Adaptive Human Migration in Changing Environments: Leveraging Geographic Sciences to Advance New Models of Migration Decision-Making

点击翻译按钮获取中文摘要

This research investigates human migration as both a consequence of, and cause of environmental and social change. This project asks two related questions: why do people choose to move, and what are outcomes of their mobility in both origin and destination areas? Migration and subsequent outcomes result from complex processes that are not well understood because they are based on over-generalized assumptions and on place-specific factors. This project addresses these complex processes by combining field survey data and satellite imagery that will result in generalizable and predictive models of migration and environmental change. Results will produce a potentially transformative agent-based model that can be used to predict population movements, changing land use intensity and agricultural productivity. The scientific methods used in this research will be incorporated into STEM education and outreach.Despite the importance of understanding human mobility and ecological change, theory regarding migration-environment relationships has been slow to develop. This project uses innovative methods to collect data about migration decision-making on the ground coupled with advanced satellite-based Earth observation data that measures land use and land change over time. These data are combined into a predictive agent-based model designed to test existing theories on migration-environment relationships (e.g., people move to access land vs. people move following social networks) and promoting the development and testing of emerging theories. The results of this project will improve our understanding of migration dynamics and will provide important data and tools that promotes resilience in social and environmental change.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.