A generative model for predicting terrorist incidents

Dinesh C. Verma; Archit Verma; Diane Felmlee; Gavin Pearson; Roger Whitaker

doi:10.1117/12.2264909

4 May 2017 A generative model for predicting terrorist incidents

Dinesh C. Verma, Archit Verma, Diane Felmlee, Gavin Pearson, Roger Whitaker

Proceedings Volume 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII; 101900E (2017) https://doi.org/10.1117/12.2264909
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations

Conference Presentation

Citation Download Citation

Dinesh C. Verma, Archit Verma, Diane Felmlee, Gavin Pearson, and Roger Whitaker "A generative model for predicting terrorist incidents", Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 101900E (4 May 2017); https://doi.org/10.1117/12.2264909

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available