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Proposing a Broader Scope of Predictive Features for Modeling Refugee Counts

The world-wide refugee problem has a long history, but continues to this day, and will unfortunately continue into the foreseeable future. Efforts to anticipate, mitigate and prepare for refugee counts, however, are still lacking. There are many potential causes, but the published research has primarily focused on identifying ways to integrate already existing refugees into the various communities wherein they ultimately reside, rather than on preventive measures. The work proposed herein uses a set of features that can be divided into three basic categories: 1) sociocultural, 2) socioeconomic, and 3) economic, which refer to the nature of each proposed predictive feature. For example, corruption perception is a sociocultural feature, access to healthcare is a socioeconomic feature, and inflation is an economic feature. Forty-five predictive features were collected for various years and countries of interest. As may seem intuitive, the features that fell under the category of "economic" produced the highest predictive value from the regression technique employed. However, additional potential predictive features that have not been previously addressed stood out in our experiments. These include: the global peace index (gpi), freedom of expression (fe), internet users (iu), access to healthcare (hc), cost of living index (coli), local purchasing power index (lppi), homicide rate (hr), access to justice (aj), and women's property rights (wpr). Many of these features are nascent in terms of both their development and collection, as well as the fact that some of these features are not yet collected at a universal level, meaning that the data is missing for some countries and years. Ongoing work regarding these datasets for predicting refugee counts is also discussed in this work.