Semantic Motif-Based Analysis of Post-Pandemic Mobility Adaptation in Brazil Using Mobile Phone Data

Human mobility analysis is critical for applications ranging from urban planning and epidemic modeling to personalized recommendation systems. However, existing approaches often neglect the semantic dimension of mobility which can reveal latent behavioral insights. In this work, we introduce semantic motifs to capture lifestyle shifts at both individual and collective levels. Using the proposed framework, we analyze 95.5 million GPS records from 65,402 mobile users in Brazil, comparing movement behaviors between 2021, strict COVID-19 restrictions period, and 2022, after easing restrictions. Our distributed pipeline, implemented on Apache Spark and Apache Sedona, scales efficiently to extract and cluster these motifs, enabling large-scale spatiotemporal analysis. Key findings reveal a transition from teleworking to hybrid lifestyles with greater variability in displacements.