Designing and developing simulations to understand climate-driven migration, mentored by Save The Children’s Innovation Project Lead William Low and Brunel University London’s Reader Derek Groen.
Climate-driven migration is a major challenge for the global community, and frequently cited as a main cause for future societal disruption. However, little is understood about the direct impact mechanisms of climate change on the livelihoods and coping mechanisms of affected people in the Global South. Simulation approaches have previously been successful in forecasting conflict-driven migration (https://www.nature.com/articles/s41598-017-13828-9), and provide a good environment to study the interplay between climate events and the migrations of affected communities.
Flee (flee.readthedocs.io) is a widely recognized agent-based migration model that represents people as individual agents that move between locations and make autonomous decisions based on user-specified criteria. The approach is unique in the migration modeling context in that it works with explicit assumptions, and that it can be used without the need to train it against (often ethically sensitive) humanitarian data. Our latest version has the added capability to explicitly support agents with different ages, ethnicities and economic circumstances, and Brunel University and Save The Children currently collaborate to incorporate support for disaster- and climate-driven migration. This placement will operate within that context, with a specific focus on a particular climate hotspot region.
The Team proposing this project consists of the Migration Modelling Group at Brunel University London (Dr. Derek Groen, Dr. Diana Suleimenova and Dr. Alireza Jahani) in collaboration with Save The Children International (Innovation Lead William Low and Data Scientist Auke Tas). The Migration Modelling Group has led the development of the Flee simulation code, while Save The Children has coordinated efforts to investigate the benefits of Flee to forecast migration in countries such as Burundi, Ethiopia and Nigeria.
In this project we are interested in simulating how communities adapt and respond to climate-driven events such as flooding. The project is divided into two phases, each lasting approximately 3 months.
Phase 1: The initial learning phase is devoted to gaining essential technical knowledge, as well as a well-rounded understanding of the climate and community dynamics in a selected geographical climate hotspot region (e.g., Bangladesh, Somalia or Central America) as we know it today. Deliverables from this phase include an overview report of the migration and climate situation in this region, as well as a prototype simulation sketch which captures the essential communities, climate events, and coping/migration behaviors.
Phase 2: The second phase will focus on the refinement and implementation of the simulation sketch into a working Flee model, and extract preliminary findings by analyzing the behaviors of this model as a running simulation.
Prerequisites: This project requires prior experience in Python programming, and applicants will benefit from basic knowledge of graph theory and at least one type of model (e.g. Nbody, SPH, or any other).
Placement: The successful applicant will work in collaboration with the Migration Modelling Group at Brunel University London and Save The Children International in both phases. Dr. Derek Groen will take the main mentoring responsibility, but the applicant will be encouraged to work with other members of the team as they find convenient.
This placement is now closed to applications.