  Predicting forced displacement is an important undertaking of many
humanitarian aid agencies, which must anticipate flows in advance in order to
provide vulnerable refugees and Internally Displaced Persons (IDPs) with
shelter, food, and medical care. While there is a growing interest in using
machine learning to better anticipate future arrivals, there is little
standardized knowledge on how to predict refugee and IDP flows in practice.
Researchers and humanitarian officers are confronted with the need to make
decisions about how to structure their datasets and how to fit their problem to
predictive analytics approaches, and they must choose from a variety of
modeling options. Most of the time, these decisions are made without an
understanding of the full range of options that could be considered, and using
methodologies that have primarily been applied in different contexts - and with
different goals - as opportunistic references. In this work, we attempt to
facilitate a more comprehensive understanding of this emerging field of
research by providing a systematic model-agnostic framework, adapted to the use
of big data sources, for structuring the prediction problem. As we do so, we
highlight existing work on predicting refugee and IDP flows. We also draw on
our own experience building models to predict forced displacement in Somalia,
in order to illustrate the choices facing modelers and point to open research
questions that may be used to guide future work.
