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There’s No Place Like Home! Examining the Diffusion of Suicide Attacks Through Terrorist Group Locations

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Abstract

Suicide attacks have been widely used by many terrorist groups since the 1980s. It cannot be ignored that Al-Qaeda has played a role in diffusing this tactic, in particular among groups that have links with Al-Qaeda. However, the decision to adopt an innovative tactic is not without risk. Its implementation may inspire potential supporters, but may also cause a backlash from government and alienation from those whose support the group may be seeking. Thus, in their decision-making, it is crucial that terrorist groups learn the repercussions of their decision to adopt such tactics. In so doing, examination of the success or otherwise of other groups that adopt an innovation and its results can provide evidence for the predictability of their decision. This research argues that terrorist groups tend to learn and be influenced more by nearby groups due to the similarity of their environment of operation. The estimation is conducted through the logit model with the original terrorist group location dataset and the findings indicate that the influence of an Al-Qaeda link in adopting the tactics of suicide attack is larger when they are geographically close.

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Notes

  1. The data are excerpted from the Global Terrorism Database (GTD) and Terrorist Groups Profiles (TOPs).

  2. Finding the exact location of terrorist group locations requires expert knowledge of each region or country in which the terrorist groups operate as well as having adequate human, physical, and financial resources to complete the task; this cannot be done independently as a small, single project and is therefore beyond the scope of this paper.

  3. Regarding the application of GIS in the terrorism research, see Yildirim and Öcal (2013), Bennell and Corey (2007), Medina et al. (2011), Sui (2008), Shroder (2005), and Findley and Young (2012).

  4. The number of observations in the original data (GTD) at the time of conducting the analysis was 125,087. Of these, 74,364 observations contained latitude and longitude information; 44,748 observations contained only city name or country name information, which is used for geocoding; and 5,975 observations did not contain any information that could be used for geocoding, and were thus omitted from the research. Ultimately, we utilized 119,112 observations for this research.

  5. see https://developers.google.com/maps/documentation/geocoding/?hl=en

  6. In creating polygons as shown in Fig. 1, the “gConvexHull” function was used in the “rgeos” package in R.

  7. For the calculation, the spDistN1 function in sp package was used.

  8. To calculate the curvature, we used the curvature tool in the Spatial Analyst Toolbox in ArcGIS 10.2.

  9. The bandwidth of the kernel method was set to 30 miles.

  10. The variable of group type comes from Jones and Libicki (2008).

  11. The calculation of the predicted probability was focused on the religious groups, based on the finding that religious groups tend to adopt suicide attacks compared to other types of groups (Horowitz 2010)

  12. The mean group age was 24.58 years.

  13. The Long War Journal and the Site Intelligence group catch up on these online connections among individuals. For details, see http://www.longwarjournal.org/ and https://ent.siteintelgroup.com/.

  14. For details on JI, see Neighbour (2005), Conboy (2006), and Barton and Islamiyah (2005), and a series of publications by the International Crisis Group.

  15. We appreciate Professor Median for kindly providing the dataset for our research.

  16. In the social network analysis, the individuals are referred to as nodes and the network as a link.

  17. The location of each individual is selected from their location of capture, representative of the most recent planning and attack activities. For details, see Medina and Hepner (2011).

  18. To identify JI members, we investigated the description of each observation and 21 individuals were clearly referred to as JI members or members involved in JI attacks.

  19. As the figure shows, the distribution of geographical distance is heavily skewed. Thus, we used the median distance rather than the mean distance to represent the distribution.

  20. For the measurement of degree centrality, we used the network data provided by Professor Medina.

  21. The authors estimated other measurements such as closeness centrality and Eigenvector centrality, but the results were not significantly different from each other. Thus, we employed the simplest and most intuitive metric in this article.

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Correspondence to Yasutaka Tominaga.

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Tominaga, Y. There’s No Place Like Home! Examining the Diffusion of Suicide Attacks Through Terrorist Group Locations. Appl. Spatial Analysis 11, 355–379 (2018). https://doi.org/10.1007/s12061-016-9219-x

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