The paper by D. Brockmann, L. Hufnagel, and T. Geisel investigates the scaling laws of human travel, a critical factor in various spatiotemporal phenomena such as the spread of infectious diseases. Using a comprehensive dataset of over a million individual displacements of banknotes in the United States, the authors find that human travel exhibits anomalous dispersal characteristics. Specifically, the distribution of travel distances follows a power law, indicating scale-free random walks known as Lévy flights. Additionally, the probability of remaining in a small, spatially confined region for a given time is dominated by algebraic long tails, which attenuate superdiffusive spread. The authors propose a two-parameter continuous-time random walk (CTRW) model to describe human travel behavior on various spatiotemporal scales. This model accounts for both scale-free spatial displacements and long waiting times between displacements. The model predicts that human travel is an ambivalent, effectively superdiffusive process, with a temporal scaling exponent close to unity. The empirical data supports this model, showing that the dispersal of banknotes exhibits universal scaling behavior over a wide range of times. The findings have implications for understanding the spread of human infectious diseases, as they provide a quantitative framework for modeling human travel patterns. The authors conclude that their results will serve as a starting point for developing new models to predict the spread of such diseases.The paper by D. Brockmann, L. Hufnagel, and T. Geisel investigates the scaling laws of human travel, a critical factor in various spatiotemporal phenomena such as the spread of infectious diseases. Using a comprehensive dataset of over a million individual displacements of banknotes in the United States, the authors find that human travel exhibits anomalous dispersal characteristics. Specifically, the distribution of travel distances follows a power law, indicating scale-free random walks known as Lévy flights. Additionally, the probability of remaining in a small, spatially confined region for a given time is dominated by algebraic long tails, which attenuate superdiffusive spread. The authors propose a two-parameter continuous-time random walk (CTRW) model to describe human travel behavior on various spatiotemporal scales. This model accounts for both scale-free spatial displacements and long waiting times between displacements. The model predicts that human travel is an ambivalent, effectively superdiffusive process, with a temporal scaling exponent close to unity. The empirical data supports this model, showing that the dispersal of banknotes exhibits universal scaling behavior over a wide range of times. The findings have implications for understanding the spread of human infectious diseases, as they provide a quantitative framework for modeling human travel patterns. The authors conclude that their results will serve as a starting point for developing new models to predict the spread of such diseases.