The purpose of the present study is to produce foresights on terrorism. For the first time in the literature, we test the predictive validity of risk terrain modeling to forecast terrorism. Because the relevant literature suggests that target selection and the places where terrorist attacks occur are related to a group’s strategies, we also investigate whether and how violent terrorist acts vary with respect to their surroundings in the same jurisdiction when the ideology is the point of comparison. Separatist and leftist terrorist groups committed 1152 violent terrorist acts between 2008 and 2012 in Istanbul, Turkey. Our analysis begins with a comparison of targets and risk factors by the ideology of the perpetrators for 857 separatist and 295 leftist terrorist incidents. After identifying high-risk locations, we test the predictive validity of risk terrain modeling. The study results showed that context and spatial influence—the risky areas of terrorism—vary by the nature of the ideology in the jurisdiction. Practical implications also are discussed.
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Also, the symbolic value of the targets has been important for terrorists, and one of the well-known rating methods was proposed by Clarke and Newman (2006) to predict what terrorists can target. Several studies benefited from Clarke and Newman’s method to rate a number of physical features for the likelihood of being targeted in the city. The RTM analysis, on the other hand, is applicable to all places in a study area, though the symbolic nature of a particular feature is not weighted.
Police are responsible for the safety of urban and suburban cities of Turkey while the gendarmerie serves in the rural areas. The current study examined the terrorist incidents within the boundaries of the IPD area of responsibility.
According to the Article 1 of Law No: 3713 (Anti-Terror Law in Turkey 1991), the definition of terror is described as follows: “The terror is an act, by using force and violence, perpetrated by any of the methods of extortion, intimidation, discouragement, menace and threat by a person or by persons belonging to an organization with a view of changing the nature of the Republic as defined in its Constitution and its political, legal, social, secular and economic order, impairing the indispensable integrity of the State with its country and nation, endangering the existence of the Turkish State and Republic, weakening or annihilating or overtaking the state authority, eliminating the basic rights and freedoms and damaging the internal and external safety, public order or general health of the country.” For an act to be considered as a terrorist incident, the crimes stated in Article 4 of the Turkish Anti-Terror Act have to be committed with the intentions cited in Article 1. Accordingly, Article 4 of the Turkish Anti-Terror Act articulates that the offenses described in the different articles of Turkish Penal Code “shall be considered as terrorist offenses if they have been committed for the purposes described in Article 1 under the scope of the activities of a terrorist organization established with the purpose of committing crimes.” For example, a defendant would be accused of violating the Anti-Terror Act if the person committed a burglary to provide monetary support to the operations of a terrorist group.
Most recent empirical research on the spatial aspect of terrorism have used different specific acts together to construct a dependent variable of terrorism (Baudains et al. 2012; Cothren et al. 2008; LaFree et al. 2012; LaFree and Bersani, 2014; Nemeth et al. 2014). For example, Nemeth et al. (2014) based their construct on five criteria: “the attacks are intentional; involve the use, or threatened use, of violence; and the perpetrating actors are subnational; the act must be directed towards a political, economic, religious, or social goal; there must be evidence of intent to coerce; and the action must be outside the realm of legitimate military activities.” (p.308). To explore spatial and temporal patterns of terrorism, Lafree et al. 2012 used 1993 terrorist attacks attributed to the ETA from 1970 to 2007, and the data from GTD also include unarmed assaults and attacks to facilities or infrastructure. The spatio-temporal patterns were also examined with terrorist incidents that were designated by a law enforcement agency (see Cothren et al. 2008).
A sample incident summary is as follows: “On January 1st, 2008 at around 18.00, a bomb detonated in a waste container that was in front of the building at Inonu Street 122 in Cumhuriyet Neighborhood of the district of Kucukcekmece. Two persons were wounded, and some buildings around the address were damaged. An investigation was initiated to identify suspect(s).” In practice, counterterrorism units intervene in any investigation with the suspicion of terrorism. If the incident is not a terrorist act, then it is cleared from the database of the Counter Terrorism Department.
