Modified Project Summary/Abstract Section Weather extremes and natural disasters associated are wreaking havoc on human health worldwide, and these events will become more frequent and more intense in coming years. Other disruptive shocks – such as exposure to armed conflict and disease outbreaks – also have significant impacts on health globally. Shock-induced mobility patterns likely play a role in linking shocks to adverse health outcomes. Despite the importance of understanding these relationships, little research has been conducted to date due to a dearth of detailed temporal and spatial data on mobility patterns, particularly in settings with limited resources. Novel location data derived from mobile phone use promises to elucidate granular human mobility patterns. Leveraging established partnerships to obtain the data and machine learning methods to process it, the proposed research will provide critical information on the health of shock-affected populations in Kenya, an east African country with increasing environmental volatility and a history of armed conflict. Further, this project will demonstrate the utility of applying these data to address critical population health problems. The proposed Pathway to Independence Award will provide essential and synergistic training to position Dr. Luetke as a leading expert in using big data and artificial intelligence methods to elucidate the social and health implications of shocks and human mobility responses in the context of increasing environmental variability and global insecurity. The mentored phase of the proposed project will provide training in (1) geospatial methods and population-environment research, (2) machine learning for spatial big data, (3) demographic theories and methods related to human migration, and (4) career development activities to prepare to be an independent investigator and future tenure-track faculty member. The empirical research of the proposed project will address three primary aims: (Aim 1) Use artificial intelligence methods to process the mobile phone data to identify mobility patterns over time (2018-2022) and space; (Aim 2) Test the role of environmental variability, natural disasters, and armed conflict as mechanisms to explain changes in these mobility patterns; and (Aim 3) Quantify the effect of weather extremes, natural disasters, and armed conflict on women’s exposure to intimate partner violence and explore mobility patterns, particularly when crisis-induced, as a mediator of these associations. The training and research detailed in this proposal will form a solid foundation to launch a rigorous and sustainable research agenda and provide the pilot work for a future R01 proposal. The additional training and mentorship will be an important step toward establishing a rich, independent research career aimed at reducing social and health disparities. A strong interdisciplinary mentorship team and an outstanding supportive training environment at the Minn...