Modeling Conflict Dynamics with Spatio-temporal Data / Andrew Zammit-Mangion (2013)  

Público ISBD Título : Modeling Conflict Dynamics with Spatio-temporal Data Tipo de documento: documento electrónico Autores: Andrew Zammit-Mangion ; SpringerLink (Online service) ; Michael Dewar ; Visakan Kadirkamanathan ; Anaïd Flesken ; Guido Sanguinetti Editorial: Cham : Springer International Publishing Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: SpringerBriefs in Applied Sciences and Technology, ISSN 2191-530X Número de páginas: VIII, 74 p. 13 illus., 1 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-319-01038-0 Idioma : Inglés (eng) Palabras clave: Physics  Probabilities  Mathematics  Social  sciences  Sociophysics  Econophysics  Complexity,  Computational  Socio-  and  Econophysics,  Population  Evolutionary  Models  in  the  Humanities  Sciences  Complexity  Probability  Theory  Stochastic  Processes  Signal,  Image  Speech  Processing Clasificación: 51 Matemáticas 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 practitioners in the involved fields but the book may also be beneficial for graduate students Nota de contenido: Conflict Data Sets and Point Patterns -- Theory -- Modelling and Prediction in Conflict: Afghanistan En línea: http://dx.doi.org/10.1007/978-3-319-01038-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32472 Modeling Conflict Dynamics with Spatio-temporal Data [documento electrónico] / Andrew Zammit-Mangion ; SpringerLink (Online service) ; Michael Dewar ; Visakan Kadirkamanathan ; Anaïd Flesken ; Guido Sanguinetti . - Cham : Springer International Publishing : Imprint: Springer, 2013 . - VIII, 74 p. 13 illus., 1 illus. in color : online resource. - (SpringerBriefs in Applied Sciences and Technology, ISSN 2191-530X) . ISBN : 978-3-319-01038-0 Idioma : Inglés (eng) Palabras clave: Physics  Probabilities  Mathematics  Social  sciences  Sociophysics  Econophysics  Complexity,  Computational  Socio-  and  Econophysics,  Population  Evolutionary  Models  in  the  Humanities  Sciences  Complexity  Probability  Theory  Stochastic  Processes  Signal,  Image  Speech  Processing Clasificación: 51 Matemáticas 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 practitioners in the involved fields but the book may also be beneficial for graduate students Nota de contenido: Conflict Data Sets and Point Patterns -- Theory -- Modelling and Prediction in Conflict: Afghanistan En línea: http://dx.doi.org/10.1007/978-3-319-01038-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32472

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