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Modeling Conflict Dynamics with Spatio-temporal Data [electronic resource] /by Andrew Zammit-Mangion, Michael Dewar, Visakan Kadirkamanathan, Anaïd Flesken, Guido Sanguinetti.

by Zammit-Mangion, Andrew [author.]; Dewar, Michael [author.]; Kadirkamanathan, Visakan [author.]; Flesken, Anaïd [author.]; Sanguinetti, Guido [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Applied Sciences and Technology: Publisher: Cham : Springer International Publishing : 2013.Description: VIII, 74 p. 13 illus., 1 illus. in color. online resource.ISBN: 9783319010380.Subject(s): Physics | Distribution (Probability theory) | Engineering | Physics | Socio- and Econophysics, Population and Evolutionary Models | Mathematics in the Humanities and Social Sciences | Complexity | Probability Theory and Stochastic Processes | Signal, Image and Speech ProcessingDDC classification: 621 Online resources: Click here to access online
Contents:
Conflict Data Sets and Point Patterns -- Theory -- Modelling and Prediction in Conflict: Afghanistan.
In: Springer eBooksSummary: This authored monograph presents the use of dynamic spatiotemporal modeling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. The authors use ideas from statistics, signal processing, and ecology, and provide a predictive framework which is able to assimilate data and give confidence estimates on the predictions. The book also demonstrates the methods on the WikiLeaks Afghan War Diary, the results showing that this 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 preceding years. The target audience primarily comprises researchers and practitioners in the involved fields but the book may also be beneficial for graduate students.
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Conflict Data Sets and Point Patterns -- Theory -- Modelling and Prediction in Conflict: Afghanistan.

This authored monograph presents the use of dynamic spatiotemporal modeling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. The authors use ideas from statistics, signal processing, and ecology, and provide a predictive framework which is able to assimilate data and give confidence estimates on the predictions. The book also demonstrates the methods on the WikiLeaks Afghan War Diary, the results showing that this 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 preceding years. The target audience primarily comprises researchers and practitioners in the involved fields but the book may also be beneficial for graduate students.

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