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.
翻译:预测被迫流离失所是许多人道主义援助机构的一项重要任务,它们必须预先预测流动情况,以便向弱势难民和国内流离失所者提供住所、食物和医疗照顾。虽然人们越来越有兴趣利用机器学习来更好地预测未来抵达者,但在实践中,对于如何预测难民和境内流离失所者流动情况缺乏标准化的知识。研究人员和人道主义官员面临需要决定如何构建其数据集和如何适应其问题以预测分析方法,他们必须从各种模式选项中作出选择。这些决定大多是在不了解可考虑的各种选择的情况下作出的,并且使用主要在不同情况下应用的方法,以及不同目标作为机会性参考。我们努力通过提供系统的模式-认知框架,根据大数据源的使用情况加以调整,促进对这一新兴研究领域的更全面了解,以构建预测问题。我们这样做时,我们强调关于预测难民和境内流离失所者流动的现有工作。我们还利用我们自己的经验建立模型来预测索马里面临的强迫流离失所问题,以便说明未来研究所使用的工作方向。