The field of data science is witnessing significant advancements in data augmentation and urban mobility modeling. Researchers are developing innovative methods to generate synthetic data that can effectively capture complex patterns and distributions, thereby enhancing the accuracy of machine learning models. A notable trend is the use of generative models, such as transformer-based approaches and diffusion models, to augment data for various applications, including time series classification and urban mobility analysis. These models have shown remarkable performance in generating behaviorally valid activity patterns and capturing intrinsic temporal patterns. Furthermore, studies are leveraging large-scale dynamic analyses of mobile network data to investigate commuting patterns and accessibility inequalities in urban areas, revealing significant disparities between sociodemographic groups. Noteworthy papers include: Beyond 9-to-5, which introduces a novel transformer-based approach for augmenting mobility data of underrepresented shift workers. Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion proposes a multi-view, multi-stream network traffic generative model that exhibits higher statistical similarity to original data compared to current state-of-the-art solutions.