A quantitative geospatial analysis of the risk that Boko Haram will target a school
Lirika Sola, Youdinghuan Chen, V. S. Subrahmanian, Steve Zimmerman, Jessica Leight, Jessica Leight
TL;DR
This paper uses data and machine learning to predict the risk of Boko Haram attacking schools in Nigeria based on security, activity, and socioeconomic factors.
Contribution
The paper introduces a novel geospatial dataset and machine learning models to predict Boko Haram school attack risks.
Findings
Security presence, Boko Haram activity, and socioeconomic factors are key predictors of school attacks.
Machine learning models can accurately quantify the likelihood of a school being targeted.
Decision trees reveal specific conditions that increase the risk of attacks.
Abstract
We provide a novel quantitative geospatial analysis of school attacks perpetrated by Boko Haram in Nigeria. Such attacks are used by Boko Haram to kidnap boys (for potential use as child soldiers and suicide bombers) and girls (for potential use as domestic servants, as sex slaves, and suicide bombers). We first build a novel geospatially tagged data set spanning almost 15 years (July 2009 to April 2023) of data not only on Boko Haram attacks on schools (our dependent variable) but also a set of 15 independent variables (or features) about other attacks by Boko Haram, locations of security installations, as well as socioeconomic and geospatial characteristics of the regions around these schools. Second, we develop a univariate statistical analysis of this data, showing strong links between three broad factors affecting attacks on schools: Security presence in and around a school, the…
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Taxonomy
TopicsCrime Patterns and Interventions · Terrorism, Counterterrorism, and Political Violence · COVID-19 epidemiological studies