Explaining the Lethality of Boko Haram’s Terrorist Attacks in Nigeria, 2009–2014: A Hierarchical Bayesian Approach

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 194)


Since 2009, Nigeria has been the scene of numerous deadly terrorist attacks perpetrated by the terrorist group Boko Haram. In response to this threat, stakeholders in the fight against terrorism have deployed various counterterrorism policies, the costs of which could be reduced through efficient preventive measures. Statistical models able to integrate complex spatial dependence structures have not yet been applied, despite their potential for providing guidance to assess characteristics of terrorist attacks. In an effort to address this shortcoming, we use a flexible approach that represents a Gaussian Markov random field through stochastic partial differential equation and model the fine-scale spatial patterns of the lethality of terrorism perpetrated by Boko Haram in Nigeria from 2009 to 2014. Our results suggest that the lethality of terrorist attacks is contagious in space and attacks are more likely to be lethal at higher altitudes and far from large cities.


Bayesian hierarchical model Boko Haram GMRF Terrorism SPDE 


  1. 1.
    Abadie, A.: Poverty, political freedom and the roots of terrorism. Am. Econ. Rev. 96, 50–56 (2006)CrossRefGoogle Scholar
  2. 2.
    Amante, C., Eakins, B.: ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA (2009). Accessed 7 Dec 2014
  3. 3.
    Banerjee, S., Carlin, B.P., Gelfand, A.E.: Hierarchical Modeling and Analysis for Spatial Data. CRC Press, Boca Raton (2014)zbMATHGoogle Scholar
  4. 4.
    Behlendorf, B., LaFree, G., Legault, R.: Microcycles of violence: evidence from terrorist attacks by ETA and the FMLN. J. Quant. Criminol. 28, 49–75 (2012)CrossRefGoogle Scholar
  5. 5.
    Berrebi, C., Lakdawalla, D.: How does terrorism risk vary across space and time? An analysis based on the Isreali experience. Def. Peace Econ. 18, 113–131 (2007)CrossRefGoogle Scholar
  6. 6.
    Blangiardo, M., Cameletti, M.: Spatial and Spatio-Temporal Bayesian Models with R-INLA. Wiley, New York (2015)CrossRefzbMATHGoogle Scholar
  7. 7.
    Braithwaite, A., Li, Q.: Transnational terrorism hot spots: identification and impact evaluation. Confl. Manag. Peace Sci. 24, 281–296 (2007)CrossRefGoogle Scholar
  8. 8.
    CIESIN: Gridded Population of the World, Version 4 (GPWv4): Population Density Grid. Data from Center for International Earth Science Information Network (CIESIN). Columbia University and Centro Internacional de Agricultura Tropical (2005). Accessed 12 July 2016
  9. 9.
    Coaffee, J.: Protecting vulnerable cities: the UK’s resilience response to defending everyday urban infrastructure. Int. Aff. 86(4), 939–954 (2010)CrossRefGoogle Scholar
  10. 10.
    Crenshaw, M.: The Causes of Terrorism. St. Martin’s Press Inc., New York (1990)Google Scholar
  11. 11.
    Cummings, R.: World Deadliest? Boko Haram and the Challenge of Quantifying Violence (2015). Accessed 18 Feb 2015
  12. 12.
    Elvidge, C.D., Safran, J., Tuttle, B., Sutton, P., Cinzano, P., Pettit, D., Arvesen, J., Small, C.: Potential for global mapping of development via a nightsat mission. GeoJournal 69(1–2), 45–53 (2007)CrossRefGoogle Scholar
  13. 13.
    Elvidge, C.D., Hsu, F.C., Baugh, K.E., Ghosh, T.: National Trends in Satellite Observed Lighting: 1992–2012. CRC Press, Boca Raton (2013)Google Scholar
  14. 14.
    Fearon, J.D., Laitin, D.D.: Ethnicity, insurgency, and civil war. Am. Polit. Sci. Rev. 97(1), 75–90 (2003). CrossRefGoogle Scholar
  15. 15.
    Gao, P., Guo, D., Liao, K., Webb, J.J., Cutter, S.L.: Early detection of terrorism outbreaks using prospective space-time scan statistics. Prof. Geogr. 65, 676–691 (2013)CrossRefGoogle Scholar
  16. 16.
    Gassebner, M., Luechinger, S.: Lock, stock, and barrel: a comprehensive assessment of the determinants of terror. Public Choice 149, 235–261 (2011)CrossRefGoogle Scholar
  17. 17.
    Gelman, A., Hwang, J., Vehtari, A.: Understanding predictive information criteria for Bayesian models. Stat. Comput. 24(6), 997–1016 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    GTD: Global Terrorism Database (GTD) Codebook: Inclusion Criteria and Variables (2014). Accessed 9 Dec 2014
  19. 19.
