An analysis of the crucial indicators impacting the risk of terrorist attacks: A predictive perspective

•The proposed crucial indicator identification method combines prediction and recursive feature elimination.•Indicators are obtained from both root causes of terrorism and previous attacks’ feedback.•Terrorist attack risk prediction considers human-property losses and success rate.•The results highl...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Safety science 2021-12, Vol.144, p.105442, Article 105442
Hauptverfasser: Luo, Lanjun, Qi, Chao
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•The proposed crucial indicator identification method combines prediction and recursive feature elimination.•Indicators are obtained from both root causes of terrorism and previous attacks’ feedback.•Terrorist attack risk prediction considers human-property losses and success rate.•The results highlight the essentiality of minimizing human losses and restructuring terrorism breeding background. The factors that may impact the risk of terrorist attacks are numerous and interrelated in a complex manner. This complexity makes the prediction of terrorist attacks challenging and leads to information redundancy and the obscuring of critical points. This paper aims at identifying crucial indicators from the perspective of predicting the risk of terrorist attacks. Both root cause and incident level factors are taken into account, which are qualified using 28 indicators. A random forest (RF) model is established to predict terrorist attack risk, and the prediction performance is recorded as the baseline result in terms of MAE,MSE, and R2. A recursive feature elimination method utilizing random forest kernels (RF-RFE) is proposed to identify crucial ones from the 28 initial indicators. The RF-RFE process gradually eliminates the least important indicators and compares the corresponding prediction performance with the baseline result. The prediction performance is relatively stable until the number of input indicators is reduced from 28 to less than 8. The indicators that make up the input set at the hedging point with eight indicators are considered as the most important ones, including Human_loss, GDPGrowth, MilitaryExpenditure, PopulationGrowth, Population(lg.), Unemployment, UrbanPopulationGrowth, InternalConflict, etc. The identified crucial indicators indicate that foresight and preemptive measures should be taken not only for specific intelligence and response operations, but also to improve the underlying government stability, economic quality, and other basic elements of citizens’ lives.
ISSN:0925-7535
1879-1042
DOI:10.1016/j.ssci.2021.105442