Geopolitics Quarterly

Geopolitics Quarterly

Fuzzy Analysis of Factors Affecting Border Security in Sistan and Baluchestan Province Based on the Global Terrorism Database

Document Type : Original Article

Authors
Amirhamzeh Khammar 1
Hassan Tarighat Mehrabani 2
Sina Keyhanian 3
1 PhD, Faculty of Mathematical Sciences and Statistics, University of Birjand, South Khorasan, Iran
2 Researcher, National Defense University, Tehran, Iran.
3 Assistant Professor, School of National Security, National Defense University, Tehran, Iran.
10.22034/igq.2026.552243.2105
Abstract
Extended Abstract
Introduction
In West Asia's turbulent geopolitics, Iran's Sistan and Baluchestan Province emerges as a vital borderland, adjacent to volatile Pakistan and Afghanistan and linked to the Indian Ocean through Chabahar Port. This geostrategic hub faces threats from groups like Jaish al-Adl, which leverage porous borders, harsh landscapes, and socioeconomic gaps for infiltration. Post-9/11 global terrorism surges have intensified these risks. This study uses the Global Terrorism Database (GTD) to analyze incidents from 2008 to 2020, applying fuzzy logic to manage vague variables such as security perceptions. It identifies economic, social, judicial, and defense drivers of border insecurity, addressing the limitations of traditional methods. Research questions probe how variables such as population, unemployment, and defense spending affect terrorism forecasts, with goals to model preventive measures for enhanced national stability in this critical zone.
Methododology
This practical study employs fuzzy logistic regression for quantitative modeling of Iran's counterterrorism in the province. Data from GTD (1970–2020) and Iran's Statistics Center (2010–2022) inform 11 independent variables as follows: economic (unemployment, GDP), social (population, students), judicial (drug arrests, thefts), and defense (security budgets). The dependent variable—success rate of thwarted attacks—is fuzzified into terms like "very weak" to "very good" via triangular fuzzy numbers. R software regression (p < 0.05) filters significant variables, removing irrelevant ones like divorce rates. Mathematica fits the model for predictive border security analysis.
Results and Discussion
The model highlights population and unemployment as major negative influencers, fostering extremism in youth-heavy, marginalized areas amid ethnic diversity and neighboring instability. Defense investments and security actions (e.g., drug busts halting smuggling) yield positive effects, emphasizing tech like drones and coordination. Income ratios and GDP reduce threats by alleviating deprivation. Insignificant cultural factors were excluded. Findings align with global research (Freytag et al., 2011) but tailor to Iran's context, noting U.S. proxy wars' role in escalating dangers, urging data-driven adaptations.
Conclusion
This fuzzy framework innovates border security analysis in a high-tension region, stressing socioeconomic uplift and defense enhancements to counter terrorism. It provides policy recommendations: boost jobs, strengthen diplomacy with neighbors, and adopt AI alerts. Fortifying this area secures Iran and rebalances West Asia. Future models could add cyber or migration factors for improved accuracy.
Keywords
Border Security
Fuzzy Analysis
Sistan and Baluchestan
Logistic Regression
Global Terrorism Databse
Subjects
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Geopolitics Quarterly
Volume 22, Issue 3
Summer 2026
Pages 189-217

  • Receive Date 24 November 2025
  • Revise Date 19 February 2026
  • Accept Date 19 June 2026