Editor’s Introduction: Quantitative Approaches to the Study of Terrorism
Gary LaFree & Joshua D. Freilich
Spatial and Temporal Patterns of Terrorist Attacks by ETA 1970 to 2007
Gary LaFree, Laura Dugan, Min Xie & Piyusha Singh
Rational choice perspectives maintain that seemingly irrational behavior on the part of terrorist organizations may nevertheless reflect strategic planning. In this paper we examine spatial and temporal patterns of terrorist attacks by the Spanish group ETA between 1970 and 2007. Our analysis is guided by a public announcement by ETA in 1978 that the group would shift from emphasizing attacks in the Basque territory to instead launch attacks more widely in the hopes of exhausting the Spanish government and forcing it to abandon the Basque territory. This announcement suggests that prior to the end of 1978 ETA attacks were based mostly on controlling territory in the Basque region that they hoped to rule; and after 1978 the organization decided to instead undertake a prolonged war of attrition. Accordingly, we argue that before the end of 1978 ETA was mostly perpetrating control attacks (attacking only within the Basque territories) and that the diffusion of attacks between provinces was mostly contagious (spreading contiguously). After the 1978 proclamation, we argue that the attack strategy shifted toward attrition (attacking in areas outside of the Basque territories) and that the attacks were more likely to diffuse hierarchically (spreading to more distant locations). As predicted, we find that after ETA moved toward a more attrition based attack strategy, subsequent attacks were significantly more likely to occur outside the Basque region and to target non-adjacent regions (consistent with hierarchical diffusion). We also find that hierarchical diffusion was more common when a longer time elapsed between attacks (a likely consequence of the fact that more distant attacks require more resources and planning) and that attacks against Madrid were unlikely to be followed immediately by more attacks on Madrid or surrounding provinces. After ETA announced a shift in policy, they maintained a highly dispersed attack strategy even during their period of decline. Using information about where and when prior attacks occurred could provide useful information for policy makers countering groups like ETA.
Space–Time Modeling of Insurgency and Counterinsurgency in Iraq
Alex Braithwaite & Shane D. Johnson
The US and its Coalition partners concluded combat operations in Iraq in August 2010. Rather surprisingly, little empirical evidence exists as to the factors that contributed to the ebb and flow in levels of violence and the emergence and disappearance of hot spots of hostilities during the campaign. Building upon a tradition of criminology scholarship, recent work demonstrates that Improvised Explosive Device (IED) attacks are clustered in space and time and that these trends decay in a manner similar to that observed in the spread of disease and crime. The current study extends this work by addressing a key potential correlate of these observed patterns across Iraq—namely, the timing and location of a variety of Coalition counterinsurgency (COIN) operations. This is achieved by assessing the co-evolving space–time distributions of insurgency and counterinsurgency in the first 6 months of 2005. To do so, we employ a novel analytic technique that helps us to assess the sequential relationship between these two event types. Our analyses suggest that the number of COIN operations that follow insurgent IED attacks (moderately) exceeds expectation (assuming that events are independent) for localities in the vicinity of an attack. This pattern is more consistent than is observed for the relationship in the opposite direction. The findings also suggest that less discriminatory COIN operations are associated with an elevated occurrence of subsequent insurgency in the vicinity of COIN operations in the medium to long term, whilst for more discriminatory and capacity-reducing COIN operations the reverse appears to be true.
