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

本研究调查人类迁移作为环境和社会变化的结果和原因。该项目提出了两个相关的问题：人们为什么选择迁移，以及他们在原籍和目的地的流动结果是什么？移徙及随后的结果是复杂过程的结果，由于这些过程基于过于笼统的假设和特定地点的因素，因此人们对这些过程并不十分了解。该项目通过将实地调查数据与卫星图像相结合来处理这些复杂的过程，这将产生可推广的移徙和环境变化预测模型。 研究结果将产生一个潜在的变革性的基于代理的模型，可用于预测人口流动，改变土地使用强度和农业生产力。本研究中使用的科学方法将被纳入STEM教育和推广。尽管了解人类流动和生态变化的重要性，但有关移民与环境关系的理论发展缓慢。该项目采用创新方法收集关于实地移徙决策的数据，并辅之以先进的卫星地球观测数据，以衡量土地使用和土地随时间的变化。这些数据被组合成一个基于代理人的预测模型，旨在测试现有的迁移-环境关系理论（例如，人们为获得土地而流动vs.人们追随社交网络而流动），并促进了新兴理论的发展和检验。该项目的成果将提高我们对移民动态的理解，并将提供重要的数据和工具，促进社会和环境变化的复原力。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.