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This study introduces a new method for analyzing sparse temporal events like terrorism. The findings indicate that current security policies have been ineffective against major African terrorist groups.

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Area of Science:

  • Political Science
  • Criminology
  • Data Science

Background:

  • Standard time-series analyses struggle with sparse event data, limiting the understanding of temporal patterns in terrorism.
  • Existing methods are ill-suited for analyzing low-frequency temporal events like terrorist attacks.

Purpose of the Study:

  • To propose a novel technique for analyzing low-frequency temporal events, specifically terrorism.
  • To detect trends and shocks in terrorist group activities not visible through other methods.
  • To analyze the intensity and strategic shifts of Boko Haram and Al-Shabaab.

Main Methods:

  • Developed a novel technique based on cumulative curves and gradients for analyzing sparse temporal events.
  • Employed an iterative algorithm using a piecewise linear function to detect trends and shocks.
  • Utilized disaggregated political violence data from the Armed Conflict Location & Event Data Project (ACLED).

Main Results:

  • The method successfully detects shifts in terrorist group capabilities and attack patterns.
  • Analysis highlights significant changes in government strategies based on attack directionality.
  • Identified key moments of change for Boko Haram (including ISWAP) and Al-Shabaab.

Conclusions:

  • Security policies have largely failed to diminish the forces of Boko Haram and Al-Shabaab.
  • The novel technique offers improved insights into temporal patterns of low-frequency events.
  • Understanding strategic shifts is crucial for effective counter-terrorism policy.