Microcycles of Violence: Evidence from Terrorist Attacks by ETA and the FMLN
Brandon Behlendorf, Gary LaFree & Richard Legault
Recent research has demonstrated that individual crimes elevate the risk for subsequent crimes nearby, a phenomenon termed “near-repeats.” Yet these assessments only reveal global patterns of event interdependence, ignoring the possibility that individual events may be part of localized bursts of activity, or microcycles. In this study, we propose a method for identifying and analyzing criminal microcycles; groups of events that are proximate to each other in both space and time. We use the Global Terrorism Database (GTD) to analyze over 4,000 terrorist attacks attributed to the FMLN in El Salvador and the ETA in Spain; two terrorist organizations that were both extremely active and violent but differed greatly in terms of history, grievances and motives. Based on the definition developed, we find strong support for the conclusion that many of the terrorist attacks attributed to these two distinctive groups were part of violent microcycles and that the spatio-temporal attack patterns of these two groups exhibit substantial similarities. Our logistic regression analysis shows that for both ETA and the FMLN, compared to other tactics used by terrorists, bombings and non-lethal attacks are more likely to be part of microcycles and that compared to attacks which occur elsewhere, attacks aimed at national or provincial capitals or areas of specific strategic interest to the terrorist organization are more likely to be part of microcycles. Finally, for the FMLN only, compared to other attacks, those on military and government targets were more likely part of microcycles. We argue that these methods could be useful more generally for understanding the situational and temporal distribution of crime.
Patterns of Onset and Decline Among Terrorist Organizations
Erin Miller
Despite considerable speculation among terrorism researchers regarding the conditions leading to organizational desistance from terrorism, quantitative analysis of terrorism frequently focuses on terrorist attacks as the unit of analysis, resulting in a near complete absence of analyses of terrorist organizations themselves. Moreover, research on organizations that engage in terrorism has generally been limited to case studies of individual organizations. Toward a more general understanding of what conditions predict organizational desistance from terrorism, this study uses newly available data from the Global Terrorism Database to analyze the terrorist activity of 557 organizations that were active for at least 365 days between 1970 and 2008. Much like research on conventional crime, prior research on terrorism has focused almost exclusively on the onset of criminal behavior and has neglected determinants of declining activity. Here I use group-based trajectory models to investigate patterns of decline in organization-level terrorist activity. In particular I examine how patterns of onset relate to patterns of decline among these organizations. I first estimate the trajectory models for the organizations’ frequency of attacks, and then calculate the annual ratio of attacks to attacks-at-peak for each organization in order to isolate patterns of decline, independent of the magnitude of activity. I then repeat the trajectory analysis to determine if the relative shape of the organizational trajectory has significance beyond the overall frequency of attacks. I find that the speed and magnitude of an organization’s emergence are correlated with its longevity such that those organizations characterized by rapid onset are two to three times more likely than those characterized by moderate onset to reach moderate or high levels of attacks per year. Likewise, as the rate and overall volume of attacks at onset increase, so does the likelihood that the group will follow a persistent pattern of decline. I conclude with a discussion of the implications of patterns of decline among terrorist organizations for research and policy.
Estimating Country-Level Terrorism Trends Using Group-Based Trajectory Analyses: Latent Class Growth Analysis and General Mixture Modeling
Nancy A. Morris & Lee Ann Slocum
Recent criminological research has used latent class growth analysis (LCGA), a form of group-based trajectory analysis, to identify distinct terrorism trends and areas of high terrorism activity at the country-level. The current study contributes to the literature by assessing the robustness of recent findings generated by one type of group-based analysis, LCGA, to changes in measurement and statistical methodology. Using data from the Global Terrorism Database (GTD), we consider the challenges and advantages of applying group-based analysis to macro-level terrorism data. We summarize and classify country-level patterns of domestic and transnational terrorism using two types of group-based analyses, LCGA and an alternative yet similar modeling approach, general mixture modeling (GMM). We evaluate the results from each approach using both substantive and empirical criteria, highlighting the similarities and differences provided by both techniques. We conclude that both group-based models have utility for terrorism research, yet for the purposes of identifying hot spots of terrorist activity, LCGA results provide greater policy utility.
