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，一种基于Reeb图的新方法，用于对人类轨迹中的生活模式（类似于指纹）进行建模。人类行为在日常活动中通常遵循常态模式，其特点是特定时间段内重复出现的活动。本文使用Reeb图对此行为进行建模，其中任何偏离日常常态活动的行为均被编码为Reeb图中的节点。所提算法的复杂度相对于给定轨迹的时间点数量呈线性。我们展示了ReeSPOT的用法，以及如何通过Reeb图的节点捕获具有关键意义的时空偏离。本文的案例研究包含现实人类移动场景：访问非常见地点、在非惯常时间选择异常路线、非常规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、安全监控和行为研究。