Information Geometry Reveals Hidden Patterns in How People Move Through Cities

This research moves beyond traditional dimensionality reduction techniques by treating entire pedestrian trajectory distributions as geometric points rather than isolated observations. By leveraging information geometry's dual flat structure and asymmetric KL divergence measures, the author uncovers non-linear behavioral differences between urban locations that symmetric metrics would miss—revealing how urban spatial structure invisibly shapes human movement patterns.