Life Pattern Based Human Attribute Estimation Using Only Raw 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 (e.g., home or workplace), which do not reflect actual area functions and may fail to distinguish individuals based on differences in workplaces. This limitation arises because annotating stay locations and purposes in raw 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 raw GPS data as input, models mobility from the perspective of "when and what types of places he/she visited," and enables the interpretation of individuals’ 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 clustering the representations generated by these modules, it becomes possible to estimate occupational and behavioral attributes through the interpretation of life patterns. Through an evaluation of a large-scale, real-world GPS dataset collected from tens of thousands of smartphone users, we demonstrate that ArLPH can estimate occupational attributes, such as office workers or homemakers, as well as behavioral attributes, including those who frequently work on weekends.