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Predicting non-state terrorism worldwide.

Andre Python1,2, Andreas Bender3, Anita K Nandi2

  • 1Center for Data Science, Zhejiang University, Hangzhou, P.R. China. apython@zju.edu.cn.

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Predicting non-state terrorism locally is possible using publicly available data. Theoretically informed models, including structural and procedural factors, outperform past event models for short-term terrorism forecasting.

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Area of Science:

  • Political Science
  • Criminology
  • Data Science

Background:

  • Non-state terrorism causes thousands of global deaths annually.
  • Accurate local, short-term terrorism predictions are crucial for policymakers.
  • Existing models often rely solely on historical event data.

Purpose of the Study:

  • To develop and validate predictive models for local non-state terrorism.
  • To assess the efficacy of theoretically informed predictors versus historical data alone.
  • To identify key drivers of terrorism at local and regional levels.

Main Methods:

  • Utilized publicly available data for model development.
  • Incorporated structural and procedural variables into predictive models.
  • Compared performance of theoretically informed models against historical event-based models.

Main Results:

  • Models with structural and procedural predictors accurately forecast local non-state terrorism a week in advance.
  • Theoretically informed models consistently outperformed models based only on past terrorist events.
  • Identified and interpreted significant local drivers of global and regional terrorism.

Conclusions:

  • Theoretically informed models offer a powerful tool for predicting political violence.
  • Publicly available data can be leveraged for effective, policy-relevant terrorism forecasting.
  • This approach enhances understanding and mitigation of non-state terrorism.