    Henderson, J.V., Storeygard, A., Weil, D.N.: Measuring economic growth from outer space. no. w15199. In: National Bureau of Economic Research (2009)Google Scholar
  20. 20.
    Hoffman, B.: Inside Terrorism, rev. and expanded edn. Columbia University Press, New York (2006)Google Scholar
  21. 21.
    Institute for Economics and Peace: Global terrorism index 2015 (2015)Google Scholar
  22. 22.
    Kegley, C.W.: The Characteristics, Causes, and Controls of International Terrrorism: An Introduction. St. Martin’s Press Inc., New York (1990)Google Scholar
  23. 23.
    Krueger, A.B., Laitin, D.D.: Kto Kogo?: A Cross-Country Study of the Origins and Targets of Terrorism, pp. 148–173. Cambridge University Press, Cambridge (2008)Google Scholar
  24. 24.
    Kurrild-Klitgaard, P., Justesen, M., Klemmensen, R.: The political economy of freedom, democracy and transnational terrorism. Public Choice 128, 289–315 (2006)CrossRefGoogle Scholar
  25. 25.
    LaFree, G., Morris, N.A., Dugan, L.: Cross-national patterns of terrorism comparing trajectories for total, attributed and fatal attacks, 1970–2006. Br. J. Criminol. 50(4), 622–649 (2010)CrossRefGoogle Scholar
  26. 26.
    LaFree, G., Dugan, L., Xie, M., Singh, P.: Spatial and temporal patterns of terrorist attacks by ETA 1970 to 2007. J. Quant. Criminol. 28, 7–29 (2012)CrossRefGoogle Scholar
  27. 27.
    Laqueur, W.: The New Terrorism: Fanaticism and the Arms of Mass Destruction, 1st edn. Oxford University Press, New York (1999)Google Scholar
  28. 28.
    Lindgren, F., Rue, H., Lindström, J.: An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J. R. Stat. Soc. Ser. B (Statistical Methodology) 73(4), 423–498 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Nelson, A.: Estimated travel time to the nearest city of 50,000 or more people in year 2000 (2008). Accessed 10 June 2014
  30. 30.
    Nemeth, S.C., Mauslein, J.A., Stapley, C.: The primacy of the local: identifying terrorist hot spots using Geographic Information Systems. J. Polit. 76, 304–317 (2014). doi: 10.1017/S0022381613001333 CrossRefGoogle Scholar
  31. 31.
    NOAA: Version 4 DMSP-OLS Nighttime Lights Time Series. National Oceanic and Atmospheric Administration, National Geophysical Data Center (2014).
  32. 32.
    Perl, A.: Combating terrorism: The challenge of measuring effectiveness. Technical report, Technical Re-port RL33160, Congressional Research Services (2007).
  33. 33.
    Piazza, J.: Rooted in poverty? Terrorism, poor economic development, and social cleavages. Polit. Violence 18, 159–177 (2006)CrossRefGoogle Scholar
  34. 34.
    Richardson, L.: What Terrorists Want, 1st edn. John Murray, London (2006)Google Scholar
  35. 35.
    Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B (Statistical Methodology) 71(2), 319–392 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Sánchez-Cuenca, I., De la Calle, L.: Domestic terrorism: the hidden side of political violence. Ann. Rev. Polit. Sci. 12, 31–49 (2009)CrossRefGoogle Scholar
  37. 37.
    Savitch, H.V., Ardashev, G.: Does terror have an urban future? Urban Stud. 38, 2515–2533 (2001)CrossRefGoogle Scholar
  38. 38.
    Siebeneck, L.K., Medina, R.M., Yamada, I., Hepner, G.F.: Spatial and temporal analyses of terrorist incidents in Iraq, 2004–2006. Stud. Confl. Terror. 32, 591–610 (2009)CrossRefGoogle Scholar
  39. 39.
    Silberfein, M.: Insurrections. In: Cutter, Sl., Richardson, D.B., Wilbanks, T.J. (eds.) The Geographical Dimension of Terrorism, pp. 67–73. Routledge, New York (2003)Google Scholar
  40. 40.
    START: FTO Designation: Boko Haram and Ansaru (2013). Accessed 18 Feb 2015
  41. 41.
    Sutton, P.C., Elvidge, C.D., Ghosh, T.: Estimation of gross domestic product at sub-national scales using nighttime satellite imagery. Int. J. Ecol. Econ. Stat. 8(S07), 5–21 (2007)MathSciNetGoogle Scholar
  42. 42.
    Swanstrom, T.: Are fear and urbanism at war? Urban Aff. Rev. 38(1), 135–140 (2002)CrossRefGoogle Scholar
  43. 43.
    Watanabe, S.: Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010)MathSciNetzbMATHGoogle Scholar
  44. 44.
    Weidmann, N.B., Rød, J.K., Cederman, L.E.: Representing ethnic groups in space: a new dataset. J. Peace Res. 47(4), 1–9 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of St-AndrewsSt-AndrewsUK
  2. 2.Imperial College LondonLondonUK

Personalised recommendations