中的时间点数量呈线性关系。我们展示了ReeSPOT的应用方式，以及它如何通过Reeb图节点捕捉关键的空间与时间偏差。本文的案例研究包含真实的人类移动场景：访问非常规地点、在非频繁时段选择异常路径、非正常时间访问以及异常停留时长。我们通过分析Reeb图来解读GPS轨迹的拓扑结构。ReeSPOT的潜在应用包括城市规划、安防监控及行为研究。