About Article

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge. Innovations in computer science have incorporated applied mathematics to develop a library of models that reflect a variety of approaches to counterterrorism including models designed to forecast attack locations. Although these can protect individual structures or locations, they risk changing the target without reducing the threat. This research developed ageospatial information system-based complex systems model using geographically weighted regression that incorporates a variety of quantitative, qualitative and geospatial variables that differ interms of scale, weight and type. Variable selection was based on scholarly research discoveries from specializations such as political science, geography, international relations, history and statistics to determine those variables that might impact an extremist’s selection of a safe-haven. Publically available data sets provided values that populated the variables for the model. The results display as highlighted areas most attractive to extremists. This prototype is intended to serve as a foundation for future geographically weighted models that incorporate variables from different academic disciplines. It is hoped this model will improve security through prevention by providing insights into safe-haven selection and identifying questions that should be asked.