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Terrorism Risk, Resilience and Volatility: A Comparison of Terrorism Patterns in Three Southeast Asian Countries

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Abstract

Objective

This article explores patterns of terrorist activity over the period from 2000 through 2010 across three target countries: Indonesia, the Philippines and Thailand.

Methods

We use self-exciting point process models to create interpretable and replicable metrics for three key terrorism concepts: risk, resilience and volatility, as defined in the context of terrorist activity.

Results

Analysis of the data shows significant and important differences in the risk, volatility and resilience metrics over time across the three countries. For the three countries analysed, we show that risk varied on a scale from 0.005 to 1.61 “expected terrorist attacks per day”, volatility ranged from 0.820 to 0.994 “additional attacks caused by each attack”, and resilience, as measured by the number of days until risk subsides to a pre-attack level, ranged from 19 to 39 days. We find that of the three countries, Indonesia had the lowest average risk and volatility, and the highest level of resilience, indicative of the relatively sporadic nature of terrorist activity in Indonesia. The high terrorism risk and low resilience in the Philippines was a function of the more intense, less clustered pattern of terrorism than what was evident in Indonesia.

Conclusions

Mathematical models hold great promise for creating replicable, reliable and interpretable “metrics” to key terrorism concepts such as risk, resilience and volatility.

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Notes

  1. In this instance, we define Indonesia as including East Timor.

  2. The shifting is necessary so no mass is given to 0. The shifted decay function g(u) has the same value as a negative binomial pmf at u − 1. Zero-truncated distributions are another suitable option for the decay function.

  3. In addition, Table 2 in Porter and White (2012) shows that letting μ vary over time has little effect on the estimates of the self-exciting component parameters. This is intuitive considering the relative small magnitude of μ as compared to λ(t). In order for variations in μ to significantly alter the values of the self-exciting component parameters B(t) would have to be a function that closely replicated the fitting capabilities of the self-exciting component. Thus, as B(t) approaches the self-exciting component, the self-exciting component becomes redundant, and the issues of identifiability and inestimability become evident.

  4. An example of this is shown in Fig. 2b.

  5. Defining T as the total number of days in the period of analysis (2000–2010), from (4) the log-likelihood is \(\log(L)=T[\theta\log(\theta)-\log(\Upgamma(\theta))]+ \sum_{t=1}^T\log(\Upgamma(\theta)+y_t)-\log(y_t!)-(\theta+y_t)\log(\lambda(t)+\theta) +y_t\log(\lambda(t)).\)

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  1. The Institute for Social Science Research and ARC Centre of Excellence in Policing and Security, The University of Queensland, Brisbane, QLD, 4072, Australia

    Gentry White & Lorraine Mazerolle

  2. GeoEye Analytics, McLean, VA, USA

    Michael D. Porter

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White, G., Porter, M.D. & Mazerolle, L. Terrorism Risk, Resilience and Volatility: A Comparison of Terrorism Patterns in Three Southeast Asian Countries. J Quant Criminol 29, 295–320 (2013). https://doi.org/10.1007/s10940-012-9181-y

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