This project uses advances in artificial intelligence, computer science, and statistics to develop the GeoRDyn toolkit to allow researchers to identify the spatial and relational complexities in more fine-grained data. This will permit new approaches to investigating longstanding questions in the field, such as if and how networks emerge and influence collective action and the conditions under which events diffuse across time and space. Events are often observed at different temporal and spatial scales, involving complex interactions among actors at the micro, macro, and meso levels. Methodological tools commonly used in the study of collective-action events are often less dynamic and/or do not account for complex dependencies. This project develops the GeoRDyn toolkit to help researchers dissect the spatial and relational complexities in events and draw rigorous causal inferences. The toolkit incorporates recent advances in artificial intelligence (AI), computer science, and statistics. The project develops new methods and software for studying complex and dynamic processes, such as diffusion, that can be used by researchers as well as those outside of the academic community who need to understand the causes and potential implications of events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.