The addresses of the incidents were entered into a Microsoft Excel file and then geocoded using the ESRI’s World Geocoding Service that requires an authorization by the producer. The matching scores in geocoding were quite high. Ninety-eight percent, or 1272 incident addresses, were matched successfully. Two percent, or 22 addresses, had more than one candidate with the same best match in different locations, and they were rematched to the correct addresses. The raw data included 1294 incidents over the study period. Some instances were nonviolent acts (e.g., unlawful demonstrations to propagandize a terrorist organization). The addresses of violent terrorist incidents provided by TNP were used to probe the spatial aspects of the activity.
Of all 1152 terrorist incidents, only one was invasion, seven were robberies, 28 armed assaults, 214 violent demonstrations, 280 explosives, and 622 arsons. The study area was very wide, and the number of incidents in the other types was not large enough to conduct statistical tests. Using the same variables and parameters (i.e., 110-m by 110-m cells within a two-block area), a separate model could only be tested for all terrorist arson incidents without an ideological dichotomy.
Religious facilities included mosques, synagogues, and churches. The bakery variable was constructed from the point locations of bakeries and patisseries where bread and cakes are made and sold. Franchises were convenience stores selling basic food items and consumer goods and included the locations of all branches of five franchise convenience stores (BIM, Kiler, HakMar, A101, and Carrefour) whose branches were targeted multiple times. Educational facilities were elementary schools, high schools, and universities. Eateries included cafés, coffeehouses, fast food establishments, and restaurants, where people can spend time on the premises. The government variable included police, military, and correctional facilities; courthouses; city hall; other municipal buildings; consulates; and post offices.
The models generated 44 variables from 17 risk factors to test for statistical significance. The testing process involved first using an elastic net penalized regression model assuming a Poisson distribution of events. Next, 21 variables were selected in the overall and separatist models and 23 variables in the leftist model through cross validation. The variables were then used in a bidirectional stepwise regression process starting with a null model to build an optimal model by optimizing the Bayesian information criteria (BIC). A BIC score balances how well the model fits the data against the complexity of the model. The stepwise regression process was conducted for both Poisson and negative binomial distributions with the best BIC score used to select between the distributions. The best risk terrain model was a negative binomial with eight risk factors for all incidents (BIC score = 10,265.0), seven risk factors for separatist incidents (BIC score = 8265.4), and six risk factors for leftist incidents (BIC = 3293.8). The models also included an intercept term that represents the background rate of events and an intercept term that represented overdispersion of the event counts.
The underlying correlation among the 44 variables generated from 17 risk factors may be a limitation. A potential intercorrelation of independent variables may make parameter estimates unreliable since it may produce large standard errors in the denominator, which could make a predictor insignificant in a model. On the other hand, our results could be only minimally-biased since the models include independent variables that had the strongest correlation with dependent variables.
The predictive validity could not be tested by the current ideological dichotomy since the separatist groups committed around 480 incidents in the first period, and this number was around 190 for the leftist group. The RTMDx could not find any significant risk factor across 108,471 units with the two samples of incidents.
Initially, 10.8% percent of all 110-m by 110-m cells had highest values of risk in the first period, ranging from 62 to 184. The number of cells (11,618) with a risk value of 62 exceeded the number of cells (10,847) needed for the validity test. In total, 6529 cells had a risk value 62; however, 5758 cells needed to be included in the first-tier places. Therefore, 5758 cells with a risk value of 62 were randomly selected to ensure that every cell had an equal chance of being sorted into the pool of the top 10% of cells with the highest values of risk.
The total number of incidents in the second period was 94 in all cells with a risk value of 62 in the first period. The random sampling excluded three incidents in the process of designating high-risk places.
At the outset, the IPD jurisdiction contained 149,137 places; however, 108,471 were examined after excluding the places beyond two blocks from the street. Those 108,471 places under examination covered 72.7% of the entire jurisdiction.
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Onat, I., Gul, Z. Terrorism Risk Forecasting by Ideology. Eur J Crim Policy Res 24, 433–449 (2018). https://doi.org/10.1007/s10610-017-9368-8