This newly awarded National Science Foundation grant brings together faculty in Hazards Mitigation, GIS, Math, and Computer Science to investigate mathematical approaches that can provide a deeper understanding of individual risk response by modelling human movement patterns in space and in time, at different levels of granularity. Over the past decade, the proliferation of smartphones and Global Positioning System (GPS)-enabled devices has granted us unprecedented access to vast amounts of location data, timestamped with high precision. The project aims to develop a new deep learning framework implemented on a Geographic Information System (GIS) platform. This framework will advance big spatiotemporal data analytics to identify and predict unexpected or different behaviors. This work will leverage location data gathered through prior research in the Dallas Fort Worth Living Lab. Research goals are to: 1) Develop an agent-based machine learning framework, Markov Decision Process - Inverse Reinforcement Learning - Generative Adversarial Network (MDP-IRL-GAN), to detect anomalies in individual movement dynamics; 2) Model hazard response by analyzing individual movement dynamics using the proposed machine learning framework, while identifying the key factors influencing decision-making in response to hazards; and 3) Detect changes and anomalies in spatiotemporal patterns of group dynamics by employing a newly-designed multi-resolution graph neural network (MA-GNN). The results will contribute to the efficiency of anomaly detection, the accuracy of traffic forecasting, and a deeper comprehension of human risk response behaviors.