Life Pattern-Based Human Attribute Estimation Using Only GPS Data

Analysis of large-scale GPS data enables the summarization and structuring of human mobility characteristics, providing insights into residents’ behavior patterns. However, existing methods analyze sequences of individual-derived stay labels, such as home or workplace, which do not accurately reflect actual area functions and can fail to distinguish individuals based on differences in workplaces. This limitation arises because annotating stay locations and purposes in GPS data is challenging due to the complexity of urban structures and constraints on data quality. In this study, we propose ArLPH, a framework that takes only GPS data as input, models mobility from the perspective of “when and what types of places he/she visited,” and enables the interpretation of life patterns. ArLPH consists of two modules: “Area Modeling,” which represents urban areas based on usage patterns, and “Human Modeling,” which represents individuals based on behavior patterns. By the processing flow of area modeling to human modeling, it becomes possible to analyze human mobility while considering area characteristics. Through an evaluation of a large-scale, real-world GPS dataset collected from tens of thousands of smartphone users, we demonstrate that ArLPH enables the characterization of individuals based on their life patterns and estimates attributes such as occupation (e.g., office workers or homemakers) and lifestyle (e.g., those who work frequently on weekends).