A Comparison of Ideologically-Motivated Homicides from the New Extremist Crime Database and Homicides from the Supplementary Homicide Reports Using Multiple Imputation by Chained Equations to Handle Missing Values
Jeff Gruenewald & William Alex Pridemore
This study took advantage of the new open-source Extremist Crime Database (ECDB) to overcome obstacles to studying domestic far-right terrorism from a criminological perspective. In the past, exclusive definitions and inclusion criteria have limited available data on violent crimes committed by domestic far-right terrorists, and official data on violent crimes fail to capture offenders’ links to domestic far-right terrorism and ideological motivation (e.g., anti-government, anti-abortion, anti-religion). Therefore, little is known about the nature of far-right terrorist violence and how such violence is similar to and different from routine or more common forms of violence. Focusing on homicides, this study addressed why and how open-source terrorism data and official crime data can be comparatively analyzed. In doing so, we also demonstrate the utility of synthesizing terrorism and official crime data sources. Data on 108 far-right terrorist homicides were taken from the ECDB. Data on 540 common homicides (five comparison homicides for each far-right terrorist homicide) were randomly sampled from the 2000 Supplementary Homicide Reports. Using multiple imputation by chained equations and logistic regression, we imputed missing values and estimated models to compare the two homicide types on 12 different victim, offender, and event characteristics. Relative to common homicides, we found that far-right terrorist homicides were significantly more likely to have white offenders, multiple victims, multiple offenders, and to occur between strangers, and they were significantly less likely to have white victims, to be carried out with a firearm, and to occur in cities with more than 100,000 residents.
Cross-Classified Multilevel Models: An Application to the Criminal Case Processing of Indicted Terrorists
Brian D. Johnson
This study provides an application of cross-classified multilevel models to the study of early case processing outcomes for suspected terrorists in U.S. federal district courts. Because suspected terrorists are simultaneously nested within terrorist organizations and criminal court environments, they are characterized by overlapping data hierarchies that involve cross-nested ecological contexts. Cross-classified models provide a useful but underutilized approach for analyzing such data. Using the American Terrorism Study (ATS), this research examines case dismissals, trial adjudications and criminal convictions for a sample of 574 terrorist suspects. Findings indicate that diverse factors affect case processing outcomes, including legal factors such as the number of counts, number of co-defendants, and statute of indictment, extralegal factors such as the ethnicity of the offender, and incident characteristics such as the type of terrorism target. Case processing outcomes also vary significantly across both terrorist groups and criminal courts and are partially explained by select group and court characteristics including the type of terrorist organization and the terrorism trial rate of the court. Results are discussed vis-à-vis contemporary research on terrorism punishments and future directions are suggested for additional applications of cross-classified models in criminological research.
American Terrorism and Extremist Crime Data Sources and Selectivity Bias: An Investigation Focusing on Homicide Events Committed by Far-Right Extremists
Steven M. Chermak, Joshua D. Freilich, William S. Parkin & James P. Lynch
This paper examines the reliability of the methods used to capture homicide events committed by far-right extremists in a number of open source terrorism data sources. Although the number of research studies that use open source data to examine terrorism has grown dramatically in the last 10 years, there has yet to be a study that examines issues related to selectivity bias. After reviewing limitations of existing terrorism studies and the major sources of data on terrorism and violent extremist criminal activity, we compare the estimates of these homicide events from 10 sources used to create the United States Extremist Crime Database (ECDB). We document incidents that sources either incorrectly exclude or include based upon their inclusion criteria. We use a “catchment-re-catchment” analysis and find that the inclusion of additional sources result in decreasing numbers of target events not identified in previous sources and a steadily increasing number of events that were identified in any of the previous data sources. This finding indicates that collectively the sources are approaching capturing the universe of eligible events. Next, we assess the effects of procedural differences on these estimates. We find considerable variation in the number of events captured by sources. Sources include some events that are contrary to their inclusion criteria and exclude others that meet their criteria. Importantly, though, the attributes of victim, suspect, and incident characteristics are generally similar across data source. This finding supports the notion that scholars using open-source data are using data that is representative of the larger universe they are interested in. The implications for terrorism and open source research are discussed.
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