Point process modelling of the Afghan War Diary

Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available....

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2012-07, Vol.109 (31), p.12414-12419
Main Authors: Zammit-Mangion, Andrew, Dewar, Michael, Kadirkamanathan, Visakan, Sanguinetti, Guido
Format: Article
Language:English
Subjects:
Afghan Campaign 2001
Covariance
Data models
Datasets
Dynamic modeling
Ecological modeling
ecology
Ecology - history
History, 21st Century
Humans
Inference
Mathematical functions
Modeling
Models, Theoretical
Physical Sciences
prediction
Social Sciences
Statistics
War
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Summary:Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. Using ideas from statistics, signal processing, and ecology, we provide a predictive framework able to assimilate data and give confidence estimates on the predictions. We demonstrate our methods on the WikiLeaks Afghan War Diary. Our results show that the approach allows deeper insights into conflict dynamics and allows a strikingly statistically accurate forward prediction of armed opposition group activity in 2010, based solely on data from previous years.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1203177109