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Due to the limited availability of actual large-scale datasets, realistic synthetic trajectory data play a crucial role in various research domains, including spatiotemporal data mining and data management, and domain-driven research related to transportation planning and urban analytics. Existing generation methods rely on predefined heuristics and cannot learn the unknown underlying generative mechanisms. This work introduces two end-to-end approaches for trajectory generation. The first approach comprises deep generative VAE-like models that factorize global and local semantics (habits vs. random routing change). We further enhance this approach by developing novel inference strategies based on variational inference and constrained optimization to ensure the validity of spatiotemporal aspects. This novel deep neural network architecture implements generative and inference models with dynamic latent priors. The second approach introduces a language model (LM) inspired generation as another benchmarking and foundational approach. The LM-inspired approach conceptualizes trajectories as sentences with the aim of predicting the likelihood of subsequent locations on a trajectory, given the locations as context. As a result, the LM-inspired approach implicitly learns the inherent spatiotemporal structure and other embedded semantics within the trajectories. These proposed methods demonstrate substantial quantitative and qualitative improvements over existing approaches, as evidenced by extensive experimental evaluations. 

To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used large language models (LLMs) and traditional transformer-based models, such as BERT, to classify news and social media events using the example of the Sudan Conflict. A systematic evaluation framework was introduced to test GPT models using Zero-Shot prompting, Retrieval-Augmented Generation (RAG), and RAG with In-Context Learning (ICL) against standard and hyperparameter-tuned bert-based and bert-large models. BERT outperformed GPT in F1-score and accuracy for multi-label classification (MLC) while GPT outperformed BERT in accuracy for Single-Label classification from Multi-Label Ground Truth (SL-MLG). The results illustrate that a larger model size improves classification accuracy for both BERT and GPT, while BERT benefits from hyperparameter tuning and GPT benefits from its enhanced contextual comprehension capabilities. By addressing challenges such as overlapping semantic categories, task-specific adaptation, and a limited dataset, this study provides a deeper understanding of LLMs’ applicability in constrained, real-world scenarios, particularly in highlighting the potential for integrating NLP with other applications such as GIS in future conflict analyses. 

Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science. 

Infectious disease spread within the human population can be conceptualized as a complex system composed of individuals who interact and transmit viruses through spatio-temporal processes that manifest across and between scales. The complexity of this system ultimately means that the spread of infectious diseases is difficult to understand, predict, and respond to effectively. Research interest in GeoAI for public health has been fueled by the increased availability of rich data sources such as human mobility data, OpenStreetMap data, contact tracing data, symptomatic online surveys, retail and commerce data, genomics data, and more. This data availability has resulted in a wide variety of data-driven solutions for infectious disease spread prediction which show potential in enhancing our forecasting capabilities. This book chapter (1) motivates the need for AI-based solutions in public health by showing the heterogeneity of human behavior related to health, (2) provides a brief survey of current state-of-the-art solutions using AI for infectious disease spread prediction, (3) describes a use-case of using large-scale human mobility data to inform AI models for the prediction of infectious disease spread in a city, and (4) provides future research directions and ideas.