This continues our series of student reflections and analysis authored by our research team.

As mentioned in my previous blog post about analyzing group affiliation and ideologies, mapping is a great tool for visualizing patterns of attack. Terrorism research has drastically increased in the past 20 years. For more in depth analyses, it is essential to apply a variety of multidisciplinary fields to the subject of terrorism for a deeper understanding. The use of geographical theory presents an opportunity to understand the varying ways space affects decision making within the realm of political violence. It is able to provide a systematic way of interpreting data regarding the patterns of attack.

Geographic information systems (GIS) is a tool used to create maps which help display visualized geographical data for further analysis. The GIS application, fusion table, is a user friendly tool for those trying to experiment with mapping. For my second mini analysis, I chose to utilize fusion table in order to display information about group affiliation and ideology of defendants. I was able to display two types of maps, feature and heat maps, using tPP’s dataset. I utilized three variables from the dataset which were location of attack (city), group affiliation and ideological affiliation. Each of the maps I created presented a variation of findings, but not all showed clear patterns.

To generate significant findings regarding group affiliation, I isolated the 12 groups with the most cases included in the dataset. These 12 groups, including no affiliation, all had more than 10 cases recorded. It was important to choose sample sizes such as these because attempting to map a group with less than 10 cases would provide insignificant data without clear patterns. The groups I chose to include for mapping location of attack (city) are included in the chart below.

Relevant Group Affiliation Number of Attacks
No affiliation 352
al Qaeda 145
Hezbollah 78
al Shabab 40
ELF 31
Animal Liberation Front 24
Hamas 20
Taliban 20
KKK 18
Army of God 11

Each of the maps I created for the twelve group affiliations presented interesting findings. All of the maps I created depicted some type of pattern, yet a number of them showed outliers. A few of these outliers I was able to justify, but others proved to be random concentrations of attack. One of the significant outliers was the concentrations of attack in Minnesota. When I mapped ISIL and al Shabab, both heat maps presented the color red which suggested a significant amount of attacks by each group within that particular state. After further investigation, I realized that Minnesota is home to the largest group of immigrants from Somalia. These people are often targeted by terrorist groups for recruitment and therefore make up a handful of cases with charges of support/association of a terrorist organization. This provided a solid explanation as to why this outlier existed on my heat map. The map below helps visualize the patterns and outliers of defendants associated with al Shabab.

The group of defendants with no affiliation make up a majority of the attacks I focused on. This group made up 40% of the 868 cases I plotted through mapping. After creating the heat map for the group of no affiliation, it was clear that defendants with no affiliation largely attack major US cities. It is common for urban areas to be the location of attack for many terrorist groups, but those with no affiliation to a group clearly focus solely on cities and their nearby surroundings. There was no clear pattern of attack on a specific region of the US, rather, the locations of attack were spread across nearly every major city.

Using geographical tools to understand terrorism and its patterns presents an interesting way of visualizing data. Rather than configuring numbers and creating complex ways to analyze information, mapping allows a researcher to visually see patterns that may exist in their data. In the future I would like to learn how to use other types of GIS applications to expand upon my understand of mapping. More complex applications like QGIS contain tools for other types of analyzations that are able to present different types of geographical findings.

– Jessica Enhelder

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