2025/04/06 by Haoyang Wu, Wu, Haoyang, Yen‐Chi Chen +3
Computer Science · Social Sciences · #Applications (stat.AP) #Data Management and Algorithms #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Methodology (stat.ME) #Urban Transport and Accessibility
paper · pdf · doi:10.48550/arxiv.2504.04316
openalex publication_date 2025/04/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce a novel statistical framework for analyzing the GPS data of a single individual. Our approach models daily GPS observations as noisy measurements of an underlying random trajectory, enabling the definition of meaningful concepts such as the average GPS density function. We propose estimators for this density function and establish their asymptotic properties. To study human activity patterns using GPS data, we develop a simple movement model based on mixture models for generating random trajectories. Building on this framework, we introduce several analytical tools to explore activity spaces and mobility patterns. We demonstrate the effectiveness of our approach through applications to both simulated and real-world GPS data, uncovering insightful mobility trends.