Proposing Location-based Predictive Features for Modeling Refugee Counts

Esther Ledelle Mead, Maryam Maleki, Mohammad Arani and Nitin Agarwal
Additional contact information
Esther Ledelle Mead: Southern Arkansas University, United States
Maryam Maleki: California State University, United States
Mohammad Arani: University of Arkansas at Little Rock, United States
Nitin Agarwal: University of Arkansas at Little Rock, United States

Transnational Education Review, 2023, vol. 1, issue 1, 3-16

Abstract: Machine learning models to predict refugee crisis situations are still lacking. The model proposed in this work uses a set of predictive features that are indicative of the sociocultural, socioeconomic, and economic characteristics that exist within each country and region. Twenty-eight features were collected for specific countries and years. The feature set was tested in experiments using ordinary least squares regression based on regional subsets. Potential location-based features stood out in our results, such as the global peace index, access to electricity, access to basic water, media censorship, and healthcare. The model performed best for the region of Europe, wherein the features with the most predictive power included access to justice and homicide rate. Corruption features stood out in both Africa and Asia, while population features were dominant in the Americas. Model performance metrics are provided for each experiment. Limitations of this dataset are discussed, as are steps for future work.

Keywords: Data Science; Machine Learning; Predictive Modeling; Refugee Crisis (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.tplondon.com/ter/article/view/2883/2196 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mig:terjrl:v:1:y:2023:i:1:p:3-16

Ordering information: This journal article can be ordered from
https://journals.tpl ... ormation/librarians/

DOI: 10.33182/ter.v1i1.2883

Access Statistics for this article

Transnational Education Review is currently edited by Dr Lan Lo

More articles in Transnational Education Review from Transnational Press London, UK
Bibliographic data for series maintained by TPLondon ().