  The importance of personal mobility data is widely recognized in various
fields. However, the utilization of real personal mobility data raises privacy
concerns. Therefore, it is crucial to generate pseudo personal mobility data
that accurately reflects real-world mobility patterns while safeguarding user
privacy. Nevertheless, existing methods for generating pseudo mobility data,
such as mechanism-based and deep-learning-based approaches, have limitations in
capturing sufficient individual heterogeneity. To address these gaps, taking
pseudo-person(avatar) as ground-zero, a novel individual-based human mobility
generator called GeoAvatar has been proposed - which considers individual
heterogeneity in spatial and temporal decision-making, incorporates demographic
characteristics, and provides interpretability. Our method utilizes a deep
generative model to simulate heterogeneous individual life patterns, a reliable
labeler for inferring individual demographic characteristics, and a Bayesian
approach for generating spatial choices. Through our method, we have achieved
the generation of heterogeneous individual human mobility data without
accessing individual-level personal information, with good quality - we
evaluated the proposed method based on physical features, activity patterns,
and spatial-temporal characteristics, demonstrating its good performance,
compared to mechanism-based modeling and black-box deep learning approaches.
Furthermore, this method maintains extensibility for broader applications,
making it a promising paradigm for generating human mobility data.
