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Modeling conflict dynamics with spatiotemporal data

Autor: Andrew Zammit-Mangion
Editorial: Cham : Springer, 2013.
Serie: SpringerBriefs in applied sciences and technology., Mathematical methods.
Edición/Formato:   Libro-e : Documento : Inglés (eng)Ver todas las ediciones y todos los formatos
Base de datos:WorldCat
Resumen:
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  Leer más
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Género/Forma: Electronic books
Formato físico adicional: Print version:
Zammit-Mangion, Andrew, author.
Modeling conflict dynamics with spatiotemporal data
(OCoLC)855200592
Tipo de material: Documento, Recurso en Internet
Tipo de documento: Recurso en Internet, Archivo de computadora
Todos autores / colaboradores: Andrew Zammit-Mangion
ISBN: 9783319010380 3319010387
Número OCLC: 861183509
Descripción: 1 online resource.
Contenido: Conflict data sets and point patterns --
Theory --
Modelling and prediction in conflict: Afghanistan.
Título de la serie: SpringerBriefs in applied sciences and technology., Mathematical methods.
Responsabilidad: Andrew Zammit-Mangion, Michael Dewar, Visakan Kadirkamanathan, Anaïd Fleskin, Guido Sanguinetti.
Más información:

Resumen:

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 practitionersin the involved fields but the book may also be beneficial for graduate students.

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