A Bayesian mixture model for symmetric matrix data writes one precision matrix as a single power of the other; applied to GPS mobility, it links crime and youth employment to weekly activity patterns.
Modeling Human Spatial Mobility Patterns with the L\'evy Flight Cluster Model
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abstract
Despite the extensive collection of individual mobility data over the past decade, fueled by the widespread use of GPS-enabled personal devices, the existing statistical literature on estimating human spatial mobility patterns from temporally irregular location data remains limited. In this paper, we introduce the L\'{e}vy Flight Cluster Model (LFCM), a hierarchical Bayesian mixture model designed to analyze an individual's activity distribution. The LFCM can be utilized to determine probabilistic overlaps between individuals' activity patterns and serves as an anonymization tool to generate synthetic location data. We present our methodology using real-world human location data, demonstrating its ability to accurately capture the key characteristics of human movement.
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Bayesian Gaussian Mixture Modeling for Symmetric Matrix Variate Data
A Bayesian mixture model for symmetric matrix data writes one precision matrix as a single power of the other; applied to GPS mobility, it links crime and youth employment to weekly activity patterns.