Abstract
In order to assess the effectiveness of counter-terrorism interventions, terrorism must First be quantitatively measured and appropriate statistical tests developed. By combining aspects of both the frequency and impact of terrorist attacks, we describe how a marked point process framework can establish a comprehensive measure of terrorism. In addition, we show how change point analysis can provide a powerful alternative to intervention analysis in assessing the effectiveness of counter-terrorism efforts. This is illustrated by examining the in uence Detachment 88, a specialized counter-terrorism unit, had on the terrorism process in Indonesia.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We are assuming that the terrorist process is observed over discrete time units (e.g., days) and hence multiple events could occur at the same time. While this differs from most continuous time point process models that often require no events to occur at the same time, this causes no problem for the models used here.
- 2.
A Poisson point process has the properties that the number of events in any time interval [s,  t] is Poisson random variable with mean \( \mathbb{E}[N(s,t)]\)and the number of events in any set of disjoint (nonoverlapping) intervals are independent.
- 3.
While our examples will employ an impact score that is always greater than or equal to 0, there is no reason not to allow for negative values. For example, failed or foiled attacks, might actually improve measures like public perceptions of safety and security.
- 4.
See http://www.start.umd.edu/gtd/downloads/Codebook.pdf for the definition of a successful attack.
- 5.
Note that this is equivalently to the hazard function used in survival analysis. See Brown, Barbieri, Eden, and Frank (2003) for a discussion of the general correspondence between the intensity and hazard functions for more complex models.
- 6.
Using Poisson regression via a generalized linear model (GLM) (McCullagh & Nelder, 1989).
- 7.
days per year times 6 years.
- 8.
The impact score (5.3) is actually a discrete distribution with a mass at 0 corresponding to events that are not successful, a mass at 10 corresponding to events that have no injuries or fatalities, and also a long tail due to the extreme values of some large, deadly attacks.
- 9.
Eighteen equal width bins were constructed over the range [0,  360] and the estimated likelihood of observing an observation in a bin is the number of impact scores that fell into the bin divided by the total number of impact scores considered.
References
Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the basque country. American Economic Review, 93, 113–132.
Barros, C. P. (2003). An intervention analysis of terrorism: The Spanish ETA case. Defence and Peace Economics, 6(14), 401–412.
Basseville, M., & Nikiforov, I. (1993). Detection of abrupt changes: Theory and application. Englewood Cliffs: Prentice Hall.
Box, G. E. P., & Tiao, G. C. (1975). Intervention analysis with application to economic and environmental problems. Journal of the American Statistical Association, 70(349), 70–79.
Brown, E., Barbieri, R., Eden, U., & Frank, L. (2003). Likelihood methods for neural spike train data analysis. In J. Feng (Ed.), Computational neuroscience: A comprehensive approach, chap. 9. London/Boca Raton: Chapman & Hall/CRC.
Chen, A. H., & Siems, T. F. (2004). The effects of terrorism on global capital markets. European Journal of Political Economy, 20, 349–366.
Clauset, A., Young, M., & Gleditsch, K. S. (2007). On the frequency of severe terrorist events. Journal of Conflict Resolution, 51(1), 58–87.
Collier, P. (1999). On the economic consequences of civil war. Oxford Economic Papers, 51, 168–183.
Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Roayl Statistical Society, Series B, 34, 187–220.
Daley, D. J., & Vere-Jones, D. (2003). An introduction to the theory of point processes (2nd ed., Vol. I). New York: Springer.
Dugan, L. (2010). Estimating effects over time for single and multiple units. In A. R. Piquero & D. Weisburd (Eds.), Handbook of quantitative criminology (pp. 741–763). New York: Springer.
Dugan, L., LaFree, G., & Piquero, A. (2005). Testing a rational choice model of airline hijiackings. Criminology, 43, 1031–1066.
Efron, B., & Tibrshirani, R. (1993). An introduction to the bootstrap. London/Boca Raton: Chapman & Hall/CRC.
Enders, W., & Sandler, T. (1991). Causality between transnational terrorism and tourism: The case of Spain. Terrorism, 14, 49–58.
Enders, W., & Sandler, T. (1993). The effectiveness of antiterrorism policies: A vector-autoregression intervention analysis. The American Political Science Review, 4(87), 829–844.
Enders, W., & Sandler, T. (2000). Is transnational terrorism becoming more threatening? Journal of Conflict Resolution, 44, 307–332.
Enders, W., & Sandler, T. (2002). Patterns of transnational terrorism, 1970-1999: Alternative time-series estimates. International Studies Quarterly, 2(46), 145–165.
Enders, W., & Sandler, T. (2006). The political economy of terrorism. New York: Cambridge University Press.
Fielding, D. (2003). Counting the cost of the Intifada: Consumption, saving and political instability in Israel. Public Choice, 116, 297–312.
Frey, B. S., & Luechinger, S. (2005). Measuring terrorism. In A. Marciano & J.-M. Josselin (Eds.), Law and the state: A political economy approach (pp. 142–181). Cheltenham, UK/Northampton, MA: Edward Elgar.
Frey, B. S., Luechinger, S., & Stutzer, A. (2007). Calculating tragedy: Assessing the costs of terrorism. Journal of Economic Surveys, 21(1), 1–24.
Kabelfleisch, J. D., & Prentice, R. L. (2002). The statistical analysis of faliure time data. New York: Wiley.
LaFree, G., & Dugan, L. (2007). Introducing the global terrorism database. Terrorism and Political Violence, 19, 181–204.
Lafree, G., Dugan, L., & Korte, R. (2009). The impact of British counter terrorist strategies on political violence in Northern Ireland: Comparing deterrence and backlash models. Criminology, 47, 17–45.
McCullagh, P., & Nelder, J. (1989). Generalized linear models. London/Boca Raton: Chapman & Hall/CRC.
McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted time series analysis (Vol. 21). Thousand Oaks: Sage.
Perl, R. (2007). Combating terrorism: The challenge of measuring effectiveness (Technical Report, Congressional Research Services Report RL33160).
Rice, J. (1977). On generalized shot noise. Advances in Applied Probability, 9(3), 553–565.
Wand, M. P., & Jones, M. C. (1995). Kernel smoothing. London/Boca Raton: Chapman & Hall/CRC.
Acknowledgements
We would like to thank CEPS/ISSR Research Assistant Jacqui Davis for her help in editing and reviewing various drafts of this chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Porter, M.D., White, G., Mazerolle, L. (2012). Innovative Methods for Terrorism and Counterterrorism Data. In: Lum, C., Kennedy, L. (eds) Evidence-Based Counterterrorism Policy. Springer Series on Evidence-Based Crime Policy, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0953-3_5
Download citation
DOI: https://doi.org/10.1007/978-1-4614-0953-3_5
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-0952-6
Online ISBN: 978-1-4614-0953-